[ { "productId": "codegen", "storyId": "agentic-agent-docs", "verdict": "full", "quality": 9, "confidence": "high", "rationale": "A probe confirms Codegen's docs site serves a working llms.txt at https://docs.codegen.com/llms.txt (HTTP 200) listing structured doc links, directly enabling an agent to be pointed at agent-oriented docs; this is corroborated by extensive first-party documentation of agent-friendly interfaces (SDK, CLI, API). Missing for 10: independent third-party confirmation of agents actually consuming the llms.txt in practice.", "evidenceIds": [ "codegen-probe-1", "codegen-docs-1", "codegen-docs-11" ] }, { "productId": "codegen", "storyId": "agentic-ai-insights", "verdict": "partial", "quality": 5, "confidence": "medium", "rationale": "Codegen generates AI-driven insights specifically around code artifacts—summarizing PR diffs, running security/secrets scans, checking test coverage, and suggesting changes/comments on PRs and issues—which counts as data-driven insight generation within its coding-agent context. However, these insights are narrowly scoped to code/repo/PR data rather than broader 'your data' (e.g., databases, business metrics) despite a mention of database connectivity via MCP with no insight-generation detail there. Missing for 10: evidence of insights/suggestions generated from non-code data sources (databases, analytics), and no independent/hands-on corroboration of insight quality.", "evidenceIds": [ "codegen-docs-20", "codegen-docs-4", "codegen-docs-6" ] }, { "productId": "codegen", "storyId": "agentic-autonomous-automation", "verdict": "full", "quality": 7, "confidence": "medium", "rationale": "Codegen documents agents that trigger autonomously from CI/CD failures, Slack/Linear/Jira events, and PR activity (auto-review, auto-comment), plus SDK/CLI for programmatic background automation — this matches the story of autonomous background automations. Missing for 10: independent/hands-on verification of reliability, and no explicit scheduled/cron-trigger mechanism documented.", "evidenceIds": [ "codegen-docs-18", "codegen-docs-20", "codegen-docs-2", "codegen-docs-3", "codegen-docs-11", "codegen-docs-15", "codegen-docs-16" ] }, { "productId": "codegen", "storyId": "agentic-builtin-assistant", "verdict": "full", "quality": 8, "confidence": "medium", "rationale": "Codegen's documentation clearly describes delegating coding tasks to a built-in AI agent that analyzes requirements, implements features, fixes bugs, writes tests, and reports progress via prompts, accessible through chat, Slack, CLI, and SDK. Missing for 10: independent/hands-on verification of the assistant's actual task-completion quality beyond vendor docs.", "evidenceIds": [ "codegen-docs-1", "codegen-docs-2", "codegen-docs-4", "codegen-docs-15", "codegen-docs-16" ] }, { "productId": "codegen", "storyId": "agentic-headless", "verdict": "full", "quality": 8, "confidence": "medium", "rationale": "Codegen explicitly supports headless/automated usage via a Python SDK for programmatic agent invocation, a REST API with explicit CI/CD trigger support ('Trigger agents from CI/CD pipelines when builds fail'), and a CLI for scripted interactions and telemetry — all first-party documented. Missing for 10: independent/hands-on confirmation of CI usage in the wild and a working OpenAPI spec (probe found 404s), so it's vendor-doc-only evidence.", "evidenceIds": [ "codegen-docs-11", "codegen-docs-15", "codegen-docs-16", "codegen-docs-17", "codegen-docs-18", "codegen-docs-12", "codegen-probe-2" ] }, { "productId": "codegen", "storyId": "agentic-mcp-client", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "Codegen's docs explicitly mention connecting to Slack, Linear, Figma, databases and extending capabilities 'with custom MCP tools', and separately reference provisioning MCP servers across an org — direct evidence of MCP client integration. However, the evidence is a single brief doc mention with no detailed setup guide, supported server list, or independent/hands-on confirmation of the feature working. Missing for 10: detailed MCP integration docs/tutorial, list of supported MCP servers, independent verification of the feature in practice.", "evidenceIds": [ "codegen-docs-6", "codegen-docs-7" ] }, { "productId": "codegen", "storyId": "agentic-mcp-server", "verdict": "na", "quality": 0, "confidence": "medium", "rationale": "Codegen is itself an AI coding agent, so serving as an MCP server for other agents to connect to is a different product role (client vs server) per the category rule. Evidence only shows Codegen acting as an MCP client (connecting to Slack/Linear/Figma/custom MCP tools) or provisioning MCP servers for its own org's Claude Code instances, not exposing itself as an official MCP server endpoint for external agents to connect to.", "evidenceIds": [ "codegen-docs-6", "codegen-docs-7" ] }, { "productId": "codegen", "storyId": "agentic-nl-commands", "verdict": "full", "quality": 8, "confidence": "medium", "rationale": "Codegen is fundamentally natural-language driven: docs show prompt-based task execution ('Leave a review on PR #123'), Slack/Jira/Linear interactions, and SDK agent.run(prompt=...) all operate via NL commands across code, PRs, and issue trackers. This spans chat, CLI, and SDK surfaces, consistently prompt/NL-driven. Missing for 10: independent/hands-on corroboration of NL command interpretation quality and edge cases beyond first-party docs.", "evidenceIds": [ "codegen-docs-1", "codegen-docs-2", "codegen-docs-3", "codegen-docs-4", "codegen-docs-15", "codegen-docs-16" ] }, { "productId": "codegen", "storyId": "agentic-official-cli", "verdict": "full", "quality": 8, "confidence": "medium", "rationale": "Codegen documents an official CLI (docs.codegen.com/introduction/cli) with concrete capabilities: viewing/pulling agent work, creating new agents, running Claude Code with telemetry, and managing organizations/repositories — matching an AI-native agentic workflow tool. missing for 10: independent/hands-on corroboration beyond first-party docs, and no details on installation/versioning maturity.", "evidenceIds": [ "codegen-docs-12", "codegen-docs-13", "codegen-docs-14", "codegen-probe-3" ] }, { "productId": "codegen", "storyId": "agentic-public-api", "verdict": "full", "quality": 8, "confidence": "high", "rationale": "Codegen documents a full API reference (agent creation, traces, CI/CD triggers), a Python SDK for programmatic agent control, and a CLI for managing agents/orgs, giving AI-native users multiple documented ways to drive the product programmatically. Missing for 10: a discoverable machine-readable OpenAPI/swagger spec (probe returned 404s) and independent third-party corroboration of the API's reliability.", "evidenceIds": [ "codegen-docs-11", "codegen-docs-12", "codegen-docs-15", "codegen-docs-16", "codegen-docs-17", "codegen-docs-18", "codegen-probe-2", "codegen-probe-3" ] }, { "productId": "codegen", "storyId": "agentic-scoped-keys", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "Codegen's SDK/API uses a simple org_id + token authentication (codegen-docs-15) with no documentation of scoped, role-based, or least-privilege credential issuance, token permissions, or granular access controls. Missing for 10: any mention of scoped API keys, permission levels, or restricted-access tokens for agents.", "evidenceIds": [ "codegen-docs-15", "codegen-docs-16" ] }, { "productId": "codegen", "storyId": "agentic-sdks", "verdict": "full", "quality": 8, "confidence": "medium", "rationale": "Codegen documents an official Python SDK for programmatic agent control (agent.run, org_id/token auth), a full API reference with traces and CI/CD triggers, and a companion CLI — all first-party docs. missing for 10: independent/hands-on corroboration beyond docs, and no public OpenAPI spec was found (probe returned 404s).", "evidenceIds": [ "codegen-docs-11", "codegen-docs-15", "codegen-docs-16", "codegen-docs-17", "codegen-docs-18", "codegen-probe-2", "codegen-probe-3" ] }, { "productId": "codegen", "storyId": "agentic-webhooks", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "No evidence in the pack mentions webhooks or event-subscription mechanisms; only SDK/API polling, CI/CD triggers, and integrations (Slack, Linear, Jira) are documented, none of which describe webhook subscriptions.", "evidenceIds": [] }, { "productId": "codegen", "storyId": "ai-automated-test-generation-on-change", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "Docs state the agent writes tests as part of implementing features and can run code/install dependencies/test changes in isolated environments, and a PR-review flow checks test coverage before flagging issues — together implying self-validation via generated tests. However, there is no explicit end-to-end walkthrough showing the agent generating tests, running them, and iterating/fixing before proposing a PR, nor independent/hands-on confirmation. missing for 10: explicit documented workflow tying test generation+execution to pre-PR validation, independent corroboration of this loop working in practice.", "evidenceIds": [ "codegen-docs-1", "codegen-docs-5", "codegen-docs-20" ] }, { "productId": "codegen", "storyId": "ai-automatic-model-routing", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "No evidence anywhere in the pack of automatic model routing per task/prompt; Codegen's docs describe agent capabilities, integrations, CLI/SDK, but nothing about selecting or routing between underlying LLMs based on task type.", "evidenceIds": [] }, { "productId": "codegen", "storyId": "ai-issue-triage", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "Evidence shows Codegen integrates with issue trackers (Jira, Linear, etc.) to update statuses, add comments, and create tasks, but there is no mention of automatic severity assessment or routing incoming issues to the correct owner. Missing for 10: automatic severity classification, owner/routing logic, any triage workflow evidence.", "evidenceIds": [ "codegen-docs-3", "codegen-docs-10" ] }, { "productId": "codegen", "storyId": "api-interactive-docs", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "Codegen has an API reference page (overview, SDK docs) but no evidence of an interactive reference with runnable/executable examples; the OpenAPI/spec probe returned 404 for all candidate paths, suggesting no interactive API explorer exists.", "evidenceIds": [ "codegen-docs-16", "codegen-docs-17", "codegen-docs-18", "codegen-probe-2" ] }, { "productId": "codegen", "storyId": "api-machine-spec", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "Codegen has an API reference (docs-16/17/18) but explicit probes for machine-readable spec files (openapi.json, swagger.json, etc.) all returned 404, indicating no downloadable OpenAPI/machine-readable spec is available.", "evidenceIds": [ "codegen-probe-2", "codegen-docs-16" ] }, { "productId": "codegen", "storyId": "api-sandbox", "verdict": "partial", "quality": 5, "confidence": "medium", "rationale": "Codegen docs state that code changes run in 'robust isolated environments' to safely execute code and test changes, which implies sandboxed testing separate from production. However, there's no explicit description of how production data is excluded or protected, no detail on the sandbox's scope/persistence, and no independent verification of this isolation claim. missing for 10: explicit production-data isolation guarantees, technical detail on sandbox architecture, independent/hands-on confirmation.", "evidenceIds": [ "codegen-docs-5" ] }, { "productId": "codegen", "storyId": "api-versioning-policy", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "There's an API reference and SDK docs, but no evidence of API versioning scheme or a documented deprecation policy; the openapi probe even returned 404s, suggesting no formal spec surfacing version info.", "evidenceIds": [ "codegen-docs-16", "codegen-probe-2" ] }, { "productId": "codegen", "storyId": "approve-plan-before-execution", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "No evidence in the pack of a plan-preview or approval step prior to code changes; agents appear to run prompts directly to implementation (create branches, commit code, open PRs) without a documented plan-review gate. missing for 10: any mention of a plan/preview mode, approval gate, or dry-run step before code execution.", "evidenceIds": [ "codegen-docs-1", "codegen-docs-4", "codegen-docs-15" ] }, { "productId": "codegen", "storyId": "approve-task-contract", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "Evidence shows Codegen agents can be given prompts and run autonomously with PR review/creation, but there is no mention of any scope/contract approval gate or engineering-lead sign-off step required before an agent is allowed to modify a repository.", "evidenceIds": [] }, { "productId": "codegen", "storyId": "assign-task-from-ticket", "verdict": "full", "quality": 7, "confidence": "medium", "rationale": "Docs show native integrations with Jira, Linear, ClickUp and Monday.com allowing the agent to update issue statuses, add comments, link PRs and create tasks, and specifically state connecting a Linear workspace 'enables agent interactions with your issues,' which directly supports assigning work from a ticket. Missing for 10: a concrete end-to-end example of assigning a task from an issue/ticket, and independent/hands-on corroboration beyond vendor docs.", "evidenceIds": [ "codegen-docs-3", "codegen-docs-10", "codegen-docs-8" ] }, { "productId": "codegen", "storyId": "auto-approve-all-actions", "verdict": "none", "quality": 0, "confidence": "low", "rationale": "The evidence describes Codegen's autonomous capabilities (running code, committing, creating PRs, Slack/Jira integration) but never mentions any per-action confirmation mode or an auto-approve toggle/setting for oversight control. Missing for 10: any documentation of an approval/confirmation workflow, a setting to enable/disable auto-approval, or human-in-the-loop gating that this story presupposes.", "evidenceIds": [] }, { "productId": "codegen", "storyId": "auto-fix-readiness-gaps", "verdict": "partial", "quality": 5, "confidence": "low", "rationale": "Codegen agents can fix bugs, write tests, and run CI/CD-triggered fixes (codegen-docs-1, codegen-docs-18, codegen-docs-20), which could be applied to remediate failing agent-readiness checks, but there is no explicit documentation of an 'agent-readiness criteria' concept, scoring rubric, or automated fix workflow tied specifically to such criteria. missing for 10: explicit agent-readiness criteria/checklist, automated detection-to-fix pipeline for those criteria, evidence of hands-on success fixing readiness gates.", "evidenceIds": [ "codegen-docs-1", "codegen-docs-18", "codegen-docs-20" ] }, { "productId": "codegen", "storyId": "auto-open-pr-on-completion", "verdict": "full", "quality": 7, "confidence": "medium", "rationale": "Codegen's agent creates branches, commits code, and manages repositories via GitHub App integration, and can be run programmatically via SDK/CLI/API to complete tasks; PR creation is a core documented capability of the agent workflow. Missing for 10: explicit first-party documentation/example showing a config flag or setting to 'auto-open PR on task completion' and independent/hands-on confirmation of this exact behavior.", "evidenceIds": [ "codegen-docs-4", "codegen-docs-8", "codegen-docs-16", "codegen-docs-18" ] }, { "productId": "codegen", "storyId": "automated-pr-review", "verdict": "full", "quality": 7, "confidence": "medium", "rationale": "Docs explicitly describe automated PR review functionality: reading the diff, summarizing changes, running security/secrets scans, checking test coverage, and posting a review comment, plus general 'Review PRs, suggest changes, comment on issues' capability tied to GitHub App integration for repo access. This matches the engineering-lead story of automatic AI-reviewed PRs with comments. Missing for 10: explicit confirmation that comments are inline (per-line) rather than a single summary comment, and independent/hands-on evidence corroborating this in production use.", "evidenceIds": [ "codegen-docs-4", "codegen-docs-20", "codegen-docs-8", "codegen-docs-18" ] }, { "productId": "codegen", "storyId": "automatic-environment-setup", "verdict": "full", "quality": 8, "confidence": "medium", "rationale": "Docs describe agents that access repos via GitHub App integration, run in isolated environments, and 'safely run code, install dependencies, and test changes' automatically without complex setup, matching the story of autonomous environment setup. Missing for 10: independent/hands-on verification of environment provisioning behavior and more detail on how dependency installation is auto-configured beyond the brief doc mention.", "evidenceIds": [ "codegen-docs-5", "codegen-docs-8", "codegen-docs-4" ] }, { "productId": "codegen", "storyId": "automation-bulk-operations", "verdict": "partial", "quality": 5, "confidence": "medium", "rationale": "The Python SDK and API allow programmatic creation/monitoring of agents and triggering from CI/CD, which could be scripted in a loop to perform bulk operations, but there is no documented batch/bulk endpoint or explicit guidance for operating across many items at once. missing for 10: dedicated bulk/batch API or CLI command, documented examples of multi-item automation, and evidence of scale/performance handling for bulk workloads.", "evidenceIds": [ "codegen-docs-11", "codegen-docs-15", "codegen-docs-16", "codegen-docs-18" ] }, { "productId": "codegen", "storyId": "automation-rules-engine", "verdict": "partial", "quality": 5, "confidence": "medium", "rationale": "Codegen supports event-driven automation via CI/CD triggers (docs-18) and PR review triggers on failure (docs-20), plus SDK/API for programmatic agent invocation, but there's no evidence of a general user-facing rules engine for defining arbitrary trigger-condition-action automations across events. missing for 10: a documented rules/automation builder UI, broader event types beyond CI/CD failures and PR diffs, and independent confirmation of custom trigger logic.", "evidenceIds": [ "codegen-docs-18", "codegen-docs-20", "codegen-docs-11", "codegen-docs-16" ] }, { "productId": "codegen", "storyId": "automation-scheduled-jobs", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "No evidence of any scheduling, cron-like, or recurring job/workflow capability; the docs cover SDK/API triggers, CI/CD triggers, and manual agent runs, but nothing about time-based or recurring automation.", "evidenceIds": [] }, { "productId": "codegen", "storyId": "automation-versioned-workflows", "verdict": "none", "quality": 0, "confidence": "low", "rationale": "Evidence covers PR/code review, commits, branches, and traces/telemetry, but there is no mention of versioning automations themselves, reviewing automation history, or rolling back agent configurations/workflows. missing for 10: automation versioning system, rollback mechanism for agent workflows, audit/history UI for automations.", "evidenceIds": [] }, { "productId": "codegen", "storyId": "autonomous-bug-fixing", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "Docs explicitly claim the agent can 'analyze requirements, implement features, fix bugs, write tests' and can be triggered from issue trackers/CI to autonomously act, plus run in isolated environments to test changes — directly supporting autonomous bug diagnosis and fix. However, all evidence is first-party marketing/docs with no independent or hands-on validation of end-to-end autonomous diagnosis-to-fix workflows, and no detailed walkthrough of the diagnosis step itself (e.g., root-cause analysis) beyond generic 'fix bugs' language. Missing for 10: independent/hands-on verification of successful autonomous bug fixes, detailed diagnostic reasoning process, and success-rate or case-study evidence.", "evidenceIds": [ "codegen-docs-1", "codegen-docs-3", "codegen-docs-5", "codegen-docs-16", "codegen-docs-18" ] }, { "productId": "codegen", "storyId": "bring-your-own-model", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "No evidence pack item mentions BYO-LLM, custom API key configuration, or model selection; Codegen's docs focus on its own agents, integrations, and SDK/CLI without any option to swap the underlying model provider.", "evidenceIds": [] }, { "productId": "codegen", "storyId": "chat-with-agent-in-slack", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "Codegen supports Slack-based chat interactions where users can message/tag the agent to report progress, ask for clarification, and delegate tasks (codegen-docs-2, codegen-docs-9), and Linear/Jira integration allows commenting and task creation to delegate bugs (codegen-docs-3, codegen-docs-10). However, evidence doesn't explicitly confirm an '@mention' tagging mechanism within an existing chat thread to delegate a specific bug — it's inferred from general Slack/Linear interaction docs rather than a documented tagging workflow. Missing for 10: explicit documentation of @-mention/tagging syntax in threads, confirmation of thread-level context retention when delegating, and independent/hands-on verification of this specific workflow.", "evidenceIds": [ "codegen-docs-2", "codegen-docs-9", "codegen-docs-3", "codegen-docs-10" ] }, { "productId": "codegen", "storyId": "ci-failure-auto-fix", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "Docs explicitly mention triggering agents from CI/CD pipelines when builds fail and agents that fix bugs/write code and open PRs, directly supporting automated diagnosis-and-fix-PR workflows for failed CI. However, there is no dedicated end-to-end CI-failure-diagnosis feature description, no example of the proposed PR format for a failing build, and no independent/hands-on confirmation of this specific workflow. missing for 10: dedicated CI-failure-diagnosis workflow documentation, concrete example of a CI-triggered fix PR, independent verification of this specific capability.", "evidenceIds": [ "codegen-docs-18", "codegen-docs-1", "codegen-docs-16" ] }, { "productId": "codegen", "storyId": "codebase-context-file", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "The evidence pack covers Codegen's integrations, CLI, SDK, and PR/issue automation, but nowhere mentions a repo-level context or rules file (e.g., conventions file) that developers can add to steer agent plans/code generation. missing for 10: any mention of a context/config file for codebase conventions, documentation on custom instructions, or repo-level rules ingestion.", "evidenceIds": [] }, { "productId": "codegen", "storyId": "concurrent-task-execution", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "Codegen's SDK/API let you programmatically create and run multiple agents (agent.run, API 'full programmatic control over execution and monitoring') and CLI can view multiple running agents, implying support for parallel task execution, and CI/CD triggers suggest scaling automation into pipelines. However, there is no explicit documentation of concurrency limits, throughput guarantees, or dashboards for managing many simultaneous tasks at scale. Missing for 10: explicit concurrency/parallelism claims, scaling limits or throughput benchmarks, and independent evidence of running many agents simultaneously in practice.", "evidenceIds": [ "codegen-docs-15", "codegen-docs-16", "codegen-docs-12", "codegen-docs-18" ] }, { "productId": "codegen", "storyId": "connect-github-repo", "verdict": "full", "quality": 8, "confidence": "medium", "rationale": "Codegen provides a GitHub App install for repo access with no complex setup, and docs describe the agent creating branches, committing code, reviewing/opening PRs, and managing repositories. missing for 10: independent/hands-on confirmation beyond vendor docs, and explicit detail on multi-repo/org-level permission scoping.", "evidenceIds": [ "codegen-docs-8", "codegen-docs-4", "codegen-docs-16", "codegen-docs-18" ] }, { "productId": "codegen", "storyId": "daily-task-quota-limits", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "No evidence pack item mentions plan-based daily task limits, concurrency limits, or any admin dashboard/controls for engineering leads to monitor or manage usage quotas; documentation covers agent capabilities, integrations, CLI, and SDK but nothing on pricing tiers or limit management.", "evidenceIds": [] }, { "productId": "codegen", "storyId": "delegate-sessions-for-teammates", "verdict": "partial", "quality": 4, "confidence": "low", "rationale": "Codegen's org-level SDK/CLI/API (org_id+token, 'manage organizations', programmatic agent creation) implies an engineering lead with org credentials could spin up agents at scale, but no docs explicitly describe creating or assigning a session to another named user in the org. Missing for 10: explicit user-impersonation or 'create on behalf of' API/UI feature, role-based permissions for delegating agent sessions, and any admin console for assigning sessions to teammates.", "evidenceIds": [ "codegen-docs-14", "codegen-docs-15", "codegen-docs-12", "codegen-docs-16" ] }, { "productId": "codegen", "storyId": "describe-feature-in-plain-language", "verdict": "full", "quality": 7, "confidence": "medium", "rationale": "Codegen's docs describe taking natural-language prompts and turning them into scoped agent tasks: analyzing requirements, implementing features, fixing bugs, writing tests, and running agents from prompts via SDK/CLI/Slack/issue trackers. This directly matches the intent-to-spec story of describing a feature/bug and having it become an actionable implementation task. Missing for 10: no independent/hands-on evidence showing the quality or accuracy of the generated scoped task (e.g. how well it decomposes ambiguous requests), and no detail on how it handles clarification/scoping edge cases beyond a general 'ask for clarification' mention.", "evidenceIds": [ "codegen-docs-1", "codegen-docs-15", "codegen-docs-3", "codegen-docs-2" ] }, { "productId": "codegen", "storyId": "design-to-code-handoff", "verdict": "partial", "quality": 4, "confidence": "low", "rationale": "Codegen's docs show relevant building blocks — a Figma connector, Slack/Linear interfaces that let non-engineers prompt the agent, and full PR/branch creation — implying a PM could describe a design and get code without direct engineering handoff. However, there is no concrete workflow, case study, or example showing a mockup/design being turned into a working implementation end-to-end. Missing for 10: an explicit design-to-code workflow example, evidence of Figma-to-PR pipeline, and PM-specific case studies or testimonials showing the handoff-free process actually works.", "evidenceIds": [ "codegen-docs-6", "codegen-docs-2", "codegen-docs-4", "codegen-docs-9", "codegen-docs-10" ] }, { "productId": "codegen", "storyId": "diff-review-before-pr", "verdict": "partial", "quality": 5, "confidence": "low", "rationale": "Codegen agents create branches, commit code, and open PRs, and the agent itself can review PRs and post comments, but the evidence pack does not show a dedicated human-in-the-loop diff review/approval gate before a PR is created — the CLI lets you 'pull agent work' locally which could support manual review, but no explicit approval workflow is documented. missing for 10: explicit pre-PR diff approval UI/workflow, evidence of a human approve/reject gate, independent confirmation that PR creation is blocked pending review.", "evidenceIds": [ "codegen-docs-4", "codegen-docs-12", "codegen-docs-13", "codegen-docs-20" ] }, { "productId": "codegen", "storyId": "end-to-end-feature-implementation", "verdict": "full", "quality": 8, "confidence": "medium", "rationale": "Docs explicitly describe agents analyzing requirements, implementing features, writing tests, and running/testing changes in isolated environments, plus PR/branch management for end-to-end delivery. Missing for 10: independent/hands-on validation of end-to-end feature delivery and no concrete example transcript showing a full feature+test cycle.", "evidenceIds": [ "codegen-docs-1", "codegen-docs-5", "codegen-docs-4", "codegen-docs-16" ] }, { "productId": "codegen", "storyId": "enterprise-sso-licensing", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "No evidence in the pack mentions SSO, enterprise licensing, commercial support tiers, or org-wide rollout plans; only feature-level docs and API/CLI references are provided. Missing for 10: SSO/SAML support, enterprise plan/pricing tier documentation, commercial support SLA, org-wide admin/rollout tooling.", "evidenceIds": [] }, { "productId": "codegen", "storyId": "feedback-to-task-conversion", "verdict": "partial", "quality": 3, "confidence": "low", "rationale": "Codegen can analyze requirements and create/link tasks in Jira, Linear, Clickup, Monday.com, which could theoretically be used to turn feedback into tasks, but there is no evidence of a feedback-intake feature or automatic scope proposal tailored for PM workflows. Missing for 10: dedicated feedback ingestion mechanism, structured scope/estimate output, PM-specific workflow examples or case studies.", "evidenceIds": [ "codegen-docs-1", "codegen-docs-3" ] }, { "productId": "codegen", "storyId": "headless-ci-execution", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "Codegen explicitly documents triggering agents from CI/CD pipelines (e.g., on build failures) and offers a Python SDK plus CLI for programmatic/scripted invocation, which supports headless usage in pipelines and scripts. However, there's no dedicated CI/CD integration guide, no example pipeline configs (e.g., GitHub Actions YAML), and no independent/hands-on evidence confirming real-world headless CI usage. missing for 10: concrete CI/CD pipeline examples or config snippets, exit-code/status handling for scripts, independent verification of headless operation in automated pipelines.", "evidenceIds": [ "codegen-docs-18", "codegen-docs-11", "codegen-docs-15", "codegen-docs-12", "codegen-probe-3" ] }, { "productId": "codegen", "storyId": "isolated-sandbox-execution", "verdict": "full", "quality": 7, "confidence": "medium", "rationale": "First-party docs explicitly state the agent can 'Safely run code, install dependencies, and test changes in robust isolated environments,' directly matching the story. missing for 10: independent/hands-on corroboration of sandbox isolation, technical details on sandbox implementation (e.g., container tech, resource limits) beyond a single doc line.", "evidenceIds": [ "codegen-docs-5" ] }, { "productId": "codegen", "storyId": "issue-tracker-sync", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "Docs explicitly claim support for Jira, Linear, ClickUp, and Monday.com (update statuses, add comments, link PRs, create tasks), but detailed setup instructions are only shown for Linear (codegen-docs-10); Jira, ClickUp, and Monday.com lack dedicated connection/integration docs equivalent to Linear or Slack. Missing for 10: explicit setup guides or independent verification for Jira/ClickUp/Monday.com, and hands-on confirmation beyond first-party docs.", "evidenceIds": [ "codegen-docs-3", "codegen-docs-10", "codegen-docs-6" ] }, { "productId": "codegen", "storyId": "live-agent-activity-monitoring", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "Codegen's CLI lets developers view agents and run them with 'full telemetry and monitoring,' and agents can 'report progress' via Slack, giving some real-time visibility into agent activity and status. However, there's no explicit documentation of a live dashboard/status stream showing step-by-step actions as they happen, and the closest 'trace' feature is framed as post-hoc analysis/debugging rather than live watching. Missing for 10: a documented real-time UI/dashboard showing live agent status, explicit streaming of in-progress actions, and independent/hands-on confirmation of live monitoring accuracy.", "evidenceIds": [ "codegen-docs-12", "codegen-docs-2", "codegen-docs-17", "codegen-docs-13" ] }, { "productId": "codegen", "storyId": "managed-cloud-execution", "verdict": "full", "quality": 7, "confidence": "medium", "rationale": "Codegen offers cloud-run agents with isolated execution environments, API/SDK/CLI access, and no need to self-host infrastructure (codegen-docs-5, codegen-docs-11, codegen-docs-15, codegen-docs-16). This directly matches running agents without operating own backend. Missing for 10: independent/hands-on verification of the managed infrastructure's reliability at scale, and explicit pricing/SLA details for the managed cloud offering.", "evidenceIds": [ "codegen-docs-5", "codegen-docs-11", "codegen-docs-15", "codegen-docs-16", "codegen-docs-17", "codegen-docs-18" ] }, { "productId": "codegen", "storyId": "manual-model-override", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "No evidence in the pack mentions model selection, choice of underlying LLM, or any setting to override automatic model routing; the docs cover integrations, CLI, SDK, and agent workflows but never reference model choice.", "evidenceIds": [] }, { "productId": "codegen", "storyId": "mid-task-takeover", "verdict": "partial", "quality": 5, "confidence": "low", "rationale": "Codegen supports interacting with agents across multiple surfaces (Slack notifications/clarification requests, CLI to pull agent branches locally, Python SDK to run/monitor agents) and lets you download in-progress work to your local environment, which implies some ability to pick up and continue work. However there is no explicit documentation of pausing, redirecting, or steering a task mid-execution from editor/terminal/browser as a unified handoff flow. Missing for 10: explicit 'takeover' or redirect mid-task feature, editor/IDE integration for live task control, and evidence of seamless cross-surface handoff (Slack->CLI->browser) of the same in-progress task.", "evidenceIds": [ "codegen-docs-2", "codegen-docs-12", "codegen-docs-13", "codegen-docs-15", "codegen-docs-9" ] }, { "productId": "codegen", "storyId": "mobile-approval-of-decisions", "verdict": "partial", "quality": 3, "confidence": "low", "rationale": "Codegen supports Slack-based notifications and agent interaction (docs-2, docs-9), which could technically be used from a phone via the Slack app, but there is no explicit mobile app, push-approval workflow, or documented 'approve on the go' feature for key agent decisions. missing for 10: dedicated mobile/approval UI, evidence of approving specific agent decisions (e.g., merge/deploy gates) from a phone, and any hands-on confirmation of this workflow.", "evidenceIds": [ "codegen-docs-2", "codegen-docs-9" ] }, { "productId": "codegen", "storyId": "one-click-repo-access", "verdict": "full", "quality": 8, "confidence": "medium", "rationale": "Docs explicitly state installing the GitHub App grants repo access with 'no complex setup required', directly matching the story, and this is corroborated by broader repo-integration capabilities (PR review, branch/commit management). Missing for 10: independent/hands-on confirmation of the one-click flow beyond first-party docs.", "evidenceIds": [ "codegen-docs-8", "codegen-docs-4" ] }, { "productId": "codegen", "storyId": "openness-api-parity", "verdict": "partial", "quality": 5, "confidence": "medium", "rationale": "Codegen offers a Python SDK, CLI, and API to create/run agents, pull work, and monitor executions, showing solid programmatic control (codegen-docs-11,15,16,17,18). However, there's no evidence of a complete OpenAPI/API surface matching every UI capability (e.g., Slack/Jira/Linear integration management, PR review workflows) — the OpenAPI probe returned 404s, suggesting no public full API spec. missing for 10: OpenAPI reference confirming full UI parity, evidence that integration configs and review workflows are API-accessible, independent confirmation of API completeness.", "evidenceIds": [ "codegen-docs-11", "codegen-docs-15", "codegen-docs-16", "codegen-docs-17", "codegen-docs-18", "codegen-probe-2" ] }, { "productId": "codegen", "storyId": "openness-full-export", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "No evidence of a bulk data-export feature, open-format export of agent histories/traces, or account-portability tooling; only partial building blocks exist (pulling agent branches via CLI, retrieving traces via API) but nothing documenting a full data export/exit path.", "evidenceIds": [ "codegen-docs-13", "codegen-docs-17" ] }, { "productId": "codegen", "storyId": "openness-open-license", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "No evidence of an open-license source repository or public codebase for Codegen; all evidence relates to product features, docs, CLI, and SDK usage, not source availability or licensing.", "evidenceIds": [] }, { "productId": "codegen", "storyId": "openness-self-host", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "Codegen is presented as a cloud-hosted SaaS agent platform (GitHub App, Slack App, cloud CLI/SDK, hosted API); no evidence of any self-hosted deployment option, on-prem package, or open-source core repository. Absence of evidence for this applicable capability yields 'none'.", "evidenceIds": [] }, { "productId": "codegen", "storyId": "privacy-data-residency", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "No evidence in the pack mentions data residency, region selection, or storage location options for Codegen; the docs cover integrations, CLI, SDK, and agent workflows but nothing about data storage location controls.", "evidenceIds": [] }, { "productId": "codegen", "storyId": "privacy-no-training", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "No evidence in the pack addresses data-training opt-out, privacy controls, or policies about AI model training usage; all citations concern feature functionality (agents, CLI, SDK, integrations) rather than privacy posture.", "evidenceIds": [] }, { "productId": "codegen", "storyId": "privacy-retention-controls", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "No evidence pack items discuss data retention policies, deletion controls, or privacy/compliance settings; the docs cover functionality (agents, integrations, CLI, SDK) but nothing about controlling or deleting stored data.", "evidenceIds": [] }, { "productId": "codegen", "storyId": "privacy-telemetry-optout", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "No evidence pack item mentions a telemetry opt-out setting or privacy controls; in fact docs-12 explicitly notes the CLI runs Claude Code 'with full telemetry and monitoring,' with no mention of a way to disable it.", "evidenceIds": [ "codegen-docs-12" ] }, { "productId": "codegen", "storyId": "query-repo-documentation", "verdict": "none", "quality": 0, "confidence": "low", "rationale": "Evidence shows Codegen can 'improve documentation' as part of coding tasks, but there is no evidence of a feature to generate and then query documentation for a repository (public or private) in a Q&A style. No docs, API, or CLI reference describe a documentation-query capability.", "evidenceIds": [] }, { "productId": "codegen", "storyId": "repo-readiness-report", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "The evidence pack shows PR review, security scanning, and CI/CD triggers, but there is no mention of a dedicated 'readiness report' feature that evaluates overall repository readiness for autonomous agents. Missing for 10: any documentation of a repo-wide readiness/scorecard report, criteria for agent-readiness, or a command/API that produces such an assessment.", "evidenceIds": [ "codegen-docs-20", "codegen-docs-4", "codegen-docs-5" ] }, { "productId": "codegen", "storyId": "security-alert-remediation", "verdict": "partial", "quality": 5, "confidence": "low", "rationale": "Codegen documents a security/secrets scan that posts a review comment on failure (codegen-docs-20) and general bug-fixing/PR-creation capabilities plus CI/CD-triggered agents (codegen-docs-1, codegen-docs-4, codegen-docs-18), suggesting it could act on a flagged issue and open a fix PR. However, there is no explicit evidence of ingesting external security alerts (e.g., Dependabot/CodeQL/Snyk), validating them as true positives, or automatically opening a remediation PR end-to-end — the documented scan flow stops at commenting, not remediating.\nmissing for 10: explicit security-alert ingestion/validation workflow, evidence of an opened remediation PR tied to a specific alert, independent confirmation of this flow working in practice.", "evidenceIds": [ "codegen-docs-20", "codegen-docs-18", "codegen-docs-4", "codegen-docs-1" ] }, { "productId": "codegen", "storyId": "self-hosted-agent-deployment", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "Codegen is presented entirely as a cloud/SaaS agent service (GitHub App, Slack App, API, SDK, isolated cloud environments) with no evidence of a self-hostable runtime, container image, or on-prem/VM deployment option; the CLI logs local Claude Code sessions to Codegen's own cloud rather than hosting infrastructure locally.", "evidenceIds": [ "codegen-docs-5", "codegen-docs-7", "codegen-docs-8", "codegen-docs-12", "codegen-docs-16" ] }, { "productId": "codegen", "storyId": "steer-active-session", "verdict": "none", "quality": 0, "confidence": "low", "rationale": "No evidence describes sending follow-up instructions to an already-running agent session to redirect its work; docs only describe initiating agents via prompts, Slack notifications, or SDK task creation, not mid-session steering. Missing for 10: any documentation of a 'continue/steer/update' API, chat-like follow-up in an active run, or session-resume mechanism.", "evidenceIds": [] }, { "productId": "codegen", "storyId": "task-completion-notifications", "verdict": "full", "quality": 8, "confidence": "medium", "rationale": "Codegen explicitly supports sending notifications, asking for clarification, and reporting progress directly in Slack channels, plus updating statuses/comments in Jira, Linear, Clickup, and Monday.com, covering both completion and input-needed scenarios. missing for 10: independent/hands-on confirmation of real-time notification delivery, and no explicit mention of email/other channel notification options.", "evidenceIds": [ "codegen-docs-2", "codegen-docs-3", "codegen-docs-9", "codegen-docs-10" ] }, { "productId": "codegen", "storyId": "tiered-autonomy-levels", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "No evidence describes configurable autonomy tiers, approval gates, or permission levels for agent actions; the docs describe what agents can do (code, PRs, Slack, Jira/Linear) but nothing about setting graduated confirmation thresholds for an engineering lead to control autonomy.", "evidenceIds": [] }, { "productId": "codegen", "storyId": "trigger-agent-on-build-failure", "verdict": "full", "quality": 8, "confidence": "medium", "rationale": "Docs explicitly state agents can be 'Triggered from CI/CD pipelines when builds fail' and support fixing bugs/tests via API/SDK with programmatic control, which directly matches the story. Missing for 10: independent/hands-on confirmation of a real CI/CD integration example (e.g., GitHub Actions snippet) and details on how failing test context is passed to the agent.", "evidenceIds": [ "codegen-docs-18", "codegen-docs-16", "codegen-docs-11", "codegen-docs-15" ] }, { "productId": "codegen", "storyId": "visual-context-for-tasks", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "No evidence in the pack mentions attaching screenshots, images, or marked-up mockups to a task/prompt; Codegen's docs cover text prompts, Slack/Jira/Linear integrations, CLI/SDK, and PR review but nothing about visual/image input for implementing UI changes. Figma is mentioned only as a connectable integration, not as an image-annotation workflow.", "evidenceIds": [ "codegen-docs-6" ] }, { "productId": "devin", "storyId": "agentic-agent-docs", "verdict": "full", "quality": 7, "confidence": "medium", "rationale": "Devin explicitly supports AGENTS.md, an agent-oriented documentation file that Devin reads before starting a coding session to gain project context — directly matching the story's intent of pointing an agent at agent-oriented docs. Devin's own docs site also serves an llms.txt endpoint (verified via probe), showing familiarity with the convention, though this is Devin's own site rather than evidence Devin ingests arbitrary llms.txt files from user projects. Missing for 10: explicit documentation that Devin consumes/parses llms.txt files themselves (as opposed to AGENTS.md) and independent hands-on confirmation of this workflow.", "evidenceIds": [ "devin-docs-24", "devin-probe-1" ] }, { "productId": "devin", "storyId": "agentic-ai-insights", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "Devin's repo indexing enables 'Ask Devin' and DeepWiki features that generate AI insights and explanations from your codebase data, and Devin can proactively suggest fixes/improvements via code review auto-fix and bug triage. However, evidence is scoped only to codebase/ticket data, not broader business or analytics data, and there's no dedicated insights dashboard or proactive summarization feature documented. Missing for 10: evidence of insights beyond code (e.g., analytics/dashboards), independent corroboration of Ask Devin/DeepWiki quality, and any UI showcasing generated insights.", "evidenceIds": [ "devin-docs-22", "devin-docs-1", "devin-docs-20", "devin-docs-31" ] }, { "productId": "devin", "storyId": "agentic-autonomous-automation", "verdict": "full", "quality": 8, "confidence": "high", "rationale": "Devin supports scheduling via cron expressions (recurring/one-time) and agent selection through its MCP tooling, plus API session creation and Slack/Teams triggering, enabling autonomous background automations beyond interactive chat sessions. missing for 10: independent/hands-on verification of scheduled runs actually executing unattended, and more detail on failure/retry handling for scheduled tasks.", "evidenceIds": [ "devin-docs-17", "devin-docs-15", "devin-docs-8", "devin-docs-9", "devin-docs-19", "devin-docs-20" ] }, { "productId": "devin", "storyId": "agentic-builtin-assistant", "verdict": "full", "quality": 8, "confidence": "high", "rationale": "Devin itself functions as the built-in AI assistant to which tasks are delegated across web app, terminal, Slack/Teams, and API, with rich first-party documentation of the delegation workflow (docs-1,3,4,5,8,19). Community feedback raises reliability/skepticism concerns but does not concretely contradict that delegation itself works, so it's not disputed. Missing for 10: independent hands-on validation of delegation reliability/success rate beyond old demo critiques.", "evidenceIds": [ "devin-docs-1", "devin-docs-3", "devin-docs-4", "devin-docs-5", "devin-docs-8", "devin-docs-19", "devin-comm-2" ] }, { "productId": "devin", "storyId": "agentic-headless", "verdict": "full", "quality": 8, "confidence": "high", "rationale": "Devin exposes a full REST API for session creation and automation (devin-docs-8, devin-docs-9), supports scheduling recurring/one-time headless runs via MCP (devin-docs-17), auto-triggers on CI failures and code review comments via Devin Review/Auto-Fix (devin-docs-20), and supports parallel/headless session orchestration (devin-docs-18). This covers CI/automation use cases well beyond interactive use. Missing for 10: no independent/hands-on CI-integration case study, and no dedicated 'CI runner' or GitHub Actions-style first-class integration doc beyond the review/auto-fix hook.", "evidenceIds": [ "devin-docs-8", "devin-docs-9", "devin-docs-17", "devin-docs-20", "devin-docs-18", "devin-docs-21" ] }, { "productId": "devin", "storyId": "agentic-mcp-client", "verdict": "full", "quality": 9, "confidence": "high", "rationale": "Devin CLI explicitly supports connecting external MCP servers, exposing their tools as slash commands with permission controls, and docs list example integrations (Datadog, Sentry, databases, Figma, Notion, Stripe, etc.). Missing for 10: independent/hands-on community verification of MCP tool usage in practice.", "evidenceIds": [ "devin-docs-12", "devin-docs-13", "devin-docs-14", "devin-docs-21", "devin-probe-3" ] }, { "productId": "devin", "storyId": "agentic-mcp-server", "verdict": "full", "quality": 8, "confidence": "medium", "rationale": "Devin ships an official MCP server (documented at docs.devin.ai/work-with-devin/devin-mcp) that exposes session management, playbooks, knowledge, and scheduling to any MCP-compatible agent or IDE — this is distinct from Devin's own MCP-client capability and directly satisfies the 'connect an agent via an official MCP server' story. Missing for 10: independent/hands-on confirmation that external agents successfully connect and operate through this server, and more detail on setup/auth flow beyond first-party docs.", "evidenceIds": [ "devin-docs-15", "devin-docs-16", "devin-docs-17", "devin-docs-29" ] }, { "productId": "devin", "storyId": "agentic-nl-commands", "verdict": "full", "quality": 8, "confidence": "high", "rationale": "Devin is fundamentally driven by natural-language task requests across web app, Slack/Teams tagging, CLI, and API (create_as_user_id sessions), letting users describe tasks like bug fixes, features, and migrations in plain language rather than issuing structured commands. Community evidence corroborates that Devin operates via conversational/task-based prompts even amid skepticism about output quality. missing for 10: independent hands-on confirmation of NL command fidelity across all surfaces (CLI/Slack/API), and no evidence addressing ambiguous or multi-step NL instruction handling limits.", "evidenceIds": [ "devin-docs-1", "devin-docs-4", "devin-docs-5", "devin-docs-19", "devin-docs-8", "devin-docs-9", "devin-comm-6" ] }, { "productId": "devin", "storyId": "agentic-official-cli", "verdict": "full", "quality": 8, "confidence": "high", "rationale": "Devin ships an official CLI documented extensively (adaptive model routing, slash commands, MCP extensibility) confirmed by both docs and a probe hit, plus a separate Terminal/local CLI mode for quick fixes. Missing for 10: independent hands-on review of the CLI itself and more detail on installation/versioning.", "evidenceIds": [ "devin-docs-2", "devin-docs-10", "devin-docs-11", "devin-docs-12", "devin-docs-14", "devin-probe-4" ] }, { "productId": "devin", "storyId": "agentic-public-api", "verdict": "full", "quality": 8, "confidence": "high", "rationale": "Devin publishes a documented public API for integrating into applications, automating workflows, and creating sessions programmatically (including on behalf of other users), plus an MCP server exposing session/playbook/knowledge/scheduling management to external agents. Missing for 10: a discoverable machine-readable OpenAPI/swagger spec (probe found 404s) and independent third-party corroboration of API usage beyond vendor docs.", "evidenceIds": [ "devin-docs-8", "devin-docs-9", "devin-docs-15", "devin-docs-29", "devin-probe-1", "devin-probe-2" ] }, { "productId": "devin", "storyId": "agentic-scoped-keys", "verdict": "partial", "quality": 3, "confidence": "low", "rationale": "Devin documents a permission system that restricts MCP tool access at multiple levels and an API parameter (create_as_user_id) for acting on behalf of specific users, but there is no direct documentation of issuing scoped/least-privilege API keys or credentials for the agent itself. Missing for 10: explicit API key/token scoping mechanism, documented permission tiers or roles for API credentials, and any least-privilege credential issuance workflow.", "evidenceIds": [ "devin-docs-13", "devin-docs-9", "devin-docs-8" ] }, { "productId": "devin", "storyId": "agentic-sdks", "verdict": "full", "quality": 8, "confidence": "high", "rationale": "Devin publishes a documented API reference for building integrations (create sessions, manage on behalf of users, automate workflows), plus MCP support for extensibility — clear official SDK-like surface for AI-native developers. Missing for 10: no official language-specific SDK packages (e.g. Python/JS client libraries) are cited, only REST API docs and no discoverable OpenAPI spec (probe found 404s), and no independent/hands-on corroboration of API usage.", "evidenceIds": [ "devin-docs-8", "devin-docs-9", "devin-docs-12", "devin-docs-15", "devin-probe-2" ] }, { "productId": "devin", "storyId": "agentic-webhooks", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "No evidence in the pack mentions webhooks or event subscription mechanisms for Devin; only API session creation, MCP integrations, and CLI features are documented. As an API-driven agentic product, webhook support would be a plausible and expected capability, so its absence counts as 'none' rather than 'na'.", "evidenceIds": [] }, { "productId": "devin", "storyId": "ai-automated-test-generation-on-change", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "Devin's docs describe autonomous CI-failure iteration and auto-fix behavior (devin-docs-20) and a terminal for executing/observing command output (devin-docs-6), implying it runs and reacts to test/build results before finalizing changes, but there is no explicit documentation of Devin proactively writing new tests and running them pre-emptively to self-validate before proposing a PR. missing for 10: explicit test-generation workflow documentation, evidence of Devin authoring test cases for new code, independent hands-on confirmation of this specific validation loop.", "evidenceIds": [ "devin-docs-20", "devin-docs-6", "devin-docs-31" ] }, { "productId": "devin", "storyId": "ai-automatic-model-routing", "verdict": "full", "quality": 8, "confidence": "medium", "rationale": "Devin's CLI has an explicit \"Adaptive\" model router that analyzes each prompt and automatically routes it to the best-performing model, with an override via /model if manual control is desired. This directly matches the story of automatic per-task model routing, though it's documented only for the CLI product surface and lacks independent/hands-on validation of routing quality. Missing for 10: independent corroboration of routing accuracy, and confirmation this applies across all Devin surfaces (web/IDE) not just CLI.", "evidenceIds": [ "devin-docs-10", "devin-docs-11", "devin-docs-26", "devin-probe-4" ] }, { "productId": "devin", "storyId": "ai-issue-triage", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "Devin can be pointed at Linear/Jira tickets or tagged in Slack to work a bug, but nothing in the evidence describes automatic triage logic that assesses severity or routes issues to the correct owner — these are core, specific parts of the story. Missing for 10: severity classification/scoring, ownership/routing logic, any 'triage' feature or automated inbox-sorting workflow.", "evidenceIds": [ "devin-docs-1", "devin-docs-4", "devin-docs-19" ] }, { "productId": "devin", "storyId": "api-interactive-docs", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "Devin documents an API reference overview and parameters (e.g. create_as_user_id) but nothing describes an interactive, in-browser 'try it' experience with runnable code examples; a probe for standard OpenAPI/Swagger interactive-doc endpoints returned 404s, suggesting no such interactive explorer exists.", "evidenceIds": [ "devin-docs-8", "devin-docs-9", "devin-probe-2" ] }, { "productId": "devin", "storyId": "api-machine-spec", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "Devin has a documented API (devin-docs-8, devin-docs-9) but a direct probe for OpenAPI/swagger spec files at common paths returned 404s (devin-probe-2), and no docs page offers a downloadable machine-readable spec.", "evidenceIds": [ "devin-docs-8", "devin-probe-2" ] }, { "productId": "devin", "storyId": "api-sandbox", "verdict": "partial", "quality": 3, "confidence": "low", "rationale": "Devin's docs describe each session running in its own configurable workspace/environment (devin-docs-25) and connecting via VPN to reach internal services rather than being embedded in production (devin-docs-23), which implies some environment isolation, but there is no explicit claim of a dedicated 'sandbox vs production' testing mode or safeguards against touching production data. missing for 10: explicit documentation of a production-safe/sandboxed test environment, evidence of data isolation guarantees, and independent confirmation that production systems are never touched.", "evidenceIds": [ "devin-docs-25", "devin-docs-23" ] }, { "productId": "devin", "storyId": "api-versioning-policy", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "There is an API reference (devin-docs-8) but no evidence of API versioning scheme or a documented deprecation policy; the openapi.json probe returned 404s, suggesting no formal machine-readable spec either. Missing for 10: versioning scheme documentation, deprecation policy/notice, changelog for breaking changes.", "evidenceIds": [ "devin-docs-8", "devin-probe-2" ] }, { "productId": "devin", "storyId": "approve-plan-before-execution", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "The evidence pack describes Devin's task delegation, IDE handoff, terminal visibility, and MCP integrations, but nowhere documents a plan-review/approval gate before Devin begins making code changes. Missing for 10: any doc mentioning an upfront plan proposal, explicit approval step, or 'plan mode' prior to execution.", "evidenceIds": [ "devin-docs-5", "devin-docs-1" ] }, { "productId": "devin", "storyId": "approve-task-contract", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "The evidence pack shows delegation, review-after-the-fact, and IDE takeover workflows, but no documented mechanism where an engineering-lead must approve a task's scope/contract before Devin is allowed to start modifying the repository. Missing for 10: explicit pre-execution approval/gating workflow, scope/contract sign-off UI or API, evidence of blocking repo writes until lead approval.", "evidenceIds": [ "devin-docs-5", "devin-docs-3" ] }, { "productId": "devin", "storyId": "assign-task-from-ticket", "verdict": "full", "quality": 8, "confidence": "medium", "rationale": "Docs explicitly state Devin can be asked to tackle Linear/Jira tickets, tagged on Slack/Teams threads about bugs to start sessions, and integrated via API/MCP to create sessions from external triggers, directly supporting assigning tasks from existing issues/tickets. Missing for 10: independent hands-on verification of ticket-to-task handoff and no direct product screenshot of ticket-linking UI in evidence.", "evidenceIds": [ "devin-docs-1", "devin-docs-19", "devin-docs-4", "devin-docs-8", "devin-docs-9" ] }, { "productId": "devin", "storyId": "auto-approve-all-actions", "verdict": "none", "quality": 0, "confidence": "low", "rationale": "The evidence pack describes permission systems for MCP tools (devin-docs-13) and autonomous features like Auto-Fix (devin-docs-20), but there is no documentation of a setting that lets a developer toggle between 'confirm each action' and 'auto-approve all actions' for Devin's own task execution. missing for 10: explicit auto-approve/confirmation toggle documentation, evidence of a per-action approval workflow that can be disabled, any community confirmation of this specific control.", "evidenceIds": [ "devin-docs-13", "devin-docs-20" ] }, { "productId": "devin", "storyId": "auto-fix-readiness-gaps", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "Devin Review with Auto-Fix explicitly auto-fixes CI failures and review comments, which could cover agent-readiness gate checks if they're wired into CI/PR review, and AGENTS.md support shows Devin can consume agent-readiness config files. However there's no evidence of a dedicated 'agent-readiness criteria' scorecard or targeted remediation workflow distinct from generic CI/review auto-fix. Missing for 10: explicit agent-readiness scoring/criteria detection, evidence of automatically remediating specific agent-readiness failures (vs generic CI/review comments), and independent validation of this auto-fix loop working reliably.", "evidenceIds": [ "devin-docs-20", "devin-docs-24", "devin-docs-25" ] }, { "productId": "devin", "storyId": "auto-open-pr-on-completion", "verdict": "partial", "quality": 5, "confidence": "low", "rationale": "Devin is known to work within GitHub workflows and create PRs as part of its task completion (implied by its coding-agent nature and features like Auto-Fix responding to review comments/CI), but the evidence pack has no explicit documentation describing a configurable setting to auto-open a pull request upon task completion. missing for 10: explicit docs describing PR-creation-on-completion configuration, settings/toggle for auto-PR behavior, and independent confirmation of this specific workflow.", "evidenceIds": [ "devin-docs-20", "devin-docs-1", "devin-docs-5" ] }, { "productId": "devin", "storyId": "automated-pr-review", "verdict": "partial", "quality": 5, "confidence": "low", "rationale": "Devin documents a 'Devin Review with Auto-Fix' feature that responds to code review comments, fixes flagged bugs, and iterates on CI failures, implying an automated review capability tied to PRs, but the evidence pack gives no detail on how review comments are generated, whether they are inline, or how coverage/setup works for 'every PR'. missing for 10: explicit documentation of inline AI-generated PR comments, configuration for gating every PR, and independent/hands-on confirmation of the review quality.", "evidenceIds": [ "devin-docs-20" ] }, { "productId": "devin", "storyId": "automatic-environment-setup", "verdict": "full", "quality": 8, "confidence": "high", "rationale": "Devin's docs describe indexing repos, configuring a persistent environment/workspace that boots into a known-good state every session, VPN access to internal registries/git hosts, and AGENTS.md-based setup instructions it follows before coding — together these show it autonomously clones, configures, and prepares its own working environment. missing for 10: independent/hands-on verification of the clone+dependency-install flow end-to-end, and explicit documentation of automatic dependency installation step-by-step.", "evidenceIds": [ "devin-docs-22", "devin-docs-23", "devin-docs-24", "devin-docs-25" ] }, { "productId": "devin", "storyId": "automation-bulk-operations", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "Devin's docs describe running multiple Devins in parallel for independent tasks, an API for creating sessions programmatically (including on behalf of other users), and scheduling capabilities—together supporting bulk/automated task dispatch across many items. However, there's no concrete example or independent evidence of actually orchestrating large-scale bulk operations (e.g., processing hundreds of tickets at once), and community evidence focuses on single-task demos rather than bulk workflows. Missing for 10: hands-on/independent evidence of large-scale bulk runs, explicit parallelism limits, and a documented case study of bulk item processing.", "evidenceIds": [ "devin-docs-18", "devin-docs-8", "devin-docs-9", "devin-docs-17" ] }, { "productId": "devin", "storyId": "automation-rules-engine", "verdict": "partial", "quality": 7, "confidence": "medium", "rationale": "Devin supports event-driven automation via scheduled/cron triggers (create schedules with cron expressions, notification preferences) and via Devin Review's Auto-Fix which automatically responds to code review comments and CI failures, plus API-based workflow automation. However, these are fixed built-in trigger types (schedule, CI/review events) rather than a general user-defined rules engine for arbitrary events. Missing for 10: evidence of a flexible custom rule/trigger builder covering arbitrary event sources (e.g., webhooks, ticket status changes) beyond scheduling and code-review auto-fix, and independent confirmation these triggers work reliably in practice.", "evidenceIds": [ "devin-docs-17", "devin-docs-20", "devin-docs-8", "devin-docs-15" ] }, { "productId": "devin", "storyId": "automation-scheduled-jobs", "verdict": "full", "quality": 8, "confidence": "medium", "rationale": "Devin's MCP documentation explicitly states scheduling support with 'cron expressions for recurring schedules, one-time scheduling, notification preferences, and agent selection,' and this scheduling capability is exposed via MCP for session/playbook management. Missing for 10: independent/hands-on corroboration of recurring job execution in practice and more detail on scheduling UI/reliability beyond the docs mention.", "evidenceIds": [ "devin-docs-15", "devin-docs-17", "devin-docs-29" ] }, { "productId": "devin", "storyId": "automation-versioned-workflows", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "No evidence of version history, review/diff UI, or rollback of Devin's automations (playbooks, schedules, sessions); docs mention creating/scheduling sessions and playbooks but never versioning, auditing changes, or reverting them. missing for 10: version history for playbooks/sessions, review/approval workflow for automation changes, rollback/undo mechanism.", "evidenceIds": [ "devin-docs-15", "devin-docs-17", "devin-docs-30" ] }, { "productId": "devin", "storyId": "autonomous-bug-fixing", "verdict": "disputed", "quality": 5, "confidence": "medium", "rationale": "Devin's docs explicitly describe autonomous bug repro/fix workflows, Slack-triggered bug sessions, and auto-fix on CI/review failures (devin-docs-1, devin-docs-4, devin-docs-19, devin-docs-20), supporting the core story. However, independent commentary cites a low real-world resolution rate (13.8% of issues resolved) and hands-on accounts describing shallow fixes (e.g., wrapping errors in try/catch rather than真正 diagnosing root cause), directly contradicting the strength of the autonomous-fix claim (devin-comm-2, devin-comm-6, devin-comm-4). Missing for 10: independent benchmark showing high success rate, more recent hands-on verification of bug-fix quality, and resolution of the discrepancy between vendor claims and community-reported performance.", "evidenceIds": [ "devin-docs-1", "devin-docs-4", "devin-docs-19", "devin-docs-20", "devin-comm-2", "devin-comm-6", "devin-comm-4" ] }, { "productId": "devin", "storyId": "bring-your-own-model", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "Docs describe an internal 'Adaptive' model router and a /model command to pick among Devin's supported models, but there is no evidence of bringing a custom LLM or your own API key to run agents on a model of the engineering lead's choice.", "evidenceIds": [ "devin-docs-10", "devin-docs-11", "devin-docs-26" ] }, { "productId": "devin", "storyId": "chat-with-agent-in-slack", "verdict": "full", "quality": 8, "confidence": "high", "rationale": "Docs explicitly describe tagging Devin in Slack/Teams threads to discuss and delegate bugs/tasks, starting sessions directly from conversations. Missing for 10: independent/hands-on corroboration of this specific chat-tagging workflow beyond vendor docs.", "evidenceIds": [ "devin-docs-4", "devin-docs-19", "devin-docs-1" ] }, { "productId": "devin", "storyId": "ci-failure-auto-fix", "verdict": "full", "quality": 7, "confidence": "medium", "rationale": "Devin explicitly documents 'Devin Review with Auto-Fix' that responds to code review comments, fixes flagged bugs, and iterates on CI failures, directly matching the story of auto-diagnosing and fixing failed CI with a proposed PR. However, this is vendor-only documentation with no independent/hands-on corroboration of the CI-fix workflow specifically. Missing for 10: independent verification of CI-fix accuracy/success rate, details on PR proposal format, and evidence of real-world reliability beyond the single docs line.", "evidenceIds": [ "devin-docs-20" ] }, { "productId": "devin", "storyId": "codebase-context-file", "verdict": "full", "quality": 9, "confidence": "high", "rationale": "Devin explicitly supports AGENTS.md files placed in the project root that Devin reads before coding, plus a separate 'Knowledge' feature for sharing codebase-level conventions/context to improve plans and code generation. Missing for 10: independent/hands-on confirmation that this measurably improves plan/code relevance beyond vendor docs.", "evidenceIds": [ "devin-docs-24", "devin-docs-30", "devin-docs-22" ] }, { "productId": "devin", "storyId": "concurrent-task-execution", "verdict": "partial", "quality": 7, "confidence": "medium", "rationale": "Devin docs explicitly document running multiple Devins in parallel on independent tasks and provide an API with create_as_user_id for programmatically spawning sessions on behalf of org members, directly supporting concurrent scaling of throughput. However, there's no first-party data on concurrency limits, cost/resource management at scale, or independent case studies validating throughput gains at team scale; missing for 10: quantified concurrency limits/scaling guidance, independent evidence of large-scale parallel usage, and cost/governance controls for many simultaneous sessions.", "evidenceIds": [ "devin-docs-18", "devin-docs-8", "devin-docs-9", "devin-docs-15" ] }, { "productId": "devin", "storyId": "connect-github-repo", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "Docs confirm repository indexing (public/private) and GitHub-repo access for context, plus 'Devin Review with Auto-Fix' responding to code review/CI, implying PR interaction, but there's no explicit documentation of a GitHub connection/auth flow or of Devin opening pull requests directly. missing for 10: explicit GitHub app/OAuth connection setup steps, explicit 'opens pull requests' documentation, independent/hands-on confirmation of PR creation.", "evidenceIds": [ "devin-docs-22", "devin-docs-16", "devin-docs-20", "devin-docs-23" ] }, { "productId": "devin", "storyId": "daily-task-quota-limits", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "No evidence pack item mentions plan tiers, daily task quotas, concurrency limits, or an admin console/dashboard for managing such limits; docs cover running parallel Devins and API session creation but not visibility or management of plan-based limits. Missing for 10: pricing/plan documentation, admin UI for concurrency/task caps, usage dashboards or alerts for limit management.", "evidenceIds": [ "devin-docs-18", "devin-docs-9" ] }, { "productId": "devin", "storyId": "delegate-sessions-for-teammates", "verdict": "full", "quality": 8, "confidence": "medium", "rationale": "Devin's API explicitly supports creating sessions on behalf of any user in the organization via the create_as_user_id parameter, directly matching the engineering-lead use case for org-wide session delegation. Missing for 10: independent/hands-on corroboration of this specific feature and details on permission scoping/audit controls for delegated sessions.", "evidenceIds": [ "devin-docs-9", "devin-docs-8" ] }, { "productId": "devin", "storyId": "describe-feature-in-plain-language", "verdict": "full", "quality": 7, "confidence": "medium", "rationale": "Devin's docs describe accepting natural-language asks (tickets, Slack/Teams messages, delegated tasks) and turning them into sessions/implementation tasks, which is the core intent-to-spec workflow (devin-docs-1, devin-docs-4, devin-docs-5, devin-docs-19). Community commentary raises concerns about actual task success rates and correctness (devin-comm-2, devin-comm-4), tempering confidence in scoping quality. Missing for 10: independent hands-on evidence that the auto-generated task scope/plan is accurate or reviewable before execution, and no dedicated 'plan/spec preview' feature described.", "evidenceIds": [ "devin-docs-1", "devin-docs-4", "devin-docs-5", "devin-docs-19", "devin-comm-2", "devin-comm-4" ] }, { "productId": "devin", "storyId": "design-to-code-handoff", "verdict": "partial", "quality": 5, "confidence": "medium", "rationale": "Devin's docs show it can autonomously implement features from tickets (Jira/Linear) and be tagged by non-engineers on Slack/Teams, supporting the general 'PM delegates, Devin implements' workflow, and it can connect to Figma via MCP integrations. However, there is no documented mockup-to-code pipeline, no PM-specific onboarding flow, and no evidence of turning a design file into a working UI end-to-end. missing for 10: direct design/mockup ingestion workflow, PM-focused case study or walkthrough, independent evidence of successful design-to-implementation outcomes.", "evidenceIds": [ "devin-docs-1", "devin-docs-19", "devin-docs-21", "devin-docs-5" ] }, { "productId": "devin", "storyId": "diff-review-before-pr", "verdict": "partial", "quality": 4, "confidence": "low", "rationale": "Docs mention delegating a task and 'taking over in Devin's IDE once it gives you a good first draft' (devin-docs-5), implying some human checkpoint before final output, and Devin's terminal/log viewing lets you watch work in progress (devin-docs-6). However there is no explicit documentation of a diff-review/approve-before-PR gate — 'Devin Review with Auto-Fix' (devin-docs-20) is about post-PR review comment handling, not a pre-PR approval step. Missing for 10: explicit diff viewer with approve/reject before PR creation, documentation of a formal approval gate, and independent/hands-on confirmation of this workflow.", "evidenceIds": [ "devin-docs-5", "devin-docs-6", "devin-docs-20" ] }, { "productId": "devin", "storyId": "end-to-end-feature-implementation", "verdict": "disputed", "quality": 5, "confidence": "medium", "rationale": "Devin's docs explicitly claim end-to-end feature implementation (docs-1, docs-31) and even test-writing is implied as part of full feature delivery, but independent hands-on reporting from the original demo showed only 13.8% of issues resolved and reviewers noted the work was often only ~90% correct or used hacky fixes (e.g. wrapping in try-catch) rather than proper fixes (devin-comm-2, devin-comm-4, devin-comm-6), directly contradicting the 'implements features end-to-end reliably' claim. missing for 10: verified success-rate data on test-writing specifically, more recent independent benchmarks showing improved reliability, and evidence tests are written/passed as part of the workflow rather than just code changes.", "evidenceIds": [ "devin-docs-1", "devin-docs-31", "devin-comm-2", "devin-comm-4", "devin-comm-6" ] }, { "productId": "devin", "storyId": "enterprise-sso-licensing", "verdict": "none", "quality": 0, "confidence": "low", "rationale": "No evidence pack items mention enterprise licensing, SSO, or commercial support offerings for Devin; only product feature docs and community sentiment are present. Missing for 10: SSO/SAML documentation, enterprise plan/pricing page, commercial support SLA details.", "evidenceIds": [] }, { "productId": "devin", "storyId": "feedback-to-task-conversion", "verdict": "partial", "quality": 5, "confidence": "low", "rationale": "Devin can ingest raw inputs like Linear/Jira tickets and Slack/Teams threads about bugs or feature requests and turn them into an actionable session (devin-docs-1, devin-docs-4, devin-docs-19), which covers the 'feedback-to-task' intake path, but there is no evidence of a distinct structured-task output with an explicit proposed scope/plan artifact for PM review before execution. Missing for 10: evidence of a scoping/planning deliverable (e.g., a spec doc or task breakdown with effort/scope estimate) generated prior to coding, and any PM-specific workflow or UI for reviewing/approving that scope.", "evidenceIds": [ "devin-docs-1", "devin-docs-4", "devin-docs-19", "devin-docs-5" ] }, { "productId": "devin", "storyId": "headless-ci-execution", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "Devin's API is explicitly designed to 'integrate Devin into your applications, automate workflows, and build powerful tools' and supports creating sessions on behalf of users, which enables scripted/headless invocation, and a CLI is also documented. However, there is no explicit CI/CD pipeline example, exit-code/status handling, or GitHub Actions/shell-script integration guide in the evidence — the closest CI-related capability shown is 'Auto-Fix' reacting to CI failures rather than Devin running as a CI step itself. missing for 10: explicit CI/CD pipeline integration docs (e.g., GitHub Actions/shell script examples), documented headless/non-interactive CLI invocation with exit codes.", "evidenceIds": [ "devin-docs-8", "devin-docs-9", "devin-docs-20", "devin-probe-4" ] }, { "productId": "devin", "storyId": "isolated-sandbox-execution", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "Docs describe Devin operating in its own workspace/VM with terminal execution, environment configuration ('known-good state'), VPN access to internal services, and multi-platform (Android/macOS/Windows) build environments, implying isolated sandboxed execution, but there is no explicit documentation of sandbox security guarantees, dependency isolation, or safety controls around code execution, and no independent/hands-on verification of sandbox isolation. missing for 10: explicit sandbox/isolation security documentation, independent verification of safe execution, details on dependency install safety and containment.", "evidenceIds": [ "devin-docs-6", "devin-docs-23", "devin-docs-25", "devin-docs-27", "devin-docs-28", "devin-docs-32" ] }, { "productId": "devin", "storyId": "issue-tracker-sync", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "Devin explicitly supports Linear and Jira tickets natively (devin-docs-1) and its MCP extensibility (devin-docs-12, devin-docs-21) lets it connect to arbitrary tool servers including issue trackers, which could cover ClickUp/Monday.com. However, there's no first-party documentation of native ClickUp or Monday.com connectors, and no independent/hands-on evidence confirming ticket-management workflows actually work end-to-end via these integrations. missing for 10: explicit native ClickUp/Monday.com support docs, independent verification of issue-tracker workflows succeeding in practice.", "evidenceIds": [ "devin-docs-1", "devin-docs-12", "devin-docs-21", "devin-docs-13" ] }, { "productId": "devin", "storyId": "live-agent-activity-monitoring", "verdict": "full", "quality": 7, "confidence": "medium", "rationale": "Devin's docs describe real-time observability features like watching the terminal execute commands with live output logs, jumping in during interactive browsing, and taking over in Devin's IDE mid-task, all indicating a live view of the agent's current activity/status. Missing for 10: no explicit dedicated 'status dashboard' documentation and no independent/hands-on corroboration of the real-time monitoring UX beyond vendor docs.", "evidenceIds": [ "devin-docs-6", "devin-docs-7", "devin-docs-5" ] }, { "productId": "devin", "storyId": "managed-cloud-execution", "verdict": "full", "quality": 7, "confidence": "medium", "rationale": "Devin is delivered as a cloud-hosted agent (web app, API, session creation on behalf of users) so developers don't need to run their own backend/agent infrastructure — sessions run on Devin's managed cloud with its own configured environment, VPN access, and parallel execution. missing for 10: independent/hands-on confirmation of the managed infrastructure model, and explicit documentation of uptime/scaling/ops guarantees for the hosted service.", "evidenceIds": [ "devin-docs-3", "devin-docs-5", "devin-docs-8", "devin-docs-9", "devin-docs-18", "devin-docs-23", "devin-docs-25" ] }, { "productId": "devin", "storyId": "manual-model-override", "verdict": "full", "quality": 8, "confidence": "high", "rationale": "Devin's docs explicitly state that Adaptive automatically routes prompts to the best model, but users can switch away from Adaptive to a specific model at any time using /model, directly matching the story. Missing for 10: independent/hands-on corroboration of the /model switch in practice and more detail on which specific models are selectable.", "evidenceIds": [ "devin-docs-10", "devin-docs-11", "devin-docs-26", "devin-probe-4" ] }, { "productId": "devin", "storyId": "mid-task-takeover", "verdict": "full", "quality": 8, "confidence": "high", "rationale": "Docs explicitly describe multi-surface handoff: delegate via web app then take over in Devin's IDE, use /handoff to send terminal tasks to cloud Devin, jump into the Interactive Browser to help Devin navigate, and tag Devin in Slack/Teams threads to redirect work — covering editor, terminal, and browser takeover. Missing for 10: independent/hands-on confirmation of a smooth mid-task handoff experience (community evidence is generic skepticism, not a concrete handoff failure).", "evidenceIds": [ "devin-docs-3", "devin-docs-5", "devin-docs-6", "devin-docs-7", "devin-docs-19", "devin-docs-2" ] }, { "productId": "devin", "storyId": "mobile-approval-of-decisions", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "No evidence describes a mobile app or phone-based approval interface for agent decisions; docs mention Slack/Teams tagging, web app, IDE, and scheduling notifications but nothing about approving decisions from a phone while sessions continue. The axis is plausible for an agentic dev tool with human-in-the-loop needs, but no supporting evidence exists.", "evidenceIds": [] }, { "productId": "devin", "storyId": "one-click-repo-access", "verdict": "partial", "quality": 5, "confidence": "medium", "rationale": "Devin does support connecting to repos (indexing, GitHub-based session creation, VPN access to internal git hosts) and this is described as straightforward onboarding, but there's no evidence of a literal 'one-click install' flow like a GitHub App marketplace install — setup instead involves indexing, environment configuration, AGENTS.md, and knowledge onboarding steps. Missing for 10: explicit one-click/marketplace-style install flow, independent evidence of setup simplicity, and confirmation that repo access requires no additional configuration.", "evidenceIds": [ "devin-docs-22", "devin-docs-23", "devin-docs-25", "devin-docs-24" ] }, { "productId": "devin", "storyId": "openness-api-parity", "verdict": "partial", "quality": 5, "confidence": "low", "rationale": "Devin exposes a documented API (session creation, create_as_user_id) and an MCP server that surfaces session management, playbooks, knowledge, and scheduling (devin-docs-8,9,15,29), suggesting broad but not full parity with the web/IDE UI. There's no explicit claim or evidence that UI-only features like the Interactive Browser takeover, terminal handoff, VPN setup, or environment/emulation configuration are fully API-accessible, and no OpenAPI spec was found (devin-probe-2) to confirm completeness. Missing for 10: explicit vendor statement of API/UI parity, evidence that interactive/browser/VPN/environment features are API-controllable, and independent confirmation of parity.", "evidenceIds": [ "devin-docs-8", "devin-docs-9", "devin-docs-15", "devin-docs-29", "devin-probe-2" ] }, { "productId": "devin", "storyId": "openness-full-export", "verdict": "none", "quality": 0, "confidence": "low", "rationale": "No evidence of any data export feature, open-format export, or account/data portability tooling in Devin's docs; evidence covers task delegation, MCP, CLI, and platform support but nothing about exporting session data, knowledge, or playbooks in open formats for migration away from the product.", "evidenceIds": [] }, { "productId": "devin", "storyId": "openness-open-license", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "Devin is a closed-source commercial SaaS product; no evidence of any open-source license or public source repository is present in the evidence pack, and the axis is applicable since AI tooling could conceivably publish open-source components. missing for 10: any open-license source release, public repo link, or licensing statement.", "evidenceIds": [] }, { "productId": "devin", "storyId": "openness-self-host", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "Devin is exclusively delivered as a cloud SaaS product (with local CLI/terminal front-ends that hand off to cloud sessions); no evidence anywhere in the docs or community discussion describes a self-hostable core engine or on-prem deployment option. Missing for 10: any documentation of self-hosted/on-prem deployment, container/binary distribution of the core agent, or licensing for self-hosting.", "evidenceIds": [ "devin-docs-2", "devin-docs-3", "devin-docs-25" ] }, { "productId": "devin", "storyId": "privacy-data-residency", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "No evidence in the pack mentions data residency, regional storage options, or compliance controls for where session/data is stored; docs cover VPN connectivity, environments, and MCP but nothing about choosing a storage region. Missing for 10: any mention of data residency/region selection, compliance certifications (e.g., SOC2/GDPR data locality), or enterprise data storage controls.", "evidenceIds": [] }, { "productId": "devin", "storyId": "privacy-no-training", "verdict": "none", "quality": 0, "confidence": "low", "rationale": "No evidence in the pack addresses data-training opt-out, privacy controls, or any policy about excluding user data from model training; all docs cover feature capabilities, MCP, CLI, and environment support instead.", "evidenceIds": [] }, { "productId": "devin", "storyId": "privacy-retention-controls", "verdict": "none", "quality": 0, "confidence": "low", "rationale": "No evidence in the pack addresses data retention policies, deletion controls, or privacy configuration options for Devin; the docs cover features, MCP, CLI, and environments but not data governance/retention settings.", "evidenceIds": [] }, { "productId": "devin", "storyId": "privacy-telemetry-optout", "verdict": "none", "quality": 0, "confidence": "low", "rationale": "No evidence pack item mentions telemetry opt-out, analytics settings, or usage-tracking controls for Devin; nothing addresses this privacy axis at all.", "evidenceIds": [] }, { "productId": "devin", "storyId": "query-repo-documentation", "verdict": "full", "quality": 9, "confidence": "high", "rationale": "Devin explicitly documents 'DeepWiki'-style repo indexing and MCP tools that 'explore and query documentation for any GitHub repository (public or private with authentication)', directly matching the story, backed by repo indexing docs enabling Ask Devin/DeepWiki features. Missing for 10: independent hands-on verification of documentation query quality/accuracy beyond vendor docs.", "evidenceIds": [ "devin-docs-16", "devin-docs-22", "devin-docs-29", "devin-docs-15" ] }, { "productId": "devin", "storyId": "repo-readiness-report", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "No evidence of any 'readiness report' or scoring feature that evaluates a repository's suitability for autonomous agents; docs cover onboarding features like AGENTS.md, environment setup, indexing, and knowledge, but nothing that generates a readiness assessment/report artifact for engineering leads.", "evidenceIds": [] }, { "productId": "devin", "storyId": "security-alert-remediation", "verdict": "partial", "quality": 5, "confidence": "low", "rationale": "Devin's 'Review with Auto-Fix' auto-responds to code review comments, fixes flagged bugs and iterates on CI failures, and MCP integrations extend it to security-adjacent tools like Sentry and Datadog, implying it could react to alerts and open a PR fix. However, there is no explicit documentation of a security-scanner (SAST/dependency-alert) validation-and-remediation pipeline or evidence of an actual security-alert-to-PR workflow. Missing for 10: explicit security-scanner (e.g., CodeQL/Dependabot/Snyk) integration, evidence of validating true-positive vs false-positive alerts, and a documented end-to-end 'alert triggers PR' example.", "evidenceIds": [ "devin-docs-20", "devin-docs-21" ] }, { "productId": "devin", "storyId": "self-hosted-agent-deployment", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "Devin is delivered as a cloud-hosted SaaS agent platform; all evidence points to cloud sessions, API access, VPN connectivity into customer networks, and a CLI/terminal client for local interaction, but nothing indicates the core agent infrastructure itself can be self-hosted on-prem, in customer-managed containers, or on customer VMs. Missing for 10: any documentation of a self-hosted/on-prem deployment option, container images for the agent runtime, or VM-based installation of Devin's backend.", "evidenceIds": [ "devin-docs-23", "devin-docs-8", "devin-docs-2" ] }, { "productId": "devin", "storyId": "steer-active-session", "verdict": "partial", "quality": 5, "confidence": "low", "rationale": "Devin's docs describe delegating tasks and then 'taking over in Devin's IDE' or 'jumping in' to help with browsing tasks, implying some interactivity with an active session, but there is no explicit documentation of sending follow-up chat instructions to steer an in-progress session without restarting it. missing for 10: explicit docs on mid-session chat/steering, confirmation that follow-up messages are incorporated live, independent hands-on evidence of this workflow.", "evidenceIds": [ "devin-docs-5", "devin-docs-7", "devin-docs-4", "devin-docs-19" ] }, { "productId": "devin", "storyId": "task-completion-notifications", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "Devin supports Slack/Teams tagging where sessions and results appear in-thread (devin-docs-4, devin-docs-19), and scheduled sessions explicitly support 'notification preferences' (devin-docs-17), implying some completion/status alerts. However there's no dedicated first-party documentation of a generic notify-on-completion or needs-input alert system (e.g., email/push) outside Slack/Teams and scheduling contexts. Missing for 10: explicit documentation of a unified notification mechanism across all session types, and independent/hands-on confirmation that notifications reliably fire when input is needed.", "evidenceIds": [ "devin-docs-4", "devin-docs-19", "devin-docs-17", "devin-docs-7" ] }, { "productId": "devin", "storyId": "tiered-autonomy-levels", "verdict": "partial", "quality": 4, "confidence": "low", "rationale": "Devin documents a permission system for MCP/tool access that can be controlled 'at multiple levels' and features like Auto-Fix, human takeover in the IDE, and interactive browser intervention imply some control over autonomous action, but there is no documented feature specifically framed as configurable 'tiered autonomy levels' for an engineering lead to set organization-wide policies on unattended agent actions. missing for 10: explicit autonomy-tier configuration UI/API, organization-wide policy controls, and confirmation-threshold settings distinct from ad-hoc MCP tool permissions.", "evidenceIds": [ "devin-docs-13", "devin-docs-20", "devin-docs-7", "devin-docs-5" ] }, { "productId": "devin", "storyId": "trigger-agent-on-build-failure", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "Docs explicitly state Devin Review with Auto-Fix can iterate on CI failures and fix flagged bugs, and the Devin API lets you integrate Devin into applications/workflows and create sessions programmatically (including on behalf of users), which supports triggering it from automated pipelines. However, there's no explicit documented example of a CI/CD pipeline (e.g., GitHub Actions/Jenkins) directly invoking Devin on a failing build/test, and no independent/hands-on confirmation of this exact workflow. Missing for 10: explicit CI/CD trigger integration example, independent verification that pipeline-triggered fixes work reliably.", "evidenceIds": [ "devin-docs-20", "devin-docs-8", "devin-docs-9" ] }, { "productId": "devin", "storyId": "visual-context-for-tasks", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "No evidence in the pack mentions attaching screenshots, mockups, or marked-up images to a Devin task, nor any visual-diff/design-to-code workflow; documentation covers ticket delegation, terminal, Slack, MCP, and environment setup but nothing about image/mockup-based task intake.", "evidenceIds": [] }, { "productId": "factory", "storyId": "agentic-agent-docs", "verdict": "full", "quality": 8, "confidence": "high", "rationale": "Factory hosts an actual llms.txt at docs.factory.ai/llms.txt (HTTP 200) confirmed by direct probe, and its docs describe an agent-native platform with structured agent-oriented documentation (droid-cli, agent-readiness, missions) that an agent could be pointed at. missing for 10: independent/hands-on confirmation that an agent successfully consumes llms.txt in practice, and no explicit vendor statement encouraging users to point agents at llms.txt.", "evidenceIds": [ "factory-probe-1", "factory-docs-2", "factory-docs-9" ] }, { "productId": "factory", "storyId": "agentic-ai-insights", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "Factory is an agentic coding platform focused on delegating software development tasks (code diffs, PRs, readiness reports), not a data analytics or BI product that surfaces AI-generated insights/suggestions from a user's own data. This story is a category error for this product type.", "evidenceIds": [] }, { "productId": "factory", "storyId": "agentic-autonomous-automation", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "droid exec supports one-shot autonomous runs with tiered autonomy suited for CI/CD, scripts, and batch automation, and the API supports creating/driving Droid sessions programmatically, which enables background automation setups. However, there's no evidence of a scheduling/trigger system (e.g., cron-like or event-driven automations) or a dedicated 'automations' dashboard for persistent background jobs. Missing for 10: native scheduling/triggers for autonomous background runs, independent hands-on confirmation of unattended long-running automations, and a dedicated automations management UI.", "evidenceIds": [ "factory-docs-6", "factory-docs-7", "factory-docs-13", "factory-docs-11" ] }, { "productId": "factory", "storyId": "agentic-builtin-assistant", "verdict": "full", "quality": 8, "confidence": "high", "rationale": "Factory's core product is built around delegating tasks to Droid agents via App, CLI, or exec, with clear docs on task delegation, custom droids, and session management. missing for 10: independent/hands-on corroboration beyond vendor docs, and quantified reliability of delegated task completion.", "evidenceIds": [ "factory-docs-1", "factory-docs-2", "factory-docs-5", "factory-docs-6", "factory-docs-13" ] }, { "productId": "factory", "storyId": "agentic-headless", "verdict": "full", "quality": 8, "confidence": "high", "rationale": "Factory documents droid exec as a one-shot CLI command explicitly designed for CI/CD pipelines, shell scripts, and batch processing, with tiered autonomy controls for unattended operation. This directly satisfies headless/CI automation. Missing for 10: independent/hands-on third-party verification of CI usage and more detail on exit codes/output formats for pipeline integration.", "evidenceIds": [ "factory-docs-6", "factory-docs-7", "factory-docs-2", "factory-probe-4" ] }, { "productId": "factory", "storyId": "agentic-mcp-client", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "Docs explicitly state Droid CLI can connect MCP tools alongside Jira/Notion/Slack/Linear/PagerDuty integrations, confirming MCP client support. However, there is no detail on setup/configuration process, supported transport types, or independent/hands-on confirmation of MCP tool usage in practice. missing for 10: configuration details for adding MCP servers, examples of MCP tools being invoked, independent verification of functionality.", "evidenceIds": [ "factory-docs-4", "factory-docs-2" ] }, { "productId": "factory", "storyId": "agentic-mcp-server", "verdict": "partial", "quality": 5, "confidence": "medium", "rationale": "Factory is primarily a coding agent (Droid), and factory-docs-4 shows it can connect OUT to MCP tools (client-side), which wouldn't count under the na rule — but factory-probe-3 explicitly indicates an official MCP server is documented at docs.factory.ai/harness/mcp, flipping this to the applicable server-side capability. However, the evidence pack only confirms the page's existence via a probe hit with no detail on what the server exposes or how other agents connect to it. Missing for 10: detailed documentation of the MCP server's tool surface, setup instructions for external agents, and independent confirmation of a working connection.", "evidenceIds": [ "factory-probe-3", "factory-docs-4" ] }, { "productId": "factory", "storyId": "agentic-nl-commands", "verdict": "full", "quality": 8, "confidence": "high", "rationale": "Factory's Droid CLI and App let users delegate tasks, run commands, and manage workflows via natural-language prompts, with bash-mode fallback and slash commands (/droids, /skills, /readiness-report) layered on top of NL interaction, showing deep agentic natural-language operation across CLI, exec, and Missions. missing for 10: independent/hands-on user reports confirming NL command quality and reliability outside vendor docs.", "evidenceIds": [ "factory-docs-2", "factory-docs-3", "factory-docs-5", "factory-docs-6", "factory-docs-11", "factory-probe-4" ] }, { "productId": "factory", "storyId": "agentic-official-cli", "verdict": "full", "quality": 9, "confidence": "high", "rationale": "Factory ships an official Droid CLI with documented quickstart, terminal/editor/Git integration, bash mode, one-shot exec for CI/CD, and slash-command features—clearly an official CLI for AI-native workflows. Missing for 10: independent/hands-on third-party corroboration beyond vendor docs.", "evidenceIds": [ "factory-docs-2", "factory-docs-3", "factory-docs-6", "factory-probe-4" ] }, { "productId": "factory", "storyId": "agentic-public-api", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "Factory documents a public API for creating and driving Droid sessions (factory-docs-13) plus CLI/exec automation paths (droid exec, CI/CD) that give programmatic control, but there's no discoverable OpenAPI/swagger spec (probe found only 404s), suggesting the API reference is thinner or less formally specified than a full REST API story would require. missing for 10: a published OpenAPI/swagger schema, broader API endpoint coverage beyond sessions, independent third-party confirmation of API usage.", "evidenceIds": [ "factory-docs-13", "factory-docs-6", "factory-docs-7", "factory-probe-2" ] }, { "productId": "factory", "storyId": "agentic-scoped-keys", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "No evidence of scoped or least-privilege API credential/token issuance for agents; docs mention API sessions and integrations (Jira, Slack, MCP) but nothing about credential scoping, permission tiers for API keys, or least-privilege access control. Tiered autonomy (factory-docs-7) governs action confirmation, not credential scoping.", "evidenceIds": [] }, { "productId": "factory", "storyId": "agentic-sdks", "verdict": "partial", "quality": 4, "confidence": "low", "rationale": "Factory documents an API reference for creating/driving Droid sessions (factory-docs-13) and MCP/CLI integration points, suggesting some programmatic access, but there is no evidence of an official language SDK (Python/JS/etc.) and the OpenAPI spec probe returned 404s, indicating thin or unpublished API tooling. missing for 10: no named official SDK packages, no OpenAPI/schema availability, no code samples showing SDK usage.", "evidenceIds": [ "factory-docs-13", "factory-probe-2", "factory-probe-3" ] }, { "productId": "factory", "storyId": "agentic-webhooks", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "No evidence of webhook subscription support; docs mention API sessions, MCP tools, and integrations (Jira, Slack, etc.) but nothing about webhooks for event notifications. missing for 10: any webhook documentation, event types, subscription endpoints, or third-party confirmation of webhook support.", "evidenceIds": [] }, { "productId": "factory", "storyId": "ai-automated-test-generation-on-change", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "Docs mention integration with 'tests' as part of Git workflow and bash-mode shell execution, plus readiness-report/readiness-fix commands, but none of this describes an agent autonomously generating and running tests to validate its own code changes before proposing a diff. Missing for 10: explicit documentation of automated test generation, self-validation loop, or evidence droid runs tests as a pre-proposal gate.", "evidenceIds": [ "factory-docs-2", "factory-docs-3", "factory-docs-9", "factory-docs-10" ] }, { "productId": "factory", "storyId": "ai-automatic-model-routing", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "No evidence describes automatic routing of prompts to the most suitable underlying model; docs mention model-agnostic droid workflows, custom droids, and orchestration but nothing about auto-selecting models per task.", "evidenceIds": [] }, { "productId": "factory", "storyId": "ai-issue-triage", "verdict": "none", "quality": 0, "confidence": "low", "rationale": "Factory documents integrations with issue trackers like Jira, Linear, and PagerDuty (factory-docs-4) and generic custom droid/skill automation (factory-docs-5), but there is no evidence of an automated triage workflow that suggests severity or routes issues to an owner. missing for 10: any documented triage feature, severity classification logic, or owner-routing capability.", "evidenceIds": [ "factory-docs-4", "factory-docs-5" ] }, { "productId": "factory", "storyId": "api-interactive-docs", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "Factory does have an API reference section (docs.factory.ai/api-reference/sessions) but there's no evidence of an interactive, runnable-example explorer — the probe for standard OpenAPI/Swagger interactive docs (openapi.json, swagger.json, etc.) returned 404 at all candidate paths, suggesting no live 'try it' interface exists.", "evidenceIds": [ "factory-docs-13", "factory-probe-2" ] }, { "productId": "factory", "storyId": "api-machine-spec", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "Factory has an API reference (sessions endpoints) but probes for standard OpenAPI/swagger spec locations all returned 404, indicating no downloadable machine-readable spec is published; missing for 10: any accessible OpenAPI/swagger JSON file or equivalent machine-readable spec.", "evidenceIds": [ "factory-probe-2", "factory-docs-13" ] }, { "productId": "factory", "storyId": "api-sandbox", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "Factory's evidence covers coding agent workflows (CLI, sessions, MCP, readiness reports) but nothing addresses a sandbox environment for testing separate from production data. No mention of sandbox mode, staging environment, or data isolation guarantees.", "evidenceIds": [] }, { "productId": "factory", "storyId": "api-versioning-policy", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "There is an API reference (sessions) but no evidence of API versioning scheme or a documented deprecation policy; the OpenAPI spec probe even returned 404s across candidate paths, suggesting no formal versioned spec is exposed.", "evidenceIds": [ "factory-docs-13", "factory-probe-2" ] }, { "productId": "factory", "storyId": "approve-plan-before-execution", "verdict": "partial", "quality": 4, "confidence": "low", "rationale": "Factory's tiered autonomy in droid exec implies some operations require manual confirmation before running, and Missions offer 'plan and execute' orchestration, suggesting a planning phase exists, but there is no explicit documentation of a discrete implementation-plan artifact that a developer reviews and approves before any code changes are made — the described workflow (docs-1) instead centers on reviewing the diff/output after changes.\n\nmissing for 10: explicit plan-approval UI/step description, evidence of a pre-execution plan artifact, confirmation that no code is touched until plan is approved.", "evidenceIds": [ "factory-docs-7", "factory-docs-11", "factory-docs-1" ] }, { "productId": "factory", "storyId": "approve-task-contract", "verdict": "partial", "quality": 4, "confidence": "low", "rationale": "Factory documents tiered autonomy that limits what operations run without manual confirmation and diff review before merge, which implies some human-gate mechanics, but there's no explicit evidence of an engineering-lead approving a task's defined scope/contract *before* the agent is permitted to touch the repository — reviews are framed as post-hoc diff review rather than pre-execution scope sign-off. Missing for 10: explicit scope/contract definition step, an approval gate that blocks agent execution until lead sign-off, and evidence this is lead-specific rather than generic autonomy tiering.", "evidenceIds": [ "factory-docs-1", "factory-docs-7" ] }, { "productId": "factory", "storyId": "assign-task-from-ticket", "verdict": "partial", "quality": 4, "confidence": "low", "rationale": "Factory documents integrations with Jira and Linear ('Connect Jira, Notion, Slack, Linear, PagerDuty, and MCP tools to keep development synchronized with team systems'), implying tickets can feed into agent workflows, but there is no explicit documentation of assigning/triggering a droid task directly from an issue or ticket UI. missing for 10: explicit workflow/screenshot showing task creation from a Jira/Linear ticket, confirmation that ticket assignment auto-triggers an agent session, independent corroboration.", "evidenceIds": [ "factory-docs-4" ] }, { "productId": "factory", "storyId": "auto-approve-all-actions", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "Factory's docs confirm 'tiered autonomy' in droid exec that controls what operations run without manual confirmation, implying a configurable auto-approve mode for CI/batch use, but there's no explicit documentation of a full 'auto-approve all actions' toggle or its exact configuration options/flags. missing for 10: explicit config syntax/flag for full auto-approval, independent confirmation of behavior, coverage of auto-approve in interactive (non-exec) sessions.", "evidenceIds": [ "factory-docs-6", "factory-docs-7" ] }, { "productId": "factory", "storyId": "auto-fix-readiness-gaps", "verdict": "full", "quality": 8, "confidence": "medium", "rationale": "Factory explicitly documents a /readiness-fix slash command that automatically fixes failing agent-readiness criteria, usable from the Factory App or Droid CLI, complementing the /readiness-report diagnostic command. This directly matches the story's ask, though only first-party docs support it with no independent/hands-on corroboration. Missing for 10: independent or hands-on verification that /readiness-fix reliably resolves criteria, and detail on which criteria types it can/cannot auto-fix.", "evidenceIds": [ "factory-docs-9", "factory-docs-10" ] }, { "productId": "factory", "storyId": "auto-open-pr-on-completion", "verdict": "partial", "quality": 3, "confidence": "low", "rationale": "Docs show git workflow integration (droid-cli git workflow, delegate task then review diff and merge, droid exec for CI/CD pipelines) which implies PR-related automation, but there is no explicit documentation of a feature to automatically open a pull request when a task completes. Missing for 10: explicit config/flag for auto-PR creation, first-party example of a droid opening a PR on completion, independent/hands-on confirmation.", "evidenceIds": [ "factory-docs-1", "factory-docs-6", "factory-docs-2" ] }, { "productId": "factory", "storyId": "automated-pr-review", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "Evidence shows Factory's droid CLI/exec can review diffs, be triggered in CI/CD, and connect to Git workflows, but nothing in the pack describes automatic PR review with AI-generated inline comments posted to pull requests. Missing for 10: any documentation of automated PR-triggered review, inline comment generation on PRs, or GitHub/GitLab PR integration specifics.", "evidenceIds": [ "factory-docs-1", "factory-docs-6", "factory-docs-2" ] }, { "productId": "factory", "storyId": "automatic-environment-setup", "verdict": "partial", "quality": 4, "confidence": "low", "rationale": "Factory's Droid CLI/exec can run shell commands autonomously (bash mode, tiered autonomy for unconfirmed operations) and operates within a repo's terminal/Git workflow, which implies it could run clone/install commands, but no evidence explicitly describes an agent autonomously cloning a repo or bootstrapping its own dev environment from scratch. missing for 10: explicit documentation of automated repo cloning, dependency installation, or environment provisioning as a first-class capability, and any hands-on example showing this workflow.", "evidenceIds": [ "factory-docs-2", "factory-docs-3", "factory-docs-6", "factory-docs-7" ] }, { "productId": "factory", "storyId": "automation-bulk-operations", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "droid exec is explicitly documented as a one-shot command 'ideal for CI/CD pipelines, shell scripts, and batch processing,' and the API lets users programmatically create/drive many Droid sessions, both enabling bulk automation across items. However, there's no explicit example, docs, or tooling demonstrating a native 'run across many files/repos/tickets at once' bulk operation feature — it relies on the user scripting droid exec in loops rather than a built-in bulk-operation UI/command. Missing for 10: a dedicated bulk-operation command or documented multi-item batch workflow example, and independent/hands-on evidence of it working at scale.", "evidenceIds": [ "factory-docs-6", "factory-docs-13", "factory-docs-11" ] }, { "productId": "factory", "storyId": "automation-rules-engine", "verdict": "partial", "quality": 4, "confidence": "low", "rationale": "Factory's droid exec is explicitly designed to run as a one-shot command in CI/CD pipelines, shell scripts, and batch jobs, which implies it can be wired to external events (e.g., git hooks, CI triggers) with tiered autonomy controlling what runs unattended — but this is an execution mode, not a native rule/trigger definition system where a user declares 'on event X, do Y'. Missing for 10: explicit rule/trigger authoring UI or config, built-in event listeners (e.g., webhook triggers, issue-created triggers), and any documented automation-rules engine beyond CI invocation.", "evidenceIds": [ "factory-docs-6", "factory-docs-7", "factory-docs-12" ] }, { "productId": "factory", "storyId": "automation-scheduled-jobs", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "Evidence shows droid exec for CI/CD one-shot automation and API session management, but no documentation of scheduling or recurring/cron-style job execution exists anywhere in the pack. missing for 10: any mention of scheduling, cron, recurring triggers, or timed/repeated workflow execution.", "evidenceIds": [] }, { "productId": "factory", "storyId": "automation-versioned-workflows", "verdict": "partial", "quality": 5, "confidence": "low", "rationale": "Factory supports reviewing diffs and merging via git workflow (factory-docs-1, factory-docs-2), and packages automations as reusable Custom Droids/Skills (factory-docs-5), which implies some git-based versioning, but there is no explicit documentation of a version-history feature for Droids/Skills nor an explicit rollback mechanism for automations themselves. missing for 10: explicit versioning UI/history for Skills/Droids, dedicated rollback command or feature distinct from generic git revert.", "evidenceIds": [ "factory-docs-1", "factory-docs-2", "factory-docs-5" ] }, { "productId": "factory", "storyId": "autonomous-bug-fixing", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "Factory's Droid CLI/exec and delegated task workflow support autonomous code changes (diff review and merge) and integrations like Jira/Linear/PagerDuty for bug tracking, plus tiered autonomy for unattended operation, which together plausibly support autonomous bug diagnosis and fixing. However, no evidence explicitly describes an end-to-end 'diagnose a reported bug from ticket to verified fix' workflow or hands-on validation of bug-fixing accuracy. Missing for 10: explicit bug-diagnosis workflow documentation, independent/hands-on evidence of successful autonomous bug fixes, and details on root-cause diagnosis capability.", "evidenceIds": [ "factory-docs-1", "factory-docs-6", "factory-docs-7", "factory-docs-4", "factory-docs-10" ] }, { "productId": "factory", "storyId": "bring-your-own-model", "verdict": "none", "quality": 0, "confidence": "low", "rationale": "No evidence pack item mentions bringing your own LLM, custom API keys, or model selection/configuration options; all docs focus on CLI, integrations, and workflow features.", "evidenceIds": [] }, { "productId": "factory", "storyId": "chat-with-agent-in-slack", "verdict": "partial", "quality": 3, "confidence": "low", "rationale": "Factory's docs mention connecting Slack as an integration to 'keep development synchronized with team systems,' implying some chat-based interaction, but there is no explicit evidence of an @-mention/tagging mechanism in a chat thread to discuss or delegate a specific bug/task to the agent. Missing for 10: explicit Slack @droid tagging workflow, thread-based task delegation UI, and confirmation that discussion happens inline in chat rather than just triggering external actions.", "evidenceIds": [ "factory-docs-4" ] }, { "productId": "factory", "storyId": "ci-failure-auto-fix", "verdict": "partial", "quality": 4, "confidence": "low", "rationale": "Factory's droid exec is explicitly designed to run in CI/CD pipelines as a one-shot task with tiered autonomy, and Droid CLI integrates with Git workflows, suggesting the building blocks exist for automating CI fixes. However, there is no direct evidence of a dedicated feature that detects a failed CI workflow, diagnoses the failure, and automatically opens a proposed pull request end-to-end.\nmissing for 10: explicit CI-failure detection/trigger integration, automatic diagnosis-to-PR workflow documentation, and any hands-on/independent proof of this specific use case.", "evidenceIds": [ "factory-docs-6", "factory-docs-7", "factory-docs-2" ] }, { "productId": "factory", "storyId": "codebase-context-file", "verdict": "none", "quality": 0, "confidence": "low", "rationale": "The evidence pack does not mention any context file mechanism (e.g., AGENTS.md, .factory config, or similar) for describing codebase conventions to guide agent behavior; it covers CLI usage, integrations, readiness reports, and missions but nothing about persistent repo-convention context files.", "evidenceIds": [] }, { "productId": "factory", "storyId": "concurrent-task-execution", "verdict": "partial", "quality": 7, "confidence": "medium", "rationale": "Factory provides several building blocks for scaling agent work: droid exec is designed for one-shot, CI/CD, and batch processing use cases, the Sessions API lets you create and manage multiple Droid sessions programmatically, and Missions support orchestrating large multi-feature projects with structured coordination. Together these imply the ability to run many concurrent tasks, but no evidence explicitly states a documented concurrency limit, dashboard for tracking many simultaneous droids, or independent case study proving throughput scaling. Missing for 10: explicit concurrency/parallelism guarantees or limits, a multi-task monitoring UI description, and independent/hands-on validation of running many tasks simultaneously.", "evidenceIds": [ "factory-docs-6", "factory-docs-11", "factory-docs-13", "factory-docs-12" ] }, { "productId": "factory", "storyId": "connect-github-repo", "verdict": "partial", "quality": 5, "confidence": "medium", "rationale": "Docs indicate Factory works within Git workflows (delegate tasks, review diffs, merge from App/terminal) and can run in CI/CD via droid exec, implying repo access and PR-opening capability, but there is no explicit documentation describing connecting/authorizing a GitHub repository or an explicit PR-creation feature. Missing for 10: explicit GitHub repo connection/auth flow documentation, explicit 'open pull request' feature description, and independent/hands-on confirmation of PR creation.", "evidenceIds": [ "factory-docs-1", "factory-docs-2", "factory-docs-6" ] }, { "productId": "factory", "storyId": "daily-task-quota-limits", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "No evidence of plan-based daily task limits, concurrency caps, or admin controls for managing such limits; docs cover CLI, sessions, missions, and integrations but nothing on usage/plan limit visibility or management.", "evidenceIds": [] }, { "productId": "factory", "storyId": "delegate-sessions-for-teammates", "verdict": "none", "quality": 0, "confidence": "low", "rationale": "The API reference (factory-docs-13) shows session creation/management exists, but nothing in the evidence indicates an org-admin or lead can create/manage sessions on behalf of other specific users within an organization. missing for 10: evidence of org-level user impersonation, admin controls for delegating sessions to teammates, or role-based session ownership across users.", "evidenceIds": [ "factory-docs-13" ] }, { "productId": "factory", "storyId": "describe-feature-in-plain-language", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "Factory supports delegating tasks described in plain language (via droid CLI, App, or Missions) which get turned into scoped, executable work with diffs to review and merge, and readiness tooling helps scope repo work automatically. However, there's no explicit documented workflow for turning a raw bug/feature description into a formal 'scoped implementation task' spec artifact (e.g., structured ticket/spec generation before execution) distinct from just running the agent. missing for 10: explicit intent-to-spec artifact generation/preview step, independent/hands-on evidence of accurate scoping from vague input, and detail on how ambiguous requests are clarified before execution.", "evidenceIds": [ "factory-docs-1", "factory-docs-5", "factory-docs-8", "factory-docs-9", "factory-docs-11" ] }, { "productId": "factory", "storyId": "design-to-code-handoff", "verdict": "full", "quality": 7, "confidence": "medium", "rationale": "Factory's agent-readiness docs explicitly describe the exact scenario: \"A designer shares a mockup, and the system implements it without handoffs\" (factory-docs-8), backed by delegation/review/merge workflow (factory-docs-1) and readiness tooling (factory-docs-9, factory-docs-10) that lets non-engineers trigger and validate implementation. Missing for 10: independent or hands-on corroboration of a PM-specific end-to-end mockup-to-merge case study, and detail on how a non-technical PM reviews/approves the diff without engineering involvement.", "evidenceIds": [ "factory-docs-8", "factory-docs-1", "factory-docs-9", "factory-docs-10" ] }, { "productId": "factory", "storyId": "diff-review-before-pr", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "Docs explicitly describe the delegate-review-merge workflow ('Delegate a task, review the diff, and merge from the App or your terminal') and tiered autonomy controls that gate what runs without confirmation, supporting a review-before-PR gate. However, there is no detailed documentation of the diff review UI itself, approval mechanics, or independent/hands-on confirmation of this exact flow. Missing for 10: dedicated diff-review UI documentation, explicit approval-step mechanics, and independent verification of the review-before-PR gate.", "evidenceIds": [ "factory-docs-1", "factory-docs-7" ] }, { "productId": "factory", "storyId": "end-to-end-feature-implementation", "verdict": "partial", "quality": 7, "confidence": "medium", "rationale": "Factory's docs show agents can be delegated end-to-end feature tasks (delegate, diff review, merge), run in terminal/CI with test execution and git workflow, and orchestrate multi-feature projects via Missions, but no explicit first-party evidence confirms the agent autonomously writes tests as part of implementation. missing for 10: explicit documentation of test-writing behavior, independent/hands-on verification of end-to-end feature delivery.", "evidenceIds": [ "factory-docs-1", "factory-docs-2", "factory-docs-6", "factory-docs-11" ] }, { "productId": "factory", "storyId": "enterprise-sso-licensing", "verdict": "none", "quality": 0, "confidence": "low", "rationale": "No evidence pack items mention enterprise licensing, SSO, or commercial support offerings; documentation only covers product features like Droid CLI, MCP integration, and readiness reports. Missing for 10: any mention of SSO/SAML, enterprise tier, commercial support SLAs, or org-wide licensing terms.", "evidenceIds": [] }, { "productId": "factory", "storyId": "feedback-to-task-conversion", "verdict": "none", "quality": 0, "confidence": "low", "rationale": "Factory documents task delegation, Missions planning, and integrations with Jira/Linear/Notion, but nothing in the evidence describes ingesting user feedback submissions and outputting a structured task with proposed scope — this specific PM workflow is not documented.", "evidenceIds": [] }, { "productId": "factory", "storyId": "headless-ci-execution", "verdict": "full", "quality": 9, "confidence": "high", "rationale": "Factory explicitly documents droid exec as a one-shot headless command designed for CI/CD pipelines, shell scripts, and batch processing, with tiered autonomy controls for unattended operation. Missing for 10: independent/hands-on confirmation of real-world CI pipeline usage beyond first-party docs.", "evidenceIds": [ "factory-docs-6", "factory-docs-7", "factory-docs-2" ] }, { "productId": "factory", "storyId": "isolated-sandbox-execution", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "Evidence describes tiered autonomy, bash mode, and CI/CD execution (droid exec) but never mentions an isolated sandbox environment for code execution or dependency installation; no container/VM isolation is documented.", "evidenceIds": [] }, { "productId": "factory", "storyId": "issue-tracker-sync", "verdict": "partial", "quality": 5, "confidence": "medium", "rationale": "Docs confirm Jira and Linear integration explicitly (factory-docs-4), but ClickUp and Monday.com are not mentioned anywhere in the evidence, and there's no detail on ticket management workflows (creating/updating tickets) beyond 'connecting' systems to sync development. Missing for 10: ClickUp integration, Monday.com integration, concrete ticket-management/CRUD workflows via these connectors, independent corroboration.", "evidenceIds": [ "factory-docs-4" ] }, { "productId": "factory", "storyId": "live-agent-activity-monitoring", "verdict": "partial", "quality": 5, "confidence": "low", "rationale": "Factory's session API lets you create and manage Droid sessions—including lifecycle, settings, and messages—implying some ability to track a running agent's state, and the App/CLI let you review diffs as work progresses. However, there is no explicit documentation of a live status dashboard, streaming logs, or real-time progress view of an in-flight agent. Missing for 10: dedicated real-time monitoring UI/stream, explicit 'live status' feature documentation, independent confirmation of live tracking.", "evidenceIds": [ "factory-docs-13", "factory-docs-1", "factory-docs-2" ] }, { "productId": "factory", "storyId": "managed-cloud-execution", "verdict": "full", "quality": 7, "confidence": "medium", "rationale": "Factory's docs describe a hosted 'Factory App' (web/desktop) where users delegate tasks and review diffs without local setup, plus an API for creating and managing Droid sessions server-side, indicating a managed backend rather than self-hosted infra. Missing for 10: independent/hands-on confirmation of the cloud service's scalability and infrastructure details beyond first-party docs.", "evidenceIds": [ "factory-docs-1", "factory-docs-9", "factory-docs-10", "factory-docs-13" ] }, { "productId": "factory", "storyId": "manual-model-override", "verdict": "none", "quality": 0, "confidence": "low", "rationale": "No evidence in the pack mentions model selection, automatic model routing, or the ability to choose a specific model over an automatic default; the docs cover CLI usage, integrations, missions, and readiness reports but nothing about model choice controls.", "evidenceIds": [] }, { "productId": "factory", "storyId": "mid-task-takeover", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "Factory explicitly supports multi-surface access (App/web, terminal via Droid CLI, browser) and delegating tasks, reviewing diffs, and merging from any of these surfaces, which implies continuity across surfaces. However, there's no explicit documentation of a 'takeover mid-task' handoff flow (e.g., pausing an in-progress session in one surface and resuming/redirecting it live in another) — the evidence shows task delegation and review/merge but not explicit interactive takeover semantics. Missing for 10: explicit documentation of resuming/redirecting an in-progress session across surfaces, and independent/hands-on confirmation of this handoff working smoothly.", "evidenceIds": [ "factory-docs-1", "factory-docs-2", "factory-docs-13" ] }, { "productId": "factory", "storyId": "mobile-approval-of-decisions", "verdict": "partial", "quality": 3, "confidence": "low", "rationale": "Docs mention delegating tasks, reviewing diffs, and merging 'from the App or your terminal' and tiered autonomy that gates operations needing confirmation, implying some human-in-the-loop review outside the terminal, but there is no explicit evidence of a mobile/phone app or of approving in-flight agent decisions remotely while agents keep working. Missing for 10: explicit mobile app/phone interface, evidence of asynchronous approval while agent continues running, and independent confirmation of this workflow.", "evidenceIds": [ "factory-docs-1", "factory-docs-7" ] }, { "productId": "factory", "storyId": "one-click-repo-access", "verdict": "none", "quality": 0, "confidence": "low", "rationale": "Evidence shows Factory integrates with Git workflows, CLI, and external tools like Jira/Slack/MCP, but there is no mention of a one-click GitHub/repo install or simplified OAuth-based repo authorization flow. Setup appears to require CLI installation and configuration rather than a one-click grant.", "evidenceIds": [] }, { "productId": "factory", "storyId": "openness-api-parity", "verdict": "partial", "quality": 5, "confidence": "medium", "rationale": "Factory documents a real API (session creation/lifecycle) alongside its CLI and UI, showing some functional overlap, but there's no OpenAPI spec (404s on all candidate URLs) and no evidence that UI-exclusive features like Missions, Software Factory's coverage map, or readiness-report/fix slash commands are exposed via the API. missing for 10: documented API parity for Missions, Software Factory, and readiness commands; a public OpenAPI/schema confirming full surface coverage.", "evidenceIds": [ "factory-docs-13", "factory-docs-11", "factory-docs-12", "factory-docs-9", "factory-docs-10", "factory-probe-2" ] }, { "productId": "factory", "storyId": "openness-full-export", "verdict": "none", "quality": 0, "confidence": "low", "rationale": "No evidence addresses data export or portability in open formats, nor any account-deletion/data-takeout mechanism; the docs focus on session management, CLI, and integrations, not exporting user data to leave the platform.", "evidenceIds": [] }, { "productId": "factory", "storyId": "openness-open-license", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "No evidence of an open-source license or publicly available source code for Factory/Droid; all evidence points to closed docs and a proprietary CLI/platform. missing for 10: any license file, GitHub repo, or open-source statement covering the product's source code.", "evidenceIds": [] }, { "productId": "factory", "storyId": "openness-self-host", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "Factory is presented as a cloud-hosted platform (Factory App, Droid CLI connecting to hosted services, API sessions) with no evidence of a self-hostable core server or on-prem deployment option anywhere in the docs or probes.", "evidenceIds": [] }, { "productId": "factory", "storyId": "privacy-data-residency", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "No evidence in the pack addresses data residency, regional storage options, or compliance/data-location controls; all evidence covers agent workflows, CLI, and integrations unrelated to data storage location.", "evidenceIds": [] }, { "productId": "factory", "storyId": "privacy-no-training", "verdict": "none", "quality": 0, "confidence": "low", "rationale": "No evidence in the pack addresses data usage/training opt-out, privacy policy, or data retention controls; the docs focus entirely on product features like CLI, missions, and integrations.", "evidenceIds": [] }, { "productId": "factory", "storyId": "privacy-retention-controls", "verdict": "none", "quality": 0, "confidence": "low", "rationale": "No evidence pack items address data retention policies, deletion controls, or privacy settings for user data; all citations relate to product features like CLI, MCP integrations, and agent workflows rather than privacy posture.", "evidenceIds": [] }, { "productId": "factory", "storyId": "privacy-telemetry-optout", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "No evidence in the pack mentions telemetry, usage tracking, data collection, or any opt-out/privacy settings for Factory or the Droid CLI.", "evidenceIds": [] }, { "productId": "factory", "storyId": "query-repo-documentation", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "The evidence pack covers Factory's CLI, agent-readiness reports, missions, and integrations, but nothing describes generating or querying documentation for a repository's codebase (e.g., an auto-generated repo wiki/docs feature). This is a plausible capability for a repo-integrated dev tool, so the axis applies, but no supporting evidence exists.", "evidenceIds": [] }, { "productId": "factory", "storyId": "repo-readiness-report", "verdict": "full", "quality": 8, "confidence": "medium", "rationale": "Factory explicitly documents a `/readiness-report` slash command (in App or Droid CLI) that evaluates a repository's agent-readiness level, plus a companion `/readiness-fix` command to auto-remediate failing criteria, directly matching the engineering-lead's story. Missing for 10: independent/hands-on validation of report contents and no sample report output shown.", "evidenceIds": [ "factory-docs-9", "factory-docs-10", "factory-docs-8" ] }, { "productId": "factory", "storyId": "security-alert-remediation", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "Evidence covers task delegation, CLI automation, readiness reports/fixes, and CI/CD execution, but nothing describes ingesting security alerts (e.g., from a scanner like Dependabot/Snyk) and automatically validating and remediating them via an opened PR. No security-alert-specific workflow or integration is documented.", "evidenceIds": [] }, { "productId": "factory", "storyId": "self-hosted-agent-deployment", "verdict": "none", "quality": 0, "confidence": "low", "rationale": "No evidence describes self-hosting Factory's agent infrastructure locally, in containers, or on customer-owned VMs; all evidence points to Factory's own cloud-hosted App, CLI, and API rather than a deployable/self-hosted backend.", "evidenceIds": [] }, { "productId": "factory", "storyId": "steer-active-session", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "Factory's API reference explicitly supports creating and driving Droid sessions including sending messages within an active session (factory-docs-13), and the CLI is interactive by nature, implying follow-up prompts are possible without restarting. However, there is no explicit documentation describing mid-task interruption/steering while the agent is actively executing a long-running task (e.g., Missions or droid exec), so the steering-while-running behavior is not directly evidenced. Missing for 10: explicit docs on interrupting/redirecting an in-progress autonomous run, and independent/hands-on confirmation that follow-up messages actually steer ongoing work rather than queue for the next turn.", "evidenceIds": [ "factory-docs-13", "factory-docs-2", "factory-docs-11" ] }, { "productId": "factory", "storyId": "task-completion-notifications", "verdict": "partial", "quality": 4, "confidence": "low", "rationale": "Factory integrates with Slack and PagerDuty and supports tiered autonomy that pauses for manual confirmation, implying some mechanism for alerting developers when input is needed, but there's no explicit documentation of a notification feature for task completion or input requests. missing for 10: explicit notification/alert documentation, evidence of completion pings, confirmation of Slack/PagerDuty being used specifically for task-status alerts.", "evidenceIds": [ "factory-docs-4", "factory-docs-7", "factory-docs-1" ] }, { "productId": "factory", "storyId": "tiered-autonomy-levels", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "Factory explicitly documents 'tiered autonomy' in droid exec to control which operations run without manual confirmation, directly matching the story, but this is scoped to the CI/CD-oriented droid exec mode rather than a broader, configurable set of autonomy tiers across all agent surfaces. missing for 10: detail on specific tier levels/permissions, configuration UI or granular controls, and evidence this applies uniformly across App/CLI sessions, not just droid exec.", "evidenceIds": [ "factory-docs-6", "factory-docs-7" ] }, { "productId": "factory", "storyId": "trigger-agent-on-build-failure", "verdict": "full", "quality": 8, "confidence": "high", "rationale": "Factory explicitly documents `droid exec` as a one-shot CLI command designed for CI/CD pipelines, shell scripts, and batch processing, with tiered autonomy controls for unattended operation — directly enabling triggering an agent from CI to fix builds/tests. Missing for 10: a concrete worked example of a CI pipeline invoking droid exec on a failing test/build, and independent/hands-on corroboration beyond vendor docs.", "evidenceIds": [ "factory-docs-6", "factory-docs-7", "factory-probe-4" ] }, { "productId": "factory", "storyId": "visual-context-for-tasks", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "Evidence only mentions a designer sharing a mockup conceptually (factory-docs-8), with no documented capability to attach an image/screenshot with markup annotations to a task or session, nor any multimodal image-input feature described.", "evidenceIds": [ "factory-docs-8" ] }, { "productId": "foreloop", "storyId": "agentic-agent-docs", "verdict": "none", "quality": 0, "confidence": "low", "rationale": "No evidence pack item mentions llms.txt or an agent-oriented docs endpoint that agents can be pointed at; docs reference CLI, MCP endpoints, and skills but not an llms.txt-style discoverable doc file.", "evidenceIds": [] }, { "productId": "foreloop", "storyId": "agentic-ai-insights", "verdict": "partial", "quality": 5, "confidence": "medium", "rationale": "Foreloop's agent reads submitted feedback/reports and proposes intentions and tasks from that data (foreloop-docs-12, foreloop-docs-4), which is a form of AI-generated suggestion from collected data, but this is limited to feedback-to-task triage rather than broader analytics or insight generation across product data. Missing for 10: evidence of AI summarizing trends/patterns across historical data, dashboards or reports surfacing insights beyond individual feedback items, and independent corroboration of insight quality.", "evidenceIds": [ "foreloop-docs-12", "foreloop-docs-4", "foreloop-docs-21" ] }, { "productId": "foreloop", "storyId": "agentic-autonomous-automation", "verdict": "partial", "quality": 5, "confidence": "medium", "rationale": "Foreloop supports background-style automation via its CLI (usable from cron or other schedulers) and agents that autonomously claim and work tasks in a repo, but it explicitly gates all agent actions behind a mandatory human approval step ('Nothing runs until a person approves a contract'), meaning true unattended autonomy is limited. Missing for 10: concrete documentation of scheduled/recurring automation setups, evidence of fully unattended runs without approval gating, and details on monitoring/alerting for background tasks.", "evidenceIds": [ "foreloop-docs-16", "foreloop-docs-7", "foreloop-docs-3", "foreloop-docs-20" ] }, { "productId": "foreloop", "storyId": "agentic-builtin-assistant", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "Foreloop's documented model relies on external agents (Claude Code or Codex) running on the user's own machine and connecting via MCP to claim tasks — there is no built-in AI assistant shipped inside Foreloop itself that a user delegates tasks to directly.", "evidenceIds": [ "foreloop-docs-3", "foreloop-docs-5", "foreloop-docs-19" ] }, { "productId": "foreloop", "storyId": "agentic-headless", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "Foreloop ships a global CLI with a stable, additive `--json` output on every command, and docs explicitly state it can be run 'from anything that can run one, including coding agents and cron,' which is direct evidence of headless/CI-style automation support. However, there's no CI-specific documentation (e.g. GitHub Actions example, env-based auth for non-interactive contexts) and the approval-gate design ('nothing runs until a person approves a contract') implies a human-in-the-loop checkpoint that could block fully unattended pipelines. missing for 10: CI/pipeline-specific setup docs or examples, confirmation that approval gates can be bypassed or pre-authorized for headless runs, independent/hands-on verification of CI usage.", "evidenceIds": [ "foreloop-docs-9", "foreloop-docs-16", "foreloop-docs-18", "foreloop-docs-7", "foreloop-probe-1" ] }, { "productId": "foreloop", "storyId": "agentic-mcp-client", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "All MCP-related evidence describes Foreloop exposing its own MCP server endpoint (/api/app/public/v1/mcp) so that external MCP clients (agents) can call into it — this is the reverse of the story, which asks whether the user can plug external MCP servers into Foreloop so it can use their tools. No evidence shows Foreloop acting as an MCP client that consumes other servers' tools.", "evidenceIds": [ "foreloop-docs-5", "foreloop-docs-19" ] }, { "productId": "foreloop", "storyId": "agentic-mcp-server", "verdict": "full", "quality": 8, "confidence": "medium", "rationale": "Foreloop documents a first-party MCP server endpoint (/api/app/public/v1/mcp over streamable HTTP) that any MCP client can connect to with just a URL and API key, explicitly enabling agents to claim tasks and work in repos. This is first-party documentation only, with no independent/hands-on corroboration of the MCP connection working in practice. Missing for 10: independent verification that an external MCP client successfully connects and operates against this endpoint.", "evidenceIds": [ "foreloop-docs-5", "foreloop-docs-19", "foreloop-docs-3" ] }, { "productId": "foreloop", "storyId": "agentic-nl-commands", "verdict": "partial", "quality": 5, "confidence": "low", "rationale": "Foreloop lets users describe problems in plain language (e.g., \"Users can't tell which invoices are overdue\") to kick off an intention that an agent turns into tasks, showing some natural-language entry point, but its actual operating surface is a structured CLI (with --json flags) and an MCP interface for agents, not a general natural-language command interface for the product as a whole. Missing for 10: evidence of NL commands controlling arbitrary product actions (not just initial feedback/intention creation), and any hands-on/independent confirmation of this NL flow working.", "evidenceIds": [ "foreloop-docs-4", "foreloop-docs-12", "foreloop-docs-16" ] }, { "productId": "foreloop", "storyId": "agentic-official-cli", "verdict": "full", "quality": 8, "confidence": "medium", "rationale": "Foreloop ships an official globally-installable CLI (npm i -g foreloop) that manages projects, loops, intentions, stories, and tasks, supports scripting via `--json` with a stable additive schema, and is explicitly designed to be run 'from anything that can run one, including coding agents and cron,' making it clearly AI-native/agentic-workflow friendly. missing for 10: independent/hands-on corroboration beyond vendor docs, and more detail on full command surface.", "evidenceIds": [ "foreloop-docs-1", "foreloop-docs-9", "foreloop-docs-16", "foreloop-docs-18", "foreloop-probe-1" ] }, { "productId": "foreloop", "storyId": "agentic-public-api", "verdict": "full", "quality": 8, "confidence": "medium", "rationale": "Foreloop documents a public MCP endpoint (/api/app/public/v1/mcp over streamable HTTP with just a URL and API key) plus a CLI with stable, additive `--json` output usable from any scriptable context including cron and coding agents, giving AI-native users two documented programmatic surfaces. missing for 10: no independent/third-party corroboration of the API's stability or real-world agent integration beyond first-party docs, and no formal OpenAPI/REST reference is shown alongside the MCP/CLI docs.", "evidenceIds": [ "foreloop-docs-5", "foreloop-docs-19", "foreloop-docs-9", "foreloop-docs-16", "foreloop-docs-3", "foreloop-probe-1" ] }, { "productId": "foreloop", "storyId": "agentic-scoped-keys", "verdict": "partial", "quality": 3, "confidence": "low", "rationale": "The docs mention a 'write-only' API key type (flpk_YOUR_WRITE_ONLY_KEY) which implies some credential scoping, and the MCP endpoint for agents requires 'an API key', but there is no explicit documentation of issuing least-privilege or role-scoped credentials specifically for agent access to repos/tasks. missing for 10: explicit agent-scoped API key creation/management flow, documented permission levels (read/write/task-only), and evidence of per-agent credential rotation or revocation.", "evidenceIds": [ "foreloop-docs-6", "foreloop-docs-19", "foreloop-docs-5" ] }, { "productId": "foreloop", "storyId": "agentic-sdks", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "Foreloop exposes an official CLI, a JS feedback SDK (initForeloopFeedback), and an MCP endpoint for agent clients, giving AI-native builders official integration surfaces beyond just docs. However, this is all first-party vendor documentation with no independent corroboration or SDK versioning/language-coverage details (e.g. only JS snippet shown, no Python/other language SDKs). Missing for 10: independent/hands-on verification of SDK usage, multi-language SDK coverage, and evidence of SDK stability/versioning beyond the JS snippet.", "evidenceIds": [ "foreloop-docs-5", "foreloop-docs-6", "foreloop-docs-19", "foreloop-docs-9", "foreloop-probe-1" ] }, { "productId": "foreloop", "storyId": "agentic-webhooks", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "The evidence pack documents MCP connectivity, CLI, and feedback widget integrations, but nowhere mentions webhooks or event subscriptions that an AI-native user could subscribe to; no push/callback mechanism is described.", "evidenceIds": [] }, { "productId": "foreloop", "storyId": "ai-automated-test-generation-on-change", "verdict": "none", "quality": 0, "confidence": "low", "rationale": "No evidence describes Foreloop's agents generating or running tests to validate code changes before proposing them; the docs describe task claiming, PR opening, and human approval gates but never mention automated test generation/execution as a validation step.", "evidenceIds": [] }, { "productId": "foreloop", "storyId": "ai-automatic-model-routing", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "Foreloop is a task-management/agent-orchestration layer that hands work to whatever coding agent (Claude Code, Codex) the developer runs locally — it does not itself route prompts across multiple underlying LLMs. Automatic model routing per task is a wrong axis for this product's category (it's a task/PM layer, not a multi-model gateway).", "evidenceIds": [ "foreloop-docs-3", "foreloop-docs-2", "foreloop-docs-15" ] }, { "productId": "foreloop", "storyId": "ai-issue-triage", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "Foreloop's docs describe turning user feedback into intentions/tasks that agents pick up and claim (foreloop-docs-12, foreloop-docs-4), but there is no evidence of automatic severity classification or routing to a specific human owner — tasks are self-claimed by agents rather than triaged and assigned. missing for 10: severity scoring, owner/routing logic, any triage workflow evidence.", "evidenceIds": [ "foreloop-docs-4", "foreloop-docs-12" ] }, { "productId": "foreloop", "storyId": "api-interactive-docs", "verdict": "none", "quality": 0, "confidence": "low", "rationale": "The evidence pack shows CLI docs, MCP endpoint info, and install snippets, but nowhere describes an interactive API reference (e.g., Swagger/OpenAPI explorer, try-it-now console) with runnable examples.", "evidenceIds": [] }, { "productId": "foreloop", "storyId": "api-machine-spec", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "Evidence shows Foreloop exposes an MCP endpoint and CLI with --json output, but there is no mention of a downloadable OpenAPI spec or any machine-readable API specification document for its REST/API surface.", "evidenceIds": [] }, { "productId": "foreloop", "storyId": "api-sandbox", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "Foreloop is a project/task-management and agent-orchestration tool for coding work (tasks, PRs, feedback widgets); it has no concept of a sandbox testing environment vs production data. This is a category error for its product type — the axis of 'sandbox vs production data testing' doesn't apply.", "evidenceIds": [] }, { "productId": "foreloop", "storyId": "api-versioning-policy", "verdict": "partial", "quality": 3, "confidence": "low", "rationale": "The docs mention a stability guarantee for CLI `--json` output ('has a stable shape. Additive changes only') and the MCP/API endpoint path includes a 'v1' segment, implying some versioning discipline, but there is no explicit documented deprecation policy, versioning scheme, or migration guidance for the API itself. missing for 10: explicit API version numbering scheme, documented deprecation timeline/process, changelog or migration guides, evidence beyond CLI JSON stability.", "evidenceIds": [ "foreloop-docs-9", "foreloop-docs-18", "foreloop-docs-5", "foreloop-docs-19" ] }, { "productId": "foreloop", "storyId": "approve-plan-before-execution", "verdict": "full", "quality": 8, "confidence": "medium", "rationale": "Docs explicitly describe a 'contract' approval gate before any agent touches the repository ('Nothing runs until a person approves a contract... an agent is about to change your repository'), and mention approving 'big calls' from a phone, indicating a review/approve workflow prior to code changes. Missing for 10: independent/hands-on confirmation of the approval UI showing the actual implementation plan content and evidence of what happens if a plan is rejected/revised.", "evidenceIds": [ "foreloop-docs-7", "foreloop-docs-20", "foreloop-docs-3" ] }, { "productId": "foreloop", "storyId": "approve-task-contract", "verdict": "full", "quality": 8, "confidence": "medium", "rationale": "Foreloop's docs explicitly state approval of a contract is a mandatory gate before any agent touches the repository ('Nothing runs until a person approves a contract... an agent is about to change your repository'), and agents only claim 'approved tasks' from the repo. This directly matches the engineering-lead approval-gate story, reinforced by mobile approval flow. Missing for 10: independent/hands-on verification that the gate is enforced in practice (not just documented) and detail on what 'scope and contract' specifically contains.", "evidenceIds": [ "foreloop-docs-7", "foreloop-docs-3", "foreloop-docs-20" ] }, { "productId": "foreloop", "storyId": "assign-task-from-ticket", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "Foreloop's workflow starts from user feedback reports or free-text intentions that get turned into tasks an agent claims (foreloop-docs-4, foreloop-docs-12), but there is no evidence of importing or linking an existing GitHub issue/Jira ticket as the task source for an agent to pick up.", "evidenceIds": [] }, { "productId": "foreloop", "storyId": "auto-approve-all-actions", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "Foreloop's docs explicitly state that human approval of a contract is a deliberate, mandatory gate before any agent action runs ('Nothing runs until a person approves a contract. Approval is the only gate'), with no mention of a setting to bypass or auto-approve this gate. This indicates the product does not offer an auto-approve configuration, so the story's capability is unsupported.", "evidenceIds": [ "foreloop-docs-7", "foreloop-docs-20" ] }, { "productId": "foreloop", "storyId": "auto-fix-readiness-gaps", "verdict": "none", "quality": 0, "confidence": "low", "rationale": "Foreloop's docs describe a task/feedback pipeline where agents claim approved tasks and open PRs, but nothing in the evidence pack mentions detecting or automatically remediating 'agent-readiness criteria' (e.g., AGENTS.md, tool-permission manifests, readiness scoring) in a repository. Missing for 10: any readiness-criteria scanning/grading feature, evidence of automatic detection of failing criteria, and evidence of automated fixes tied to such criteria.", "evidenceIds": [] }, { "productId": "foreloop", "storyId": "auto-open-pr-on-completion", "verdict": "full", "quality": 8, "confidence": "medium", "rationale": "Docs clearly describe the agent workflow: it claims a task, does the work, and opens a pull request from the user's GitHub account, with GitHub connection enabling PR creation, gated by human approval of the contract before work starts. Missing for 10: independent/hands-on confirmation that PRs are reliably opened on task completion, and no detail on configuring PR-specific settings (e.g., target branch, draft PRs).", "evidenceIds": [ "foreloop-docs-3", "foreloop-docs-15", "foreloop-docs-7" ] }, { "productId": "foreloop", "storyId": "automated-pr-review", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "Foreloop is a task-management/agent-orchestration tool that lets agents claim tasks and open PRs from feedback, not a PR-review/code-review product; there's no evidence it reviews pull requests or adds inline review comments — this is a different product category (task orchestration vs. review-quality-gates).", "evidenceIds": [] }, { "productId": "foreloop", "storyId": "automatic-environment-setup", "verdict": "partial", "quality": 4, "confidence": "medium", "rationale": "Foreloop's docs describe the developer running an agent 'in a checkout of your repository on your own machine' and using `foreloop init` to install a task-executor skill into that existing checkout — implying the human, not the agent, does the initial clone/setup, with the agent only claiming and working tasks afterward. There's no documentation of the agent autonomously cloning the repo, installing dependencies, or provisioning its own environment from scratch. Missing for 10: evidence of agent-initiated repo cloning, dependency installation steps, and autonomous environment configuration (e.g., a sandboxed or cloud checkout flow) rather than reliance on a pre-existing local checkout.", "evidenceIds": [ "foreloop-docs-2", "foreloop-docs-3", "foreloop-docs-8" ] }, { "productId": "foreloop", "storyId": "automation-bulk-operations", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "Foreloop's docs describe per-task workflows (claim one task, agent does the work, opens one PR) and a CLI/MCP interface, but nothing in the evidence describes performing an operation across many items/tasks simultaneously (e.g., batch approve, bulk edit, multi-task claim). The axis is plausible for a CLI/automation tool but no evidence of bulk operation support exists.", "evidenceIds": [ "foreloop-docs-3", "foreloop-docs-8", "foreloop-docs-9", "foreloop-docs-16" ] }, { "productId": "foreloop", "storyId": "automation-rules-engine", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "Foreloop's docs describe human-approved task workflows (agents claim tasks, but 'nothing runs until a person approves a contract'), not a rules engine for automatic event-triggered actions; the closest mention is CLI usability from cron, which is not evidence of a native rules/trigger system. missing for 10: any documented rule-definition syntax, event-trigger configuration, or automation-without-approval capability.", "evidenceIds": [ "foreloop-docs-7", "foreloop-docs-16" ] }, { "productId": "foreloop", "storyId": "automation-scheduled-jobs", "verdict": "partial", "quality": 3, "confidence": "low", "rationale": "The only relevant evidence is a single line noting the CLI can be run 'from anything that can run one, including coding agents and cron,' implying users could wire up their own cron-based scheduling — but Foreloop itself documents no native recurring-job/workflow scheduler, retry policy, or schedule management UI. Missing for 10: a first-party scheduling feature, documented recurring-workflow configuration, and evidence of built-in cron/interval management within the product.", "evidenceIds": [ "foreloop-docs-16" ] }, { "productId": "foreloop", "storyId": "automation-versioned-workflows", "verdict": "partial", "quality": 4, "confidence": "low", "rationale": "Foreloop's workflow requires human approval before any agent-driven change runs (foreloop-docs-7) and changes are delivered as GitHub pull requests (foreloop-docs-3, foreloop-docs-15), which inherently gives code-level versioning and review via git. However, there is no documentation of versioning, reviewing, or rolling back the automations/tasks/loops themselves (as opposed to the resulting code diffs) — no rollback command, task history, or automation-versioning feature is evidenced. Missing for 10: explicit automation/task versioning, a documented rollback mechanism for tasks or loops, and audit/history views beyond the live agent status list.", "evidenceIds": [ "foreloop-docs-7", "foreloop-docs-3", "foreloop-docs-15", "foreloop-docs-8" ] }, { "productId": "foreloop", "storyId": "autonomous-bug-fixing", "verdict": "full", "quality": 7, "confidence": "medium", "rationale": "Foreloop's documented workflow directly supports this: a bug report captured via the feedback widget (with page, metadata, screenshot) becomes an intention/task that an agent (Claude Code or Codex) claims, works on in a repo checkout, and resolves by opening a PR, without the reporter needing to know which files change. Human approval gates task initiation but the diagnosis-and-fix work itself is agent-driven end to end. Missing for 10: independent/hands-on evidence that agents actually diagnose root causes correctly, and more detail on how the agent investigates ambiguous bugs beyond the general task pipeline description.", "evidenceIds": [ "foreloop-docs-3", "foreloop-docs-4", "foreloop-docs-12", "foreloop-docs-21", "foreloop-docs-15" ] }, { "productId": "foreloop", "storyId": "bring-your-own-model", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "The docs describe running third-party agents (Claude Code or Codex) locally against Foreloop's task API, but there's no evidence Foreloop lets a user configure or supply their own LLM/API key for Foreloop's own model usage (e.g., intention generation, feedback triage). Missing for 10: any documentation of model/provider selection, API key configuration UI, or support for alternate LLM backends.", "evidenceIds": [ "foreloop-docs-3", "foreloop-docs-12" ] }, { "productId": "foreloop", "storyId": "chat-with-agent-in-slack", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "Foreloop's workflow is built around agents polling for approved tasks/contracts and reporting status via a dashboard (docs-3, docs-7, docs-8), not around tagging or chatting with agents in a thread. No evidence describes a chat-thread interface or an @-mention mechanism to delegate work conversationally.", "evidenceIds": [ "foreloop-docs-3", "foreloop-docs-7", "foreloop-docs-8" ] }, { "productId": "foreloop", "storyId": "ci-failure-auto-fix", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "Foreloop's documented workflow is task/intention-based (feedback → task → agent → PR), with no evidence of CI pipeline integration, failed-build detection, or automated diagnosis-and-fix triggered by CI failures. The axis is plausible for an agent-driven PR tool, but nothing in the evidence pack shows Foreloop watching or reacting to CI status.", "evidenceIds": [] }, { "productId": "foreloop", "storyId": "codebase-context-file", "verdict": "partial", "quality": 4, "confidence": "low", "rationale": "Foreloop's docs describe a 'skill' mechanism ('Write a skill when you notice yourself giving an agent the same correction twice') and an installed task-executor skill in the checkout, which loosely maps to encoding project-specific conventions for agents, but there's no explicit documentation of a dedicated context file (e.g., conventions.md) for describing codebase style/conventions. Missing for 10: explicit context-file feature, guidance on structuring codebase conventions, evidence that agents consume such a file specifically for improving plan/code relevance.", "evidenceIds": [ "foreloop-docs-2", "foreloop-docs-10" ] }, { "productId": "foreloop", "storyId": "concurrent-task-execution", "verdict": "partial", "quality": 4, "confidence": "low", "rationale": "The docs show that multiple agent workers can each claim tasks and run in parallel checkouts, with a dashboard listing one row per worker (foreloop-docs-8), implying some capacity for concurrent agent execution across a team. However, there is no explicit product feature for orchestrating, scaling, or managing many concurrent tasks at once, no throughput/scale claims, and no evidence of built-in concurrency limits or fleet management tooling. missing for 10: explicit support/documentation for running many agents simultaneously at scale, concurrency controls, throughput metrics, or a scheduler/queue for parallel task dispatch.", "evidenceIds": [ "foreloop-docs-3", "foreloop-docs-8" ] }, { "productId": "foreloop", "storyId": "connect-github-repo", "verdict": "full", "quality": 7, "confidence": "medium", "rationale": "Docs explicitly describe connecting GitHub so Foreloop can read code and agents can open PRs against it, with a full workflow (agent claims task in a checkout, does work, opens a PR from the user's GitHub account, gated by human approval). Missing for 10: independent/hands-on verification of the GitHub connection flow and no screenshots or third-party confirmation that PRs are actually opened successfully.", "evidenceIds": [ "foreloop-docs-15", "foreloop-docs-3", "foreloop-docs-7", "foreloop-docs-2" ] }, { "productId": "foreloop", "storyId": "daily-task-quota-limits", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "No evidence in the pack mentions plan-based daily task limits, concurrency limits, or any pricing/plan management dashboard for engineering leads; the docs focus on agent workflow mechanics (task claiming, approvals, MCP) with no reference to usage caps or plan tiers.", "evidenceIds": [] }, { "productId": "foreloop", "storyId": "delegate-sessions-for-teammates", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "The evidence describes agents run locally by an individual user in their own repo checkout, claiming tasks and opening PRs from their own GitHub account, but nothing describes an engineering-lead provisioning or launching agent sessions on behalf of other org members. Missing for 10: any admin/org-level session creation API or UI, delegated-session documentation, or multi-user session management evidence.", "evidenceIds": [ "foreloop-docs-3", "foreloop-docs-8", "foreloop-docs-15" ] }, { "productId": "foreloop", "storyId": "describe-feature-in-plain-language", "verdict": "full", "quality": 8, "confidence": "medium", "rationale": "Docs describe plain-language input ('Users can't tell which invoices are overdue') turning into scoped, approvable tasks via 'intentions' that an agent fills in and proposes as tasks, gated by human approval before an agent acts on the repo. This directly matches intent-to-spec workflow with first-party documentation across multiple pages. Missing for 10: independent/hands-on verification of the resulting task's scoping quality and no example of the full generated task artifact.", "evidenceIds": [ "foreloop-docs-4", "foreloop-docs-12", "foreloop-docs-7", "foreloop-docs-3" ] }, { "productId": "foreloop", "storyId": "design-to-code-handoff", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "Foreloop lets a non-engineer describe a need in plain language (e.g. \"Users can't tell which invoices are overdue\") or flag a page via a feedback widget with screenshot, and an agent turns that into tasks and a PR without the PM needing to specify files — this covers a 'no engineering handoff' description-to-implementation flow. However, the evidence never shows ingesting an actual mockup/design file (e.g. Figma) as the starting artifact, and a human still must approve the contract before code changes run, so it's not a fully autonomous design-to-code path. Missing for 10: direct mockup/design-file import as an input, evidence of end-to-end completion without any review gate, and a demonstrated design-to-code case study.", "evidenceIds": [ "foreloop-docs-4", "foreloop-docs-21", "foreloop-docs-12", "foreloop-docs-7", "foreloop-docs-3" ] }, { "productId": "foreloop", "storyId": "diff-review-before-pr", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "Foreloop's 'approval' gate (foreloop-docs-7) is a pre-work contract approval, not a diff review after the agent finishes changes — the agent then 'opens a pull request from your GitHub account' directly (foreloop-docs-3), with no documented step where a person reviews the diff before the PR is created. No evidence describes a diff-preview/approve-before-PR workflow.", "evidenceIds": [ "foreloop-docs-3", "foreloop-docs-7" ] }, { "productId": "foreloop", "storyId": "end-to-end-feature-implementation", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "Foreloop orchestrates tasks and lets an agent (Claude Code/Codex) claim a task, do the work, and open a PR in the user's repo, which covers end-to-end feature implementation via an external coding agent — but Foreloop itself doesn't write code or tests; it delegates that entirely to the connected agent, and there's no explicit mention of test-writing behavior or verification. missing for 10: explicit evidence that generated PRs include tests, independent/hands-on confirmation of successful end-to-end feature delivery, and detail on how test coverage is verified before approval.", "evidenceIds": [ "foreloop-docs-3", "foreloop-docs-4", "foreloop-docs-7", "foreloop-docs-15" ] }, { "productId": "foreloop", "storyId": "enterprise-sso-licensing", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "No evidence in the pack mentions enterprise licensing, SSO, or commercial support tiers; all citations focus on CLI usage, MCP integration, and agent workflows. This is a plausible axis for a SaaS product like Foreloop (organizations often need SSO/enterprise support), so absence of evidence yields 'none' rather than 'na'.", "evidenceIds": [] }, { "productId": "foreloop", "storyId": "feedback-to-task-conversion", "verdict": "full", "quality": 7, "confidence": "medium", "rationale": "Docs show a feedback-collection widget that captures user submissions, and an agent-driven 'intention' workflow that reads the submission, fills in an intention, and proposes tasks (foreloop-docs-12), with human approval gating execution (foreloop-docs-7). This directly matches converting feedback into structured tasks with proposed scope, though evidence is entirely vendor docs with no independent/hands-on corroboration of output quality or scope structure. Missing for 10: independent/hands-on verification of the feedback-to-task pipeline, and concrete example of the 'proposed scope' structure/fields.", "evidenceIds": [ "foreloop-docs-12", "foreloop-docs-7", "foreloop-docs-21", "foreloop-docs-4" ] }, { "productId": "foreloop", "storyId": "headless-ci-execution", "verdict": "partial", "quality": 5, "confidence": "low", "rationale": "Docs mention the CLI is invocable from 'anything that can run one, including coding agents and cron' and support scriptable --json output, suggesting headless invocation is possible (foreloop-docs-16, foreloop-docs-9). However, other docs state that 'nothing runs until a person approves a contract' (foreloop-docs-7), implying a human-in-the-loop gate that complicates fully unattended CI/CD execution, and there is no explicit CI/CD pipeline example or corroboration. Missing for 10: explicit CI/CD/pipeline example, evidence of non-interactive approval flow, independent confirmation of headless operation.", "evidenceIds": [ "foreloop-docs-16", "foreloop-docs-9", "foreloop-docs-7" ] }, { "productId": "foreloop", "storyId": "isolated-sandbox-execution", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "Foreloop's docs explicitly describe agents running 'in a checkout of your repository on your own machine' (foreloop-docs-3), with no mention of sandboxing, containerization, or isolated execution environments for code/dependency installation. Missing for 10: any documentation of sandbox/container isolation, dependency install safety, or resource/network restrictions during agent execution.", "evidenceIds": [ "foreloop-docs-3" ] }, { "productId": "foreloop", "storyId": "issue-tracker-sync", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "Foreloop's evidence covers GitHub integration, its own task/loop tracking, MCP server, and feedback widgets, but there is no mention of connecting to Jira, Linear, ClickUp, or Monday.com at all — Foreloop appears to use its own internal issue/task system rather than integrating external issue trackers.", "evidenceIds": [ "foreloop-docs-15", "foreloop-docs-16", "foreloop-docs-3" ] }, { "productId": "foreloop", "storyId": "live-agent-activity-monitoring", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "The docs describe a Development/Agents view listing each worker's agent identity, checkout name, current task, and last message — a real-time-ish status view (foreloop-docs-8). However, there's no evidence of live streaming updates, logs, or granular in-progress activity beyond a status line, and no independent/hands-on confirmation of real-time refresh behavior. missing for 10: evidence of live/streaming updates or detailed activity logs, independent confirmation of real-time refresh, granularity beyond a single status line per agent.", "evidenceIds": [ "foreloop-docs-8", "foreloop-docs-3" ] }, { "productId": "foreloop", "storyId": "managed-cloud-execution", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "Foreloop's own docs state the opposite of this story: agents (Claude Code or Codex) run 'in a checkout of your repository on your own machine,' with Foreloop only coordinating tasks and approvals — there is no evidence of a managed cloud runtime that executes agents on Foreloop's infrastructure.", "evidenceIds": [ "foreloop-docs-3", "foreloop-docs-8" ] }, { "productId": "foreloop", "storyId": "manual-model-override", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "Foreloop orchestrates tasks/agents (Claude Code, Codex) run by the user rather than offering its own model-selection setting; there is no evidence of an internal model picker, so this axis is a category error for this product's role as an agent-orchestration/task platform.", "evidenceIds": [] }, { "productId": "foreloop", "storyId": "mid-task-takeover", "verdict": "partial", "quality": 4, "confidence": "low", "rationale": "Foreloop lets agents run locally in a checkout and claim/report tasks, and humans can approve contracts or view agent status via a dashboard row per worker, but there's no evidence of a live handoff mechanism to pause, take over, or redirect an in-progress task from editor/terminal/browser mid-execution. missing for 10: explicit takeover/interrupt UX, editor-integrated control of a running agent, terminal command to seize an active task, and any hands-on confirmation of mid-task redirection.", "evidenceIds": [ "foreloop-docs-3", "foreloop-docs-7", "foreloop-docs-8", "foreloop-docs-20" ] }, { "productId": "foreloop", "storyId": "mobile-approval-of-decisions", "verdict": "partial", "quality": 5, "confidence": "low", "rationale": "Foreloop explicitly markets phone-based approval of 'big calls' and documents that no agent work runs until a human approves a contract, which matches the core ask. However, evidence is limited to marketing taglines with no detail on the mobile UI/notification flow, how approvals interact with agents continuing to work in parallel, or independent corroboration. Missing for 10: concrete mobile-app/notification workflow details, evidence of multi-agent concurrent operation while awaiting approval, and third-party confirmation.", "evidenceIds": [ "foreloop-docs-20", "foreloop-docs-7", "foreloop-docs-8" ] }, { "productId": "foreloop", "storyId": "one-click-repo-access", "verdict": "partial", "quality": 5, "confidence": "low", "rationale": "Docs confirm connecting GitHub gives Foreloop read/PR access to repos (foreloop-docs-15) and that agents run against a checkout, but achieving this requires installing a global CLI, running foreloop init, configuring MCP with an API key, and running an agent locally — multiple manual steps rather than a true one-click flow. missing for 10: explicit one-click OAuth install flow, evidence of a simple GitHub App install button, independent confirmation of setup simplicity.", "evidenceIds": [ "foreloop-docs-15", "foreloop-docs-1", "foreloop-docs-2", "foreloop-docs-5", "foreloop-docs-19" ] }, { "productId": "foreloop", "storyId": "openness-api-parity", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "Foreloop ships a CLI covering projects, loops, intentions, stories and tasks with a stable `--json` output on every command, plus an MCP endpoint for agent clients, suggesting broad API/CLI parity with the UI. However, there's no explicit statement or independent confirmation that literally every UI action (e.g., approvals, feedback widget config, agent monitoring dashboard views) is exposed via API/CLI. missing for 10: explicit UI-to-API parity documentation or audit, independent/hands-on verification, coverage of approval workflow and dashboard views via API.", "evidenceIds": [ "foreloop-docs-16", "foreloop-docs-9", "foreloop-docs-18", "foreloop-docs-5", "foreloop-docs-19", "foreloop-probe-1" ] }, { "productId": "foreloop", "storyId": "openness-full-export", "verdict": "none", "quality": 0, "confidence": "low", "rationale": "No evidence of a bulk data export feature or open-format export of projects/tasks/intentions for migration away from Foreloop; the closest hints (--json CLI output, MCP API access) are for programmatic access, not a documented full-data export/leave capability.", "evidenceIds": [] }, { "productId": "foreloop", "storyId": "openness-open-license", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "No evidence of an open-source license or public source code repository; Foreloop is presented as a closed SaaS product with a CLI/widget/MCP server, but nothing indicates the source is available under an open license.", "evidenceIds": [] }, { "productId": "foreloop", "storyId": "openness-self-host", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "All evidence describes Foreloop as a hosted SaaS (with a CLI/MCP endpoint at foreloop.com/api) that agents connect to remotely; there is no mention of a self-hostable core server, on-prem deployment, or open-source release. missing for 10: any documentation of a self-hosted deployment option, container/server image, or license permitting self-hosting.", "evidenceIds": [] }, { "productId": "foreloop", "storyId": "privacy-data-residency", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "No evidence anywhere in the pack mentions data residency, region selection, or storage location options; Foreloop's docs cover CLI, MCP, agents, and feedback widgets but never address where data is stored or any regional controls.", "evidenceIds": [] }, { "productId": "foreloop", "storyId": "privacy-no-training", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "Foreloop is a task/agent orchestration and feedback-collection tool for software repos, not a data-processing or AI-model-training service; the evidence pack contains no data-usage or model-training policy at all, and this axis is a category error for this product type.", "evidenceIds": [] }, { "productId": "foreloop", "storyId": "privacy-retention-controls", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "No evidence pack items mention data retention policies, deletion controls, data export, or privacy settings for user/feedback data collected by Foreloop; all evidence focuses on agent workflows, CLI usage, and MCP integration.", "evidenceIds": [] }, { "productId": "foreloop", "storyId": "privacy-telemetry-optout", "verdict": "none", "quality": 0, "confidence": "low", "rationale": "No evidence in the pack mentions telemetry, usage tracking, analytics collection, or an opt-out mechanism for Foreloop; the docs cover CLI usage, agent workflows, and feedback widgets but never address data collection or privacy controls.", "evidenceIds": [] }, { "productId": "foreloop", "storyId": "query-repo-documentation", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "Foreloop is a task/agent orchestration and feedback-collection tool that connects to GitHub repos to let agents claim tasks and open PRs; it does not generate or expose queryable documentation for repositories. This story targets a documentation-generation/query product category, which is a different axis from Foreloop's core offering.", "evidenceIds": [] }, { "productId": "foreloop", "storyId": "repo-readiness-report", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "No evidence in the pack describes a 'readiness report' or any repository-readiness scoring/assessment feature for autonomous agents; Foreloop's docs cover task claiming, MCP connectivity, feedback widgets, and approval gates, but nothing about evaluating repo readiness.", "evidenceIds": [] }, { "productId": "foreloop", "storyId": "security-alert-remediation", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "Foreloop's evidence describes a general feedback/task-to-PR agent workflow (bug reports, feature requests) with human approval gates, but nothing in the pack mentions ingesting security alerts (e.g., from SAST/SCA/dependency scanners), validating them, or triggering remediation PRs specifically for vulnerabilities. The axis is plausible for a task-automation platform, but no evidence shows this specific capability.", "evidenceIds": [] }, { "productId": "foreloop", "storyId": "self-hosted-agent-deployment", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "Foreloop is a hosted SaaS orchestration layer (agents run locally in your repo checkout and talk to Foreloop's cloud API/MCP endpoint) — there is no evidence of a self-hostable server/backend component; the product's core value is the hosted service coordinating tasks, so 'self-hosting agent infrastructure' is a category mismatch rather than a missing feature.", "evidenceIds": [ "foreloop-docs-3", "foreloop-docs-5", "foreloop-docs-15" ] }, { "productId": "foreloop", "storyId": "steer-active-session", "verdict": "none", "quality": 0, "confidence": "low", "rationale": "The evidence describes agents claiming tasks, opening PRs, and reporting status, plus approval gates and skill-writing to correct recurring mistakes — but nothing describes sending live follow-up/steering instructions to an already-running agent session mid-task without restarting it.", "evidenceIds": [] }, { "productId": "foreloop", "storyId": "task-completion-notifications", "verdict": "partial", "quality": 3, "confidence": "low", "rationale": "Foreloop provides a dashboard showing agent status ('last thing it said') and mentions approving 'big calls from your phone', implying some notification/approval flow, but there is no explicit documentation of push/email/Slack notifications when a task completes or needs input. missing for 10: explicit notification channel (email/Slack/push) documentation, confirmation of real-time alerts rather than manual dashboard polling, evidence of notification content for 'needs input' events.", "evidenceIds": [ "foreloop-docs-8", "foreloop-docs-20", "foreloop-docs-7" ] }, { "productId": "foreloop", "storyId": "tiered-autonomy-levels", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "Foreloop documents a single approval gate before any agent work starts ('Nothing runs until a person approves a contract') and lets you approve from your phone, but there is no evidence of configurable tiered autonomy levels (e.g., auto-approving certain task types while requiring confirmation for others).", "evidenceIds": [ "foreloop-docs-7", "foreloop-docs-20" ] }, { "productId": "foreloop", "storyId": "trigger-agent-on-build-failure", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "Foreloop's docs describe agents claiming human-approved tasks and opening PRs from a local checkout, with a CLI usable from \"anything that can run one, including coding agents and cron\" (foreloop-docs-16), but there is no evidence of CI/CD pipeline integration or of the agent being triggered specifically to fix broken builds or failing tests — its workflow centers on approved feature/task work, not automated CI failure remediation.", "evidenceIds": [ "foreloop-docs-3", "foreloop-docs-7", "foreloop-docs-16" ] }, { "productId": "foreloop", "storyId": "visual-context-for-tasks", "verdict": "partial", "quality": 4, "confidence": "low", "rationale": "The feedback widget lets a customer point at an element and attach an optional screenshot with metadata so 'the model fixes the right thing' (foreloop-docs-21), which is adjacent to the story but is customer-bug-report flow, not a developer attaching an annotated mockup directly to a task. There's no documentation of markup/annotation tools, mockup uploads, or a developer-facing task-attachment workflow for visual specs. Missing for 10: developer-facing screenshot/mockup attachment to tasks, annotation/markup capability, and evidence the agent parses visual markup to implement precise UI changes.", "evidenceIds": [ "foreloop-docs-21" ] }, { "productId": "humanlayer", "storyId": "agentic-agent-docs", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "A direct probe for llms.txt returned 404, and no evidence pack item mentions agent-oriented documentation formats or an alternative machine-readable docs endpoint.", "evidenceIds": [ "humanlayer-probe-1" ] }, { "productId": "humanlayer", "storyId": "agentic-ai-insights", "verdict": "partial", "quality": 4, "confidence": "low", "rationale": "HumanLayer's agents do generate task artifacts, draft PRs, and comments derived from a user's codebase/tickets, and 'Advanced Metrics' surfaces usage/cost/productivity data, which loosely resembles data-derived insight. However there is no evidence of dedicated AI-generated analytical insights or proactive suggestions distinct from executing assigned coding tasks. Missing for 10: explicit insight/analytics generation from user data, proactive suggestion features beyond task execution, and any first-party or community evidence of an 'insights' capability.", "evidenceIds": [ "humanlayer-docs-20", "humanlayer-docs-19", "humanlayer-docs-5" ] }, { "productId": "humanlayer", "storyId": "agentic-autonomous-automation", "verdict": "full", "quality": 7, "confidence": "medium", "rationale": "Docs explicitly describe `humanlayer automation run` for running Cloud-visible coding sessions from CI jobs, cron machines, or scripts, plus remote daemons and launch tokens for non-interactive/background execution contexts, directly supporting autonomous background automations. Missing for 10: independent/hands-on verification of long-running background automations, native scheduling UI, and clarity on how human-approval gates interact with continuous autonomous runs.", "evidenceIds": [ "humanlayer-docs-12", "humanlayer-docs-13", "humanlayer-docs-7", "humanlayer-docs-8" ] }, { "productId": "humanlayer", "storyId": "agentic-builtin-assistant", "verdict": "full", "quality": 7, "confidence": "medium", "rationale": "HumanLayer's core product model is task delegation to AI coding agents (Claude Code, Codex, Bedrock-backed Claude) via 'sessions', with workflow modes (Oneshot, RPI, PRD-Oriented, Freeform) and automation runs from CI/cron, directly matching 'delegate tasks to a built-in AI assistant'. missing for 10: independent/hands-on verification of the delegation experience beyond vendor docs, and clarity on how autonomous vs supervised the assistant is in practice.", "evidenceIds": [ "humanlayer-docs-1", "humanlayer-docs-5", "humanlayer-docs-6", "humanlayer-docs-9", "humanlayer-docs-10", "humanlayer-docs-12", "humanlayer-docs-14" ] }, { "productId": "humanlayer", "storyId": "agentic-headless", "verdict": "full", "quality": 8, "confidence": "medium", "rationale": "Docs explicitly describe running HumanLayer headlessly via `humanlayer automation run` from CI jobs, cron machines, or scripts, plus launch tokens for non-interactive/non-PTY environments, and remote daemon hosts on cloud VMs or servers, directly matching the CI/automation story. Missing for 10: independent/hands-on confirmation of CI usage and more detail on auth/config specifics for automated pipelines.", "evidenceIds": [ "humanlayer-docs-12", "humanlayer-docs-13", "humanlayer-docs-7", "humanlayer-probe-3" ] }, { "productId": "humanlayer", "storyId": "agentic-mcp-client", "verdict": "none", "quality": 0, "confidence": "low", "rationale": "No evidence anywhere in the pack mentions MCP servers or the ability to plug external tool servers into HumanLayer; integrations mentioned (Jira, Slack, GitHub, Linear) are task-source connectors, not MCP tool servers. Missing for 10: any mention of MCP protocol support, MCP server configuration, or tool-plugin mechanism.", "evidenceIds": [] }, { "productId": "humanlayer", "storyId": "agentic-mcp-server", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "HumanLayer is a platform/control-plane for running and overseeing coding-agent sessions (Claude Code, Codex) rather than itself being an agent, so an official MCP server is a fair, applicable axis. The evidence pack documents many integrations (Jira, Slack, GitHub, Linear), a CLI, and remote daemons, but no MCP server offering is mentioned anywhere, and API/OpenAPI probes returned 404s. missing for 10: any first-party MCP server documentation, endpoint, or 'mcp serve' style capability.", "evidenceIds": [ "humanlayer-probe-2", "humanlayer-probe-1", "humanlayer-docs-12" ] }, { "productId": "humanlayer", "storyId": "agentic-nl-commands", "verdict": "full", "quality": 7, "confidence": "medium", "rationale": "HumanLayer's core interaction model is giving natural-language instructions to agent sessions (Claude Code, Codex) to create tasks, configure workspaces, and choose workflow phases, as shown by the example NL workspace-config prompt and workflow-selection docs. Missing for 10: independent/hands-on corroboration of NL command robustness and no evidence of a broader NL command surface beyond task/workflow setup.", "evidenceIds": [ "humanlayer-docs-11", "humanlayer-docs-6", "humanlayer-docs-10", "humanlayer-docs-1", "humanlayer-docs-5" ] }, { "productId": "humanlayer", "storyId": "agentic-official-cli", "verdict": "full", "quality": 7, "confidence": "medium", "rationale": "HumanLayer documents an official CLI (e.g. `humanlayer automation run`, launch tokens, remote-daemon control) used for CI, cron, and scripted agentic workflows, confirmed by a dedicated CLI doc page. missing for 10: no independent/hands-on verification of the CLI, no full command reference, and no evidence of broader CLI feature parity with the app.", "evidenceIds": [ "humanlayer-docs-12", "humanlayer-docs-13", "humanlayer-docs-7", "humanlayer-probe-3" ] }, { "productId": "humanlayer", "storyId": "agentic-public-api", "verdict": "partial", "quality": 4, "confidence": "medium", "rationale": "HumanLayer documents a CLI (`humanlayer automation run`, launch tokens, remote daemons) that lets automation environments drive sessions programmatically, which is a form of documented programmatic control, but there is no evidence of a documented public REST/OpenAPI API — probes explicitly found openapi.json/swagger.json/llms.txt all 404. missing for 10: a documented HTTP/OpenAPI public API spec, SDK/client library docs, and independent confirmation of API usage.", "evidenceIds": [ "humanlayer-docs-12", "humanlayer-docs-13", "humanlayer-docs-7", "humanlayer-probe-2", "humanlayer-probe-1", "humanlayer-probe-3" ] }, { "productId": "humanlayer", "storyId": "agentic-scoped-keys", "verdict": "partial", "quality": 3, "confidence": "low", "rationale": "The only relevant evidence is a mention of a 'launch token' scoped to a single non-interactive command, which implies some least-privilege token issuance, but there is no documentation of a broader credential/permission model, scopes, or API key management for agents. missing for 10: explicit least-privilege credential scoping model, permission granularity, revocation/rotation mechanisms, and any independent corroboration.", "evidenceIds": [ "humanlayer-docs-13", "humanlayer-docs-7" ] }, { "productId": "humanlayer", "storyId": "agentic-sdks", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "The evidence pack covers HumanLayer's CLI, workspace config, and third-party integrations (Jira, Slack, GitHub, Linear) but contains no mention of an official SDK (Python, TypeScript, etc.) for building against HumanLayer programmatically. Probes for openapi.json and llms.txt both returned 404, further suggesting no discoverable API/SDK surface.", "evidenceIds": [ "humanlayer-probe-1", "humanlayer-probe-2", "humanlayer-docs-12", "humanlayer-docs-13" ] }, { "productId": "humanlayer", "storyId": "agentic-webhooks", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "No evidence pack item mentions webhooks or event subscription mechanisms; integrations described (Slack, Jira, Linear, GitHub) are inbound task-creation connectors, not outbound webhook events, and API/OpenAPI probes returned 404s.", "evidenceIds": [ "humanlayer-probe-1", "humanlayer-probe-2" ] }, { "productId": "humanlayer", "storyId": "ai-automated-test-generation-on-change", "verdict": "none", "quality": 0, "confidence": "low", "rationale": "No evidence in the pack describes HumanLayer agents automatically writing or executing tests to self-validate code changes; docs cover task/session management, integrations, and workflow phases but not test generation/execution as a validation step.", "evidenceIds": [] }, { "productId": "humanlayer", "storyId": "ai-automatic-model-routing", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "Evidence shows HumanLayer lets users manually select or configure which model/backend to use (Claude via Bedrock, Codex, RPI sub-agents) but there is no evidence of automatic routing of a task prompt to the 'most suitable' model based on task characteristics.", "evidenceIds": [ "humanlayer-docs-9", "humanlayer-docs-14", "humanlayer-docs-15" ] }, { "productId": "humanlayer", "storyId": "ai-issue-triage", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "HumanLayer connects issue trackers (Jira, GitHub, Linear) to create tasks from tickets, but there is no evidence of automatic triage, severity classification, or routing to an owner — integrations only create/link tasks, not assess or assign severity/ownership.", "evidenceIds": [ "humanlayer-docs-3", "humanlayer-docs-16", "humanlayer-docs-17" ] }, { "productId": "humanlayer", "storyId": "api-interactive-docs", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "No evidence of an interactive API reference or runnable examples; probes explicitly show no OpenAPI/swagger spec and no llms.txt found, and docs are guide/tutorial style rather than an API reference sandbox.", "evidenceIds": [ "humanlayer-probe-1", "humanlayer-probe-2" ] }, { "productId": "humanlayer", "storyId": "api-machine-spec", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "A direct probe for OpenAPI/swagger specs at all standard locations returned 404s, and no evidence pack item shows a downloadable machine-readable API spec being offered.", "evidenceIds": [ "humanlayer-probe-2", "humanlayer-probe-1" ] }, { "productId": "humanlayer", "storyId": "api-sandbox", "verdict": "none", "quality": 0, "confidence": "low", "rationale": "HumanLayer's docs describe remote daemons, workspaces, and automation sessions, but there is no mention of a sandbox environment, staging/test data isolation, or any mechanism to separate test runs from production data. missing for 10: explicit sandbox/staging environment documentation, data isolation guarantees, evidence of test-vs-production separation.", "evidenceIds": [] }, { "productId": "humanlayer", "storyId": "api-versioning-policy", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "No evidence of API versioning scheme or a documented deprecation policy; probes for openapi.json/llms.txt returned 404s and no API reference or changelog covering versioning/deprecation is present.", "evidenceIds": [ "humanlayer-probe-1", "humanlayer-probe-2" ] }, { "productId": "humanlayer", "storyId": "approve-plan-before-execution", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "HumanLayer's workflow-phases and RPI sub-agent docs describe planning phases (e.g., 'a change with unclear behavior or code shape needs more review before implementation') and PRD-Oriented/RPI workflows imply a plan stage before code changes, with tasks providing 'one place for comments and review.' However, no evidence explicitly shows a dedicated plan-approval gate/UI step where a developer reviews and approves a plan artifact before implementation begins. missing for 10: explicit documentation of a plan-approval step/UI, first-party example of blocking implementation until plan is approved, independent/hands-on confirmation of this specific gate.", "evidenceIds": [ "humanlayer-docs-6", "humanlayer-docs-9", "humanlayer-docs-10", "humanlayer-docs-5" ] }, { "productId": "humanlayer", "storyId": "approve-task-contract", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "HumanLayer's workflow-phases doc explicitly supports scoping review before implementation (e.g., 'a change with unclear behavior or code shape needs more review before implementation'), and tasks/sessions provide a structured place for comments and review prior to agent execution, plus human-in-the-loop approval is core to the product's value prop per community discussion. However, there's no explicit documented feature for an engineering-lead specifically approving a 'scope and contract' artifact as a gating step before repo modification — it's inferred from general workflow-phase and review mechanics rather than a dedicated scope-approval gate. missing for 10: explicit documentation of a formal scope/contract approval step tied to lead sign-off, evidence of blocking repo writes until such approval, and independent/hands-on confirmation this gate works as intended.", "evidenceIds": [ "humanlayer-docs-6", "humanlayer-docs-5", "humanlayer-docs-10", "humanlayer-comm-6" ] }, { "productId": "humanlayer", "storyId": "assign-task-from-ticket", "verdict": "full", "quality": 8, "confidence": "medium", "rationale": "Docs explicitly describe connecting GitHub, Jira, and Linear so HumanLayer creates tasks directly from issues/tickets and links artifacts back to the source, directly matching the story of assigning agent work from an existing ticket. Missing for 10: independent/hands-on confirmation that this ticket-to-task flow works reliably in practice, and more detail on the actual assignment UX.", "evidenceIds": [ "humanlayer-docs-16", "humanlayer-docs-3", "humanlayer-docs-17", "humanlayer-docs-5" ] }, { "productId": "humanlayer", "storyId": "auto-approve-all-actions", "verdict": "partial", "quality": 4, "confidence": "low", "rationale": "HumanLayer's core premise is human-in-the-loop approval, and docs mention workflow phases like 'Oneshot' for low-risk changes and automation sessions (humanlayer automation run) that run non-interactively without confirmation, implying some auto-approve capability exists, but no explicit documentation of a configurable 'auto-approve all actions' toggle or setting is shown. missing for 10: explicit config/flag to disable per-action confirmation entirely, documentation confirming automation sessions skip all human review rather than just running unattended, and independent confirmation this works as intended.", "evidenceIds": [ "humanlayer-docs-6", "humanlayer-docs-12", "humanlayer-docs-13" ] }, { "productId": "humanlayer", "storyId": "auto-fix-readiness-gaps", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "HumanLayer is a human-in-the-loop agent orchestration/approval tool for running coding tasks, not a repository readiness/compliance scanner with auto-remediation of 'agent-readiness criteria'. This axis is a category error for this product type — no evidence pack content relates to detecting or auto-fixing repo readiness criteria.", "evidenceIds": [] }, { "productId": "humanlayer", "storyId": "auto-open-pr-on-completion", "verdict": "partial", "quality": 4, "confidence": "low", "rationale": "Docs confirm HumanLayer can open a draft pull request from a session (humanlayer-docs-19) and integrates with GitHub for issue-linked tasks (humanlayer-docs-16), but the evidence describes PR creation as a manual 'ask the session agent' action from the UI, not an automatic trigger fired upon task completion. Missing for 10: explicit configuration option/workflow setting for automatic PR creation on task completion, and any evidence of it happening without a manual prompt.", "evidenceIds": [ "humanlayer-docs-19", "humanlayer-docs-16" ] }, { "productId": "humanlayer", "storyId": "automated-pr-review", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "HumanLayer's evidence covers task/session management, human-in-the-loop approvals, draft PR creation, and a PR diff-viewing UI, but there is no mention of an automated review process that generates inline review comments on every pull request. Missing for 10: no evidence of automatic PR review triggers, no mention of AI-generated inline comments, no review-quality-gate CI integration for PRs.", "evidenceIds": [ "humanlayer-docs-19", "humanlayer-docs-21", "humanlayer-docs-16" ] }, { "productId": "humanlayer", "storyId": "automatic-environment-setup", "verdict": "partial", "quality": 5, "confidence": "low", "rationale": "Docs describe workspace configuration (workspace.json, multi-repo setups) and remote hosts that must have access to code/tools/credentials, and one example prompt asks the agent about 'setup commands or local files to copy,' implying some environment configuration ability. However, there is no explicit description of the agent autonomously cloning a repo or installing dependencies end-to-end without human setup of the host/workspace first. Missing for 10: explicit documentation of automatic repo cloning, dependency installation steps, and end-to-end environment bootstrap without prior manual host/workspace configuration.", "evidenceIds": [ "humanlayer-docs-7", "humanlayer-docs-8", "humanlayer-docs-11", "humanlayer-docs-13" ] }, { "productId": "humanlayer", "storyId": "automation-bulk-operations", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "HumanLayer's documentation consistently frames work as single tasks/sessions ('one small task', 'one task on a remote machine', 'one Cloud-visible coding session') with per-task review and approval workflows; there is no mention of batch/bulk operations spanning many items at once. missing for 10: any documented bulk-action API/CLI flag, batch approval mechanism, or multi-item automation workflow.", "evidenceIds": [ "humanlayer-docs-1", "humanlayer-docs-2", "humanlayer-docs-12", "humanlayer-docs-5" ] }, { "productId": "humanlayer", "storyId": "automation-rules-engine", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "HumanLayer integrations (GitHub, Jira, Linear) create tasks automatically from external events like new issues/tickets, and `humanlayer automation run` lets sessions be triggered from CI jobs, cron, or scripts — both are forms of event-driven automation. However, there's no evidence of a general-purpose rules/conditions engine letting users define arbitrary trigger-condition-action logic; the automation is limited to fixed integration hooks and script-based invocation. Missing for 10: a documented rule-definition interface (conditions, filters, custom triggers) beyond fixed platform integrations, and independent confirmation these event-triggers work reliably in practice.", "evidenceIds": [ "humanlayer-docs-3", "humanlayer-docs-16", "humanlayer-docs-17", "humanlayer-docs-12", "humanlayer-docs-13" ] }, { "productId": "humanlayer", "storyId": "automation-scheduled-jobs", "verdict": "partial", "quality": 4, "confidence": "low", "rationale": "HumanLayer's `automation run` CLI lets you trigger a Cloud-visible coding session from a cron machine or CI job, implying recurring runs are possible via external schedulers, but there is no documented native scheduling/recurrence feature (no cron syntax, interval config, or job queue) inside HumanLayer itself. missing for 10: built-in recurring scheduler, interval/cron configuration options, evidence of persistent recurring workflow management.", "evidenceIds": [ "humanlayer-docs-12", "humanlayer-docs-13" ] }, { "productId": "humanlayer", "storyId": "automation-versioned-workflows", "verdict": "partial", "quality": 5, "confidence": "low", "rationale": "HumanLayer's task model provides review (comments, PR draft creation) and history that persists across sessions, giving some review/audit capability, but there is no explicit documentation of versioning workflow definitions or rolling back an automation to a prior version. missing for 10: explicit automation versioning/history diffing, a documented rollback mechanism, and independent confirmation these review features extend to full automation lifecycle management.", "evidenceIds": [ "humanlayer-docs-5", "humanlayer-docs-19", "humanlayer-docs-6" ] }, { "productId": "humanlayer", "storyId": "autonomous-bug-fixing", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "HumanLayer supports creating tasks directly from GitHub/Jira/Linear issues and then running an agent session (Claude Code/Codex) against the linked repo, with an 'Oneshot' workflow phase designed for small, clear changes — a plausible bug-fix pipeline. However, the product's core design is human-in-the-loop with approval gates rather than fully autonomous action, and there's no end-to-end documented example of an agent independently diagnosing a bug from a ticket and shipping a fix without human review. Missing for 10: a concrete autonomous bug-diagnosis-and-fix walkthrough, and clarity on how much human approval is required mid-flow.", "evidenceIds": [ "humanlayer-docs-16", "humanlayer-docs-17", "humanlayer-docs-6", "humanlayer-docs-12", "humanlayer-comm-6" ] }, { "productId": "humanlayer", "storyId": "bring-your-own-model", "verdict": "partial", "quality": 5, "confidence": "medium", "rationale": "Docs show HumanLayer sessions can use different backends/models — Claude Code, OpenAI Codex, and Claude via Amazon Bedrock instead of the Anthropic API — indicating some flexibility in model/provider choice, which implies bringing your own credentials for these paths. However, there is no explicit doc describing a generic 'bring your own API key' mechanism for arbitrary LLMs or a pricing-tier note tying this to cost savings for engineering leads. Missing for 10: explicit BYO-API-key configuration docs, support for arbitrary/third-party model providers beyond Claude/Codex/Bedrock, and any pricing-related messaging about cost control via own keys.", "evidenceIds": [ "humanlayer-docs-14", "humanlayer-docs-15", "humanlayer-docs-9" ] }, { "productId": "humanlayer", "storyId": "chat-with-agent-in-slack", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "HumanLayer's Slack integration only pushes task-artifact updates into channels (docs-4) and other integrations (GitHub, Jira, Linear) create tasks from tickets/issues, not from tagging an agent inside a chat thread. There is no evidence of a chat-native @mention or in-thread delegation workflow for discussing/assigning tasks to an agent.", "evidenceIds": [ "humanlayer-docs-4", "humanlayer-docs-16", "humanlayer-docs-3", "humanlayer-docs-17" ] }, { "productId": "humanlayer", "storyId": "ci-failure-auto-fix", "verdict": "partial", "quality": 5, "confidence": "low", "rationale": "HumanLayer supports running automation sessions from CI (`humanlayer automation run`) and can open draft PRs from a session's diff/GitHub tab, and GitHub integration links tasks to issues—so the building blocks for a CI-triggered fix-and-PR flow exist. However there is no direct evidence of automatic diagnosis of failed CI logs/errors or a documented end-to-end 'CI failure → agent diagnosis → PR' pipeline. Missing for 10: explicit CI-failure-detection/diagnosis workflow docs, example of a failing pipeline auto-triggering a session, and confirmation the resulting PR addresses the CI failure specifically.", "evidenceIds": [ "humanlayer-docs-12", "humanlayer-docs-19", "humanlayer-docs-16" ] }, { "productId": "humanlayer", "storyId": "codebase-context-file", "verdict": "none", "quality": 0, "confidence": "low", "rationale": "The docs describe workspace-level config files (workspace.json/workspace.local.json) for team/machine settings and multi-repo setup, but there is no evidence of a dedicated context file for describing codebase conventions to improve agent-generated plans/code.", "evidenceIds": [ "humanlayer-docs-8", "humanlayer-docs-11" ] }, { "productId": "humanlayer", "storyId": "concurrent-task-execution", "verdict": "partial", "quality": 5, "confidence": "medium", "rationale": "Docs describe an architecture (tasks/sessions, multi-repository workspaces, remote daemons on cloud VMs, and a CLI 'automation run' for CI/cron/scripts) that could support running many agent tasks in parallel, and 'Advanced Metrics' track usage/productivity across an org. However, tutorials and guides are framed around running one task/session at a time, and there is no explicit documentation or evidence of concurrent multi-task orchestration, throughput dashboards, or scaling guarantees for many simultaneous agents. Missing for 10: explicit multi-session concurrency docs, evidence of parallel task orchestration at scale, and independent confirmation of throughput gains.", "evidenceIds": [ "humanlayer-docs-5", "humanlayer-docs-7", "humanlayer-docs-11", "humanlayer-docs-12", "humanlayer-docs-20" ] }, { "productId": "humanlayer", "storyId": "connect-github-repo", "verdict": "full", "quality": 8, "confidence": "medium", "rationale": "Docs describe connecting GitHub to create tasks from issues and link artifacts back (humanlayer-docs-16), plus agent sessions can access code via configured workspaces/repos (humanlayer-docs-8, humanlayer-docs-11) and open draft PRs directly from the session UI (humanlayer-docs-19). missing for 10: independent/hands-on corroboration of the GitHub connection flow and PR-opening working end-to-end beyond first-party docs.", "evidenceIds": [ "humanlayer-docs-16", "humanlayer-docs-19", "humanlayer-docs-8", "humanlayer-docs-11" ] }, { "productId": "humanlayer", "storyId": "daily-task-quota-limits", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "The evidence pack has no mention of plan-based daily task/concurrency limits or any admin controls for managing such limits; only a vague reference to 'Advanced Metrics for all paid plans' which covers usage/cost/productivity viewing, not concurrency or daily task limits management.", "evidenceIds": [ "humanlayer-docs-20" ] }, { "productId": "humanlayer", "storyId": "delegate-sessions-for-teammates", "verdict": "none", "quality": 0, "confidence": "low", "rationale": "The evidence describes tasks, sessions, integrations, and org-wide metrics access but never mentions an engineering-lead or admin creating/assigning agent sessions on behalf of another named user in the organization. No account-delegation, impersonation, or 'assign session to teammate' capability is documented.", "evidenceIds": [] }, { "productId": "humanlayer", "storyId": "describe-feature-in-plain-language", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "HumanLayer's task model (docs-5) and workflow-phase selection (docs-6, docs-10) show that a task is created and can be routed through 'Oneshot' for small clear changes or heavier RPI/PRD-oriented flows for ambiguous work, and RPI sub-agents (docs-9) imply a research→plan→implement pipeline that turns a description into a scoped plan. However, there is no explicit walkthrough showing a raw plain-language bug/feature description being automatically parsed into a scoped implementation task end-to-end, and integrations (Jira/Linear/GitHub) mostly create tasks from existing tickets rather than free-form language input. Missing for 10: a concrete example or tutorial of plain-language-to-scoped-task conversion, and independent/hands-on confirmation that this pipeline works as described.", "evidenceIds": [ "humanlayer-docs-5", "humanlayer-docs-6", "humanlayer-docs-9", "humanlayer-docs-10" ] }, { "productId": "humanlayer", "storyId": "design-to-code-handoff", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "HumanLayer's evidence is entirely about developer-facing workflows: running coding sessions, connecting Jira/Linear/GitHub/Slack, managing remote daemons, and CLI automation for engineers overseeing coding agents. Nothing in the pack shows a mockup/design import capability, a no-code interface, or any path for a non-engineer product manager to turn a design into a working implementation without engineering involvement — in fact the workflow language (RPI, PRD-oriented, Oneshot) and workspace configs assume an engineering operator. Missing for evidence of delivering this story: mockup/design ingestion, PM-oriented no-code UI, and any case study of a non-engineer shipping code end-to-end.", "evidenceIds": [ "humanlayer-docs-1", "humanlayer-docs-5", "humanlayer-docs-6", "humanlayer-docs-10" ] }, { "productId": "humanlayer", "storyId": "diff-review-before-pr", "verdict": "partial", "quality": 7, "confidence": "medium", "rationale": "Release notes explicitly reference a diff view and 'draft PR creation' workflow (docs-19, docs-21), and tasks include 'One place for comments and review' (docs-5), indicating a review-before-PR mechanism. However, there's no detailed documentation of an explicit approve/reject gate tied specifically to diff review prior to PR creation, and community discussion raises concerns about automation bias in approvals (comm-3) without disputing the core capability. Missing for 10: explicit step-by-step approval workflow docs, independent hands-on verification of the diff-review-then-approve flow, and clarity on how rejection/edits are handled before PR creation.", "evidenceIds": [ "humanlayer-docs-19", "humanlayer-docs-21", "humanlayer-docs-5", "humanlayer-comm-6", "humanlayer-comm-3" ] }, { "productId": "humanlayer", "storyId": "end-to-end-feature-implementation", "verdict": "partial", "quality": 5, "confidence": "medium", "rationale": "HumanLayer clearly supports end-to-end autonomous coding sessions (Oneshot, RPI, PRD-Oriented workflows) that implement tasks using agents like Claude Code and Codex, including structured phases and sub-agents for research/plan/implement, which implies substantial feature work can be delegated (humanlayer-docs-6, humanlayer-docs-9, humanlayer-docs-10, humanlayer-docs-14). However, no evidence explicitly confirms the agent writes or runs tests as part of the workflow, and no hands-on account demonstrates a full feature-plus-tests delivery. Missing for 10: explicit documentation or example showing test generation/execution as part of the implementation flow, and independent verification of end-to-end feature completion including tests.", "evidenceIds": [ "humanlayer-docs-6", "humanlayer-docs-9", "humanlayer-docs-10", "humanlayer-docs-14", "humanlayer-docs-5" ] }, { "productId": "humanlayer", "storyId": "enterprise-sso-licensing", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "No evidence pack item mentions SSO, enterprise licensing, org-wide rollout, or commercial support tiers; docs cover tutorials, integrations, and workflow features only, and community discussion focuses on pricing/skepticism unrelated to enterprise licensing terms.", "evidenceIds": [] }, { "productId": "humanlayer", "storyId": "feedback-to-task-conversion", "verdict": "partial", "quality": 4, "confidence": "low", "rationale": "HumanLayer supports creating tasks from external issue trackers (Jira, Linear, GitHub) which could serve as a proxy for user feedback submissions, and tasks include shared files/scope info, but there's no evidence of a dedicated feature for ingesting raw user feedback (e.g., support tickets, survey responses) and auto-structuring it into a task with a proposed scope specifically tailored for PM workflows. missing for 10: dedicated feedback-ingestion mechanism, explicit 'proposed scope' generation from unstructured feedback text, PM-specific workflow templates, and any hands-on/community validation of this specific use case.", "evidenceIds": [ "humanlayer-docs-3", "humanlayer-docs-16", "humanlayer-docs-17", "humanlayer-docs-5" ] }, { "productId": "humanlayer", "storyId": "headless-ci-execution", "verdict": "full", "quality": 8, "confidence": "medium", "rationale": "Docs explicitly describe `humanlayer automation run` for running Cloud-visible coding sessions from CI jobs, cron machines, or scripts, plus launch tokens for non-interactive/non-PTY execution suited to headless environments. This directly matches running an agent headlessly in CI/CD and shell scripts. Missing for 10: independent/hands-on verification of CI usage and concrete pipeline examples (e.g. GitHub Actions config).", "evidenceIds": [ "humanlayer-docs-12", "humanlayer-docs-13", "humanlayer-docs-7" ] }, { "productId": "humanlayer", "storyId": "isolated-sandbox-execution", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "HumanLayer docs describe running sessions on remote hosts (cloud VM, workstation, private-network machine) and automation environments, but there is no mention of an isolated/sandboxed execution environment for running code or installing dependencies safely — the host selection is about access/credentials, not isolation guarantees. Missing for full/partial: any explicit sandbox, container, or isolation mechanism; no evidence of dependency-install safety controls.", "evidenceIds": [ "humanlayer-docs-7", "humanlayer-docs-12", "humanlayer-docs-13" ] }, { "productId": "humanlayer", "storyId": "issue-tracker-sync", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "HumanLayer documents native Jira Cloud and Linear integrations that create tasks from tickets and sync/link status back to source issues, directly matching the story for those trackers. However, there is no evidence of ClickUp or Monday.com integrations, so the story is only partially delivered. Missing for 10: ClickUp integration docs, Monday.com integration docs, independent/hands-on verification of ticket sync working in practice.", "evidenceIds": [ "humanlayer-docs-3", "humanlayer-docs-17" ] }, { "productId": "humanlayer", "storyId": "live-agent-activity-monitoring", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "HumanLayer's docs describe remote-daemon control from app.humanlayer.com (including from a phone), live multiplayer session viewing with presence/read-only viewing, and task/session history — all suggesting real-time visibility into agent activity. However, there's no explicit documentation of a dedicated live status/log stream or dashboard showing granular agent state (e.g., current tool call, progress bar) beyond session/task views. missing for 10: explicit real-time status/log streaming documentation, independent hands-on confirmation of live monitoring UX.", "evidenceIds": [ "humanlayer-docs-2", "humanlayer-docs-18", "humanlayer-docs-7", "humanlayer-docs-5" ] }, { "productId": "humanlayer", "storyId": "managed-cloud-execution", "verdict": "partial", "quality": 5, "confidence": "medium", "rationale": "HumanLayer offers a cloud control plane (app.humanlayer.com, automation sessions marked 'Cloud-visible', advanced metrics for paid plans) that lets users monitor and manage agent runs remotely, suggesting a managed service layer. However, docs explicitly state the actual agent execution still runs on a host that the user must select and operate—'a cloud VM, workstation, or private-network machine'—meaning the developer still needs to provision/manage backend compute for the daemon rather than HumanLayer fully hosting execution. Missing for 10: evidence of a fully HumanLayer-operated compute backend (no user-managed VM/daemon required), and independent confirmation of a true zero-ops managed runtime.", "evidenceIds": [ "humanlayer-docs-2", "humanlayer-docs-7", "humanlayer-docs-12", "humanlayer-docs-13", "humanlayer-docs-20" ] }, { "productId": "humanlayer", "storyId": "manual-model-override", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "Docs show explicit model/backend selection — choosing Codex (\"install, authenticate, select, and check Codex\") or running Claude via Amazon Bedrock instead of the Anthropic API, plus subagent model registration — indicating an engineering lead can pick a specific model rather than a default. However, there is no explicit documentation of an 'automatic' default-selection mode being toggled off, so the framing of 'switching away from automatic' isn't directly evidenced. Missing for 10: explicit mention of an automatic/default model-selection setting and a documented UI/CLI flag to override it, independent confirmation of model-switching behavior.", "evidenceIds": [ "humanlayer-docs-14", "humanlayer-docs-15", "humanlayer-docs-9" ] }, { "productId": "humanlayer", "storyId": "mid-task-takeover", "verdict": "full", "quality": 7, "confidence": "medium", "rationale": "Docs describe tasks with session/history continuity 'across agents and workstations', remote control from app.humanlayer.com on any machine or phone, and CLI-driven remote daemons for terminal/server contexts, all pointing to genuine hand-off of in-progress work between editor (Claude Code), terminal (remote daemon/CLI), and browser (web app). Live multiplayer draft recovery further shows shared/continuable session state. missing for 10: independent/hands-on confirmation of a live takeover mid-task, and explicit description of an in-editor (IDE plugin) takeover UI rather than just CLI/app/web.", "evidenceIds": [ "humanlayer-docs-1", "humanlayer-docs-2", "humanlayer-docs-5", "humanlayer-docs-7", "humanlayer-docs-13", "humanlayer-docs-18" ] }, { "productId": "humanlayer", "storyId": "mobile-approval-of-decisions", "verdict": "full", "quality": 8, "confidence": "medium", "rationale": "Docs explicitly describe controlling and continuing a running agent task from a phone via app.humanlayer.com, with tasks providing a shared review/comment history across devices, directly matching the phone-approval-while-agent-continues story; community sentiment corroborates the human-approval-of-agent-actions use case. Missing for 10: a hands-on/independent account specifically confirming the mobile approval UI in practice, and explicit documentation of an 'approve/deny decision' action (vs. general task control) on mobile.", "evidenceIds": [ "humanlayer-docs-2", "humanlayer-docs-5", "humanlayer-docs-7", "humanlayer-comm-6" ] }, { "productId": "humanlayer", "storyId": "one-click-repo-access", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "Docs describe GitHub/Jira/Linear integrations for creating tasks from issues, but connecting a repo requires selecting a host, configuring workspace.json/workspace.local.json, and setting up remote daemons or multi-repo workspaces — none of this is framed as a one-click, no-setup install. No evidence pack item claims or demonstrates a one-click repo access flow.", "evidenceIds": [ "humanlayer-docs-7", "humanlayer-docs-8", "humanlayer-docs-11", "humanlayer-docs-16" ] }, { "productId": "humanlayer", "storyId": "openness-api-parity", "verdict": "partial", "quality": 3, "confidence": "medium", "rationale": "HumanLayer offers a CLI (`humanlayer automation run`) and remote daemon controls that let you launch and manage sessions outside the UI, but there is no documented public API/OpenAPI spec (both openapi.json and llms.txt probes 404), and UI-only features like live multiplayer drafts, keyboard navigation, and image paste have no CLI/API equivalent documented. missing for 10: a documented REST/GraphQL API or OpenAPI spec, confirmation that all UI actions (draft PRs, multiplayer editing, metrics views) are exposed programmatically, and independent verification of API-UI parity.", "evidenceIds": [ "humanlayer-probe-3", "humanlayer-docs-12", "humanlayer-docs-13", "humanlayer-probe-1", "humanlayer-probe-2", "humanlayer-docs-18", "humanlayer-docs-21" ] }, { "productId": "humanlayer", "storyId": "openness-full-export", "verdict": "none", "quality": 0, "confidence": "low", "rationale": "No evidence of data export functionality or open-format data portability; docs focus on tasks, workflows, and integrations, with no mention of exporting user data or account deletion/leaving.", "evidenceIds": [] }, { "productId": "humanlayer", "storyId": "openness-open-license", "verdict": "none", "quality": 0, "confidence": "low", "rationale": "No evidence pack item mentions an open-source license or public source repository for HumanLayer; all evidence is docs, community discussion, or probes about APIs/CLI. Absence of evidence for this applicable axis yields none.", "evidenceIds": [] }, { "productId": "humanlayer", "storyId": "openness-self-host", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "Evidence shows HumanLayer's daemon/agent execution can run on a user-controlled host (cloud VM, workstation, private network), but the core control plane is explicitly tied to the hosted app.humanlayer.com service — no docs describe self-hosting that core product. missing for 10: no self-hosted control-plane/server option, no on-prem deployment guide, no Docker/Helm chart or license for running the full stack independently.", "evidenceIds": [ "humanlayer-docs-2", "humanlayer-docs-7", "humanlayer-docs-13" ] }, { "productId": "humanlayer", "storyId": "privacy-data-residency", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "No evidence in the pack addresses data region/residency selection or storage location controls; docs cover hosts, integrations, and workflows but never data residency options.", "evidenceIds": [] }, { "productId": "humanlayer", "storyId": "privacy-no-training", "verdict": "none", "quality": 0, "confidence": "low", "rationale": "No evidence in the pack addresses data usage for AI model training, opt-out policies, or privacy commitments regarding training data; the docs focus on task workflows, integrations, and remote sessions.", "evidenceIds": [] }, { "productId": "humanlayer", "storyId": "privacy-retention-controls", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "No evidence pack items mention data retention policies, deletion controls, or privacy settings for HumanLayer; the docs focus on task/session workflows, integrations, and remote daemons rather than data governance.", "evidenceIds": [] }, { "productId": "humanlayer", "storyId": "privacy-telemetry-optout", "verdict": "none", "quality": 0, "confidence": "low", "rationale": "No evidence in the pack addresses telemetry, usage tracking, or opt-out controls for HumanLayer; docs cover integrations, workflows, and CLI usage but nothing about privacy/telemetry settings. Missing for 10: any mention of telemetry collection, opt-out mechanism, privacy policy, or data-handling documentation.", "evidenceIds": [] }, { "productId": "humanlayer", "storyId": "query-repo-documentation", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "HumanLayer is a human-in-the-loop agent orchestration/approval platform, not a code documentation generator or repo-search tool; nothing in the evidence describes generating or querying documentation for repositories. This is a category mismatch, not a missing feature.", "evidenceIds": [] }, { "productId": "humanlayer", "storyId": "repo-readiness-report", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "No evidence of any readiness-report or repository-readiness-assessment feature; HumanLayer's docs focus on task/session management, integrations, and human-in-the-loop workflows, not a diagnostic scan of a repo's agent-readiness.", "evidenceIds": [] }, { "productId": "humanlayer", "storyId": "security-alert-remediation", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "HumanLayer's docs show generic task creation from GitHub/Jira/Linear issues and draft PR creation from agent sessions, but there is no evidence of any security-alert-specific validation or automated vulnerability remediation workflow (no CVE, dependency-alert, or security-scanner integration mentioned).", "evidenceIds": [] }, { "productId": "humanlayer", "storyId": "self-hosted-agent-deployment", "verdict": "partial", "quality": 7, "confidence": "medium", "rationale": "Docs describe running the remote daemon on a cloud VM, workstation, or private-network machine that you control (humanlayer-docs-7), plus automation sessions from CI/cron/server environments (humanlayer-docs-12) and launch tokens for bootstrap scripts (humanlayer-docs-13), showing self-hosted deployment flexibility. However there is no explicit mention of container/Docker deployment or an official container image, and no independent verification of self-hosted setups at scale. Missing for 10: explicit container/Docker packaging docs, independent hands-on confirmation of self-hosted deployments.", "evidenceIds": [ "humanlayer-docs-7", "humanlayer-docs-2", "humanlayer-docs-12", "humanlayer-docs-13" ] }, { "productId": "humanlayer", "storyId": "steer-active-session", "verdict": "partial", "quality": 4, "confidence": "low", "rationale": "HumanLayer's task/session model implies ongoing interaction with agents across a task's lifetime (docs-5) and release notes mention live prompt collaboration during sessions (docs-18), suggesting some capacity to interact with an active session, but there is no explicit documentation of sending mid-session follow-up instructions to steer a running agent without restarting it. missing for 10: explicit docs on injecting new instructions into a live/running session, confirmation the agent incorporates such input without restart, and independent/hands-on verification of this steering behavior.", "evidenceIds": [ "humanlayer-docs-5", "humanlayer-docs-18" ] }, { "productId": "humanlayer", "storyId": "task-completion-notifications", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "HumanLayer supports Slack/GitHub/Jira/Linear integrations that push task artifact updates and human-in-the-loop approvals, implying notification when tasks progress or need input, and its core design centers on human oversight of agent work. However, there is no explicit documentation of a dedicated 'task complete' or 'needs input' notification/alert mechanism (e.g., push notification, email, or webhook triggered specifically on completion/approval-required events) beyond generic artifact updates in Slack. missing for 10: explicit completion/needs-input notification docs, evidence of notification latency/reliability, independent/hands-on confirmation of notification delivery.", "evidenceIds": [ "humanlayer-docs-4", "humanlayer-docs-16", "humanlayer-docs-17", "humanlayer-docs-3", "humanlayer-comm-6" ] }, { "productId": "humanlayer", "storyId": "tiered-autonomy-levels", "verdict": "partial", "quality": 5, "confidence": "medium", "rationale": "HumanLayer's workflow-phases doc shows tiered approaches (Oneshot for low-risk changes vs. more review for unclear/risky changes) and skills-workflows lets users select Oneshot/RPI/PRD-Oriented/Freeform modes, which map to different levels of autonomy vs. oversight. However, there's no explicit documentation of a formal 'autonomy level' setting per agent/task with configurable confirmation thresholds, and no independent evidence confirming this tiered control works as an oversight mechanism in practice. missing for 10: explicit named autonomy-tier configuration (e.g., low/medium/high) tied to confirmation gating, evidence of engineering-lead-level policy controls across a team, and independent/hands-on validation that these workflow phases actually reduce unnecessary confirmations without sacrificing safety.", "evidenceIds": [ "humanlayer-docs-6", "humanlayer-docs-10", "humanlayer-docs-9" ] }, { "productId": "humanlayer", "storyId": "trigger-agent-on-build-failure", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "HumanLayer docs explicitly describe `humanlayer automation run` for running a Cloud-visible coding session 'from any automation environment — a CI job, a cron machine, or a script on a server,' plus launch tokens for non-interactive command execution, directly supporting CI/CD-triggered agent runs. However, there is no explicit example or integration guide showing a CI failure (broken build/failing test) triggering the agent to diagnose and fix it, nor independent/hands-on confirmation of this workflow. missing for 10: a concrete CI/CD pipeline example tied to build/test failures, evidence of automatic failure detection triggering the agent, and independent verification of this automation flow working in practice.", "evidenceIds": [ "humanlayer-docs-12", "humanlayer-docs-13", "humanlayer-docs-7" ] }, { "productId": "humanlayer", "storyId": "visual-context-for-tasks", "verdict": "partial", "quality": 4, "confidence": "medium", "rationale": "Docs confirm images can be pasted into the new task composer as attachments (humanlayer-docs-22), which supports attaching a screenshot to a task, but there is no evidence of markup/annotation tooling or of the agent parsing visual annotations to implement a corresponding UI change. Missing for 10: annotation/markup capability for screenshots, evidence the agent interprets visual markup into a specific implementation, and any hands-on example of this workflow succeeding.", "evidenceIds": [ "humanlayer-docs-22" ] }, { "productId": "jules", "storyId": "agentic-agent-docs", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "Probes show no llms.txt, docs.md, or machine-readable API spec exist at expected paths, and no evidence Jules can be pointed at such agent-oriented documentation formats; Jules does support AGENTS.md for repo context but that's a different mechanism than consuming llms.txt-style docs.", "evidenceIds": [ "jules-probe-1", "jules-probe-2", "jules-probe-3" ] }, { "productId": "jules", "storyId": "agentic-ai-insights", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "Jules generates AI-derived plans, diffs, and PR suggestions based on analysis of the user's repository data, which is the coding-agent analog of 'AI-generated insights/suggestions from your data.' Community evidence is mixed on the quality of these suggestions, with some praising usable PRs and others criticizing low-quality output on complex codebases. Missing for 10: no evidence of broader analytics-style insights beyond code-change suggestions, and no independent benchmarking confirming insight quality across use cases.", "evidenceIds": [ "jules-docs-4", "jules-docs-7", "jules-docs-8", "jules-comm-6", "jules-comm-13" ] }, { "productId": "jules", "storyId": "agentic-autonomous-automation", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "Jules supports background/autonomous execution via GitHub label-triggered tasks and a public API for building custom automations (e.g., automated bug-fixing, code review) with an optional automationMode for auto-PR creation, and it runs tasks in a cloud VM without needing the user present. However, docs also show a human-approval gate (plan approval, PR review) rather than fully hands-off automation, and community reports note tasks getting stuck, hitting limits, or needing babysitting, undercutting reliability of unattended runs. Missing for 10: evidence of true scheduled/cron-style recurring automations, and independent confirmation that automations run to completion without manual intervention.", "evidenceIds": [ "jules-docs-2", "jules-docs-9", "jules-docs-11", "jules-docs-4", "jules-comm-16", "jules-comm-3", "jules-comm-14" ] }, { "productId": "jules", "storyId": "agentic-builtin-assistant", "verdict": "full", "quality": 8, "confidence": "high", "rationale": "Jules is exactly this kind of built-in agentic assistant: users delegate coding tasks via GitHub issue labels, the web app, or API, and Jules autonomously plans, clones repos, runs in a VM, and returns diffs/PRs for approval (jules-docs-2,3,4,7,8,9). Community evidence corroborates real-world task delegation and usable output (jules-comm-2, jules-comm-11, jules-comm-15, jules-comm-16), though mixed reports of loops, babysitting needs, and reliability issues (jules-comm-14, jules-comm-18) temper quality. Missing for 10: independent benchmarking of task success rates and evidence of consistent reliability at scale.", "evidenceIds": [ "jules-docs-2", "jules-docs-3", "jules-docs-4", "jules-docs-7", "jules-docs-8", "jules-docs-9", "jules-comm-2", "jules-comm-11", "jules-comm-15", "jules-comm-16", "jules-comm-14", "jules-comm-18" ] }, { "productId": "jules", "storyId": "agentic-headless", "verdict": "full", "quality": 7, "confidence": "medium", "rationale": "Jules provides a public API with API keys for creating custom workflows, sending tasks programmatically, and an optional automationMode to auto-create PRs, plus a community example of building an MCP server to dispatch tasks from another tool — this directly supports headless/CI-style automation. Missing for 10: no official CI/CD integration examples (e.g. GitHub Actions), no documented webhook/polling pattern for task completion, and no independent verification of reliability at scale in automated pipelines.", "evidenceIds": [ "jules-docs-9", "jules-docs-10", "jules-docs-11", "jules-docs-12", "jules-comm-19" ] }, { "productId": "jules", "storyId": "agentic-mcp-client", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "No evidence that Jules can connect to or consume external MCP servers as tool providers; the only MCP-related evidence (jules-comm-19) describes someone building an MCP server that calls INTO the Jules API, which is the reverse integration direction, not Jules plugging in MCP servers for its own tool use.", "evidenceIds": [ "jules-comm-19" ] }, { "productId": "jules", "storyId": "agentic-mcp-server", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "Jules is itself a coding agent (client role), and the evidence only shows a REST API plus a third-party/community-built MCP server (jules-comm-19), not an official first-party MCP server exposing Jules as a tool endpoint. Per the agent-role rule, this axis is out of category rather than a failed capability.", "evidenceIds": [ "jules-docs-9", "jules-comm-19" ] }, { "productId": "jules", "storyId": "agentic-nl-commands", "verdict": "full", "quality": 8, "confidence": "high", "rationale": "Jules is designed around natural-language task submission and messaging: users submit a task description, Jules generates a plan, and you can send follow-up messages to the agent via API or web app (jules-docs-4, jules-docs-12). Community reports confirm real-world use of NL prompts to drive coding tasks, including iterative feedback and error-message follow-ups (jules-comm-2, jules-comm-15, jules-comm-16). Missing for 10: no detailed documentation of the full range/complexity of natural-language commands supported (e.g., multi-step conversational control, command reference) and no independent benchmark of NL command robustness beyond anecdotal reports.", "evidenceIds": [ "jules-docs-4", "jules-docs-12", "jules-comm-2", "jules-comm-15", "jules-comm-16" ] }, { "productId": "jules", "storyId": "agentic-official-cli", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "Evidence shows Jules offers a web app, GitHub integration, and a REST API for custom workflows, but no official CLI tool is documented or mentioned anywhere in the evidence pack.", "evidenceIds": [ "jules-docs-9", "jules-docs-10" ] }, { "productId": "jules", "storyId": "agentic-public-api", "verdict": "full", "quality": 8, "confidence": "high", "rationale": "Jules ships a documented public API (developers.google.com/jules/api) with API key management, custom workflows, sending messages to the agent, and automationMode config; a community user independently built an MCP server on top of the API confirming real-world programmatic access. Missing for 10: no discoverable OpenAPI/swagger spec or llms.txt (probes returned 404s), reducing machine-readability/self-service tooling confidence.", "evidenceIds": [ "jules-docs-9", "jules-docs-10", "jules-docs-11", "jules-docs-12", "jules-comm-19", "jules-probe-3" ] }, { "productId": "jules", "storyId": "agentic-scoped-keys", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "Jules documents a basic API key creation flow (max 3 keys) but provides no evidence of scoped or least-privilege permission controls — no mention of scopes, roles, or restricted-access tokens for the agent.", "evidenceIds": [ "jules-docs-9", "jules-docs-10", "jules-docs-11" ] }, { "productId": "jules", "storyId": "agentic-sdks", "verdict": "full", "quality": 7, "confidence": "medium", "rationale": "Jules ships an official public API with documented endpoints, API key management, and use cases for building custom workflows/integrations, and a community member confirms building a personal MCP server against the Jules API. missing for 10: no official OpenAPI/SDK spec discoverable (openapi probes 404), no first-party language SDKs mentioned, and no independent SDK-quality corroboration beyond one community integration example.", "evidenceIds": [ "jules-docs-9", "jules-docs-10", "jules-docs-11", "jules-docs-12", "jules-comm-19", "jules-probe-3" ] }, { "productId": "jules", "storyId": "agentic-webhooks", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "Evidence shows Jules has an API for creating tasks/sending messages, notifications for task completion, and GitHub integration, but there is no mention of webhooks or event subscription mechanisms anywhere in the docs or community evidence. Missing for 10: any documentation of a webhook endpoint, event subscription API, or push-based notification mechanism to external systems.", "evidenceIds": [ "jules-docs-9", "jules-docs-10", "jules-docs-12", "jules-docs-6" ] }, { "productId": "jules", "storyId": "ai-automated-test-generation-on-change", "verdict": "partial", "quality": 4, "confidence": "low", "rationale": "Jules runs code changes in an isolated VM and generates diffs/PRs for review, and one community report notes it is 'good at making tests for very specific/isolated functions,' implying some test generation capability, but there is no first-party documentation describing an explicit test-generate-and-run validation loop before proposing changes. missing for 10: official docs on automated test generation/execution as a validation step, evidence of test results being surfaced to users before PR creation, and independent confirmation across varied codebases.", "evidenceIds": [ "jules-docs-3", "jules-docs-4", "jules-comm-11" ] }, { "productId": "jules", "storyId": "ai-automatic-model-routing", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "No evidence Jules performs automatic model routing per task; docs mention only a single flagship model (Gemini 3 Pro) as 'priority access,' with no mention of routing prompts to different models based on suitability. Missing for 10: any documentation of multi-model routing logic, model-selection criteria, or automatic switching between models.", "evidenceIds": [ "jules-docs-14" ] }, { "productId": "jules", "storyId": "ai-issue-triage", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "Jules is a coding agent that executes tasks assigned via label or API, but there is no evidence of automatic issue triage, severity classification, or routing to owners — the closest feature is manual label-based task assignment, not automated triage. Missing for 10: any mention of severity scoring, triage logic, or owner-routing automation.", "evidenceIds": [ "jules-docs-2", "jules-docs-9" ] }, { "productId": "jules", "storyId": "api-interactive-docs", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "Jules documents an API (create tasks, send messages, API keys) but there is no evidence of an interactive API reference or runnable code examples; probes for openapi/swagger specs all returned 404s, suggesting no such interactive reference exists.", "evidenceIds": [ "jules-docs-9", "jules-docs-10", "jules-docs-12", "jules-probe-3" ] }, { "productId": "jules", "storyId": "api-machine-spec", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "Jules ships a documented REST API (jules-docs-9..12) but there is no evidence of a downloadable OpenAPI/Swagger spec; explicit probes for llms.txt, docs.md, and standard OpenAPI paths all returned 404 (jules-probe-1, jules-probe-2, jules-probe-3), confirming no machine-readable spec is published.", "evidenceIds": [ "jules-docs-9", "jules-docs-10", "jules-probe-1", "jules-probe-2", "jules-probe-3" ] }, { "productId": "jules", "storyId": "api-sandbox", "verdict": "partial", "quality": 5, "confidence": "medium", "rationale": "Jules runs tasks in an isolated cloud VM that clones the repo, meaning code changes happen in a sandboxed environment rather than directly on production, and PRs must be reviewed/approved before merging to the real branch. However, this is a code-sandbox for making changes, not a dedicated 'test against sandbox data/environment' feature, and there's no evidence of test-data isolation, staging environment provisioning, or explicit protection of production data/services beyond the VM/PR review flow. missing for 10: explicit sandbox test-data isolation, staging/production separation guarantees, independent verification that production systems are never touched.", "evidenceIds": [ "jules-docs-3", "jules-docs-4", "jules-docs-7", "jules-docs-8" ] }, { "productId": "jules", "storyId": "api-versioning-policy", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "Jules has a public API (jules-docs-9, jules-docs-10) but there is no evidence of API versioning scheme or a documented deprecation policy; probes for OpenAPI spec/docs.md/llms.txt all returned 404 (jules-probe-1, jules-probe-2, jules-probe-3), suggesting no formal machine-readable API contract or lifecycle documentation is available.", "evidenceIds": [ "jules-docs-9", "jules-docs-10", "jules-probe-1", "jules-probe-2", "jules-probe-3" ] }, { "productId": "jules", "storyId": "approve-plan-before-execution", "verdict": "full", "quality": 8, "confidence": "medium", "rationale": "Jules docs explicitly state the agent generates a plan upon task submission that users can review and approve before any code changes are made, directly matching the story. No independent/hands-on evidence specifically confirms or contradicts this plan-approval step, though other workflow claims (diffs, PRs) are corroborated by community mentions. Missing for 10: independent/hands-on confirmation of the plan-review step specifically, and more detail on what the plan interface looks like.", "evidenceIds": [ "jules-docs-4", "jules-docs-3", "jules-docs-7" ] }, { "productId": "jules", "storyId": "approve-task-contract", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "Jules generates a plan that the user can review and approve before any code changes are made, giving an engineering-lead a gate before modification, and it also produces diffs/PRs requiring approval before merge. However, this is a general 'plan approval' UX rather than a formal scope/contract sign-off workflow tailored for engineering-lead governance (e.g., no evidence of role-based approval gates, policy enforcement, or blocking unauthorized starts). missing for 10: explicit lead/role-based approval gating before task execution starts, evidence of enforceable contract/scope definitions beyond a plan preview, independent confirmation that the plan-approval step reliably blocks modification.", "evidenceIds": [ "jules-docs-4", "jules-docs-7", "jules-docs-8" ] }, { "productId": "jules", "storyId": "assign-task-from-ticket", "verdict": "full", "quality": 8, "confidence": "high", "rationale": "Jules explicitly supports assigning tasks directly from GitHub issues via a 'jules' label, and API/community evidence corroborates task dispatch and PR creation workflows. Missing for 10: evidence of ticket-tracker integrations beyond GitHub (e.g., Jira/Linear) and independent hands-on confirmation of the label-to-task flow itself.", "evidenceIds": [ "jules-docs-2", "jules-docs-4", "jules-docs-8", "jules-comm-6" ] }, { "productId": "jules", "storyId": "auto-approve-all-actions", "verdict": "partial", "quality": 4, "confidence": "low", "rationale": "Jules's default workflow requires manual approval at multiple stages (plan approval, diff review, PR approval), but the API docs mention an optional `automationMode` field that changes default behavior around automatic PR creation, suggesting some configurable auto-approve path exists via API rather than the standard UI flow. Missing for 10: explicit documentation of a setting that suppresses plan-approval and diff-review confirmations entirely, and any hands-on/community confirmation that this automation mode actually skips human checkpoints.", "evidenceIds": [ "jules-docs-4", "jules-docs-7", "jules-docs-8", "jules-docs-11" ] }, { "productId": "jules", "storyId": "auto-fix-readiness-gaps", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "Jules can be assigned generic bug-fix/coding tasks and reads an AGENTS.md file if present, but there is no evidence of it detecting or automatically remediating 'agent-readiness' quality-gate criteria (e.g., missing/invalid AGENTS.md, agent-compatibility checks) as a review gate — it only consumes such files, it doesn't audit or fix them as a compliance gate.", "evidenceIds": [ "jules-docs-5", "jules-docs-3", "jules-docs-4", "jules-comm-15" ] }, { "productId": "jules", "storyId": "auto-open-pr-on-completion", "verdict": "full", "quality": 8, "confidence": "high", "rationale": "Jules natively creates a PR when a task completes (jules-docs-8), and the API exposes an `automationMode` field to configure automatic PR creation, defaulting to off unless explicitly configured (jules-docs-11), directly matching the story of configuring automatic PR-on-completion. Community reports corroborate that Jules produces usable PRs and users regularly review PRs it opens (jules-comm-6, jules-comm-12, jules-comm-16). missing for 10: independent hands-on verification of the automationMode toggle specifically, and more detail on configuration options/edge cases (e.g., partial completions, failed tasks) affecting whether a PR is always opened.", "evidenceIds": [ "jules-docs-8", "jules-docs-11", "jules-comm-6", "jules-comm-12", "jules-comm-16" ] }, { "productId": "jules", "storyId": "automated-pr-review", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "Jules is documented as a task-execution agent that clones repos, makes code changes, and creates its own PRs for approval — not as a bot that automatically reviews incoming pull requests with inline comments. The only tangential mention is jules-docs-9's passing reference to 'automate tasks like ... code reviews' via API, but there is no documentation of an automatic inline-comment review gate applied to every PR.", "evidenceIds": [ "jules-docs-9", "jules-docs-8", "jules-docs-7" ] }, { "productId": "jules", "storyId": "automatic-environment-setup", "verdict": "full", "quality": 8, "confidence": "high", "rationale": "Jules' core workflow is documented: it runs in a cloud VM, clones the repo, installs dependencies, and configures its environment automatically before making changes, corroborated by community usage reports of automated PR generation. missing for 10: independent technical verification of dependency-install robustness across complex/monorepo setups, and some community reports note environment/config confusion in bespoke or monorepo projects.", "evidenceIds": [ "jules-docs-3", "jules-docs-1", "jules-docs-4", "jules-comm-16", "jules-comm-1", "jules-comm-13" ] }, { "productId": "jules", "storyId": "automation-bulk-operations", "verdict": "partial", "quality": 5, "confidence": "medium", "rationale": "Jules exposes an API that lets users script custom workflows and dispatch tasks programmatically (jules-docs-9, jules-docs-12), and usage-limit tiers explicitly target 'power users & agent-heavy workflows' with up to 300 tasks (jules-docs-13), while community reports confirm it 'handles large quantities of tasks very well' and users built custom dispatch tooling via the API/MCP bridge (jules-comm-11, jules-comm-19). However, there's no documented native UI or endpoint for a single bulk action across many items (e.g., batch-applying one task to many repos/issues) — each task still appears to be created and reviewed individually via GitHub labels or API calls. Missing for 10: a documented batch/bulk endpoint or UI feature, first-party bulk-operation examples, and independent verification of true parallel bulk execution rather than just high per-day task volume.", "evidenceIds": [ "jules-docs-9", "jules-docs-12", "jules-docs-13", "jules-comm-11", "jules-comm-19" ] }, { "productId": "jules", "storyId": "automation-rules-engine", "verdict": "partial", "quality": 5, "confidence": "medium", "rationale": "Jules supports some event-driven automation — e.g., assigning a 'jules' label to a GitHub issue automatically triggers a task, and the API lets developers build custom workflows/automations around Jules (jules-docs-2, jules-docs-9). However, there's no evidence of a general rules/conditions engine where users define arbitrary triggers and actions beyond this specific label mechanism and API calls. Missing for 10: a configurable rules interface (conditions + multiple trigger types), documentation of scheduled/webhook-based triggers, and independent confirmation that automated rule-based triggering works reliably in practice.", "evidenceIds": [ "jules-docs-2", "jules-docs-9", "jules-docs-11" ] }, { "productId": "jules", "storyId": "automation-scheduled-jobs", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "Evidence covers task assignment via GitHub labels, API-driven custom workflows, and one-off task automation, but nothing describes recurring/scheduled jobs (e.g., cron-like triggers) — the API docs only mention creating tasks and sending messages, not recurrence.", "evidenceIds": [ "jules-docs-9", "jules-docs-10", "jules-docs-11", "jules-docs-12" ] }, { "productId": "jules", "storyId": "automation-versioned-workflows", "verdict": "none", "quality": 0, "confidence": "low", "rationale": "Jules produces PRs and diffs via GitHub but there is no evidence of a mechanism to version, review history of, or roll back automations/tasks themselves (as opposed to code changes tracked by git/GitHub). missing for 10: automation versioning/history feature, rollback/undo of Jules tasks, changelog or audit trail for automations.", "evidenceIds": [] }, { "productId": "jules", "storyId": "autonomous-bug-fixing", "verdict": "full", "quality": 8, "confidence": "high", "rationale": "Jules is designed to autonomously diagnose and fix issues: it clones the repo into a VM, generates a plan, makes code changes, and opens a PR for review, and can be triggered directly from a GitHub issue via the 'jules' label (a common bug-report workflow). Community evidence corroborates real-world bug-fixing use, including a case where it failed then successfully fixed the issue after being given the error message. Missing for 10: independent benchmarking on bug-fix success rate and more consistent evidence across complex codebases (some reports of failures/loops on harder tasks).", "evidenceIds": [ "jules-docs-2", "jules-docs-3", "jules-docs-4", "jules-docs-7", "jules-docs-8", "jules-comm-15", "jules-comm-16" ] }, { "productId": "jules", "storyId": "bring-your-own-model", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "No evidence that Jules supports bringing a custom LLM or third-party API key; it is tied to Google's own models (Gemini), with documentation only mentioning Jules's own API key for accessing Jules itself, not for configuring underlying model providers. missing for 10: any mention of BYO-LLM support, model selection options, or third-party API key configuration.", "evidenceIds": [ "jules-docs-9", "jules-docs-10", "jules-docs-14" ] }, { "productId": "jules", "storyId": "chat-with-agent-in-slack", "verdict": "partial", "quality": 4, "confidence": "low", "rationale": "Jules supports delegating tasks via a GitHub issue label ('jules') and via API messages to the agent, which is a form of task delegation but not the classic 'tag an agent in a chat thread to discuss' interaction pattern; one community user built a custom MCP server to dispatch Jules tasks from VS Code Copilot Chat, showing it's possible but not a native feature. missing for 10: native chat-thread tagging/mention UI, evidence of back-and-forth discussion in a thread before delegation, first-party support for Slack/Teams-style @mentions.", "evidenceIds": [ "jules-docs-2", "jules-docs-12", "jules-comm-19" ] }, { "productId": "jules", "storyId": "ci-failure-auto-fix", "verdict": "partial", "quality": 3, "confidence": "low", "rationale": "Jules offers building blocks—an API for custom automation that can 'automate tasks like bug fixing' and auto-create PRs via automationMode—but there is no direct evidence of a native integration that detects a failed CI workflow and automatically diagnoses/fixes it with a PR; this would require custom API wiring, not an out-of-box feature. Missing for 10: documented CI-failure trigger/integration, evidence of automatic diagnosis of CI logs, and case studies of this specific end-to-end flow.", "evidenceIds": [ "jules-docs-9", "jules-docs-11", "jules-docs-8", "jules-docs-2" ] }, { "productId": "jules", "storyId": "codebase-context-file", "verdict": "full", "quality": 7, "confidence": "medium", "rationale": "Jules automatically reads an AGENTS.md file in the repo root to guide its plans and code generation, directly matching the story of adding a context file describing codebase conventions. Missing for 10: no independent/hands-on confirmation of how well conventions in AGENTS.md actually improve plan/code relevance, and no detail on supported format/scope beyond the single doc mention.", "evidenceIds": [ "jules-docs-5", "jules-docs-4" ] }, { "productId": "jules", "storyId": "concurrent-task-execution", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "Jules architecture (cloud VMs per task, API for creating tasks, GitHub issue labeling) supports dispatching many tasks in parallel, and usage-limit docs explicitly reference 'agent-heavy workflows' with numeric caps (e.g. 300/60), and community reports (jules-comm-11, jules-comm-16) describe handling large task volumes successfully. However, other evidence shows real caveats: daily task limits were cut from 60 to 15 on the free plan (jules-comm-8), tasks can get stuck in loops with no stop button (jules-comm-14), and users report needing heavy babysitting (jules-comm-18), undercutting smooth high-throughput scaling. Missing for 10: dedicated documentation of a concurrency/queue dashboard, enterprise-tier concurrency guarantees, and independent benchmarks confirming reliable parallel execution at scale.", "evidenceIds": [ "jules-docs-13", "jules-docs-9", "jules-comm-11", "jules-comm-16", "jules-comm-8", "jules-comm-14", "jules-comm-18" ] }, { "productId": "jules", "storyId": "connect-github-repo", "verdict": "full", "quality": 9, "confidence": "high", "rationale": "Jules is built around GitHub repo access: docs describe cloning repos, GitHub issue label assignment, generating diffs/plans, and creating PRs that can be merged on GitHub, corroborated by community reports of receiving usable PRs from Jules-driven tasks. Missing for 10: independent verification of the full connect-repo setup flow and edge-case reliability across repo types.", "evidenceIds": [ "jules-docs-1", "jules-docs-2", "jules-docs-3", "jules-docs-7", "jules-docs-8", "jules-comm-6", "jules-comm-16" ] }, { "productId": "jules", "storyId": "daily-task-quota-limits", "verdict": "partial", "quality": 4, "confidence": "medium", "rationale": "Jules docs reference a usage-limits page with plan-tiered daily task numbers (e.g., 300/60) and community confirms daily task limits exist and change between releases (60→15 on free plan), showing plan-based limits are real and documented. However there's no evidence of an engineering-lead-facing dashboard or admin console to monitor team usage, set/adjust concurrency, or manage limits across a team — only a static limits reference page and anecdotal user experience of hitting caps. Missing for 10: team/org usage dashboard, per-user concurrency visibility, ability to configure or request limit changes, and any admin/lead-specific management UI.", "evidenceIds": [ "jules-docs-13", "jules-comm-8" ] }, { "productId": "jules", "storyId": "delegate-sessions-for-teammates", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "No evidence of any organization, team, or admin/delegation features allowing an engineering-lead to create sessions on behalf of other users; Jules docs only describe individual API keys, per-user GitHub connections, and personal task creation. Missing for 10: org/team management, delegated session creation, role-based admin controls, multi-user account provisioning.", "evidenceIds": [ "jules-docs-10", "jules-docs-9", "jules-docs-1" ] }, { "productId": "jules", "storyId": "describe-feature-in-plain-language", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "Jules docs clearly show a plain-language task submission flow that generates a reviewable/approvable plan before code changes, then produces a diff and PR (jules-docs-4, jules-docs-7, jules-docs-8), directly matching the intent-to-spec story. However community reports show inconsistent scoping quality — confusion in monorepos, endless loops, and needing heavy babysitting — indicating the automatic scoping doesn't always hold up in practice. Missing for 10: independent verification of plan/spec quality on complex codebases, and consistent evidence the generated plan reliably matches developer intent without back-and-forth correction.", "evidenceIds": [ "jules-docs-4", "jules-docs-7", "jules-docs-8", "jules-comm-13", "jules-comm-14", "jules-comm-18" ] }, { "productId": "jules", "storyId": "design-to-code-handoff", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "Evidence shows Jules operates on code repositories via GitHub issues/tasks, plans, diffs, and PRs, but there is no mention of accepting mockups/designs as input or any PM-oriented no-code workflow — usage still requires repo access, issue creation, and reviewing technical diffs/plans. Nothing in the pack demonstrates a design-to-implementation path bypassing engineering.", "evidenceIds": [ "jules-docs-1", "jules-docs-2", "jules-docs-4", "jules-docs-7", "jules-docs-8" ] }, { "productId": "jules", "storyId": "diff-review-before-pr", "verdict": "full", "quality": 9, "confidence": "high", "rationale": "Jules explicitly provides a diff of changes for review and approval before it creates/publishes a PR, with plan approval and diff approval steps documented; community mentions confirm PRs are generated for review. Missing for 10: independent hands-on confirmation of the diff-approval UI flow itself (comments focus on PR quality/output rather than the diff-review step specifically).", "evidenceIds": [ "jules-docs-4", "jules-docs-7", "jules-docs-8", "jules-comm-12" ] }, { "productId": "jules", "storyId": "end-to-end-feature-implementation", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "Jules is explicitly built for autonomous end-to-end coding: it clones the repo, generates a plan, modifies files, produces a diff, and opens a PR (jules-docs-3/4/7/8), and one user specifically notes it's 'good at making tests for very specific/isolated functions' (jules-comm-11), with others confirming usable PRs and time savings (jules-comm-6, jules-comm-16). However, community evidence shows significant caveats: it struggles on complex/monorepo codebases, gets stuck in loops without a stop button, needs heavy babysitting, and often fails to complete tasks unassisted (jules-comm-3, jules-comm-9, jules-comm-10, jules-comm-13, jules-comm-14, jules-comm-18). Missing for 10: consistent reliability across complex real-world features, broader evidence of comprehensive test coverage (not just isolated functions), and independent benchmarks confirming end-to-end success rate.", "evidenceIds": [ "jules-docs-3", "jules-docs-4", "jules-docs-7", "jules-docs-8", "jules-comm-11", "jules-comm-6", "jules-comm-16", "jules-comm-9", "jules-comm-13", "jules-comm-14", "jules-comm-18" ] }, { "productId": "jules", "storyId": "enterprise-sso-licensing", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "No evidence of enterprise licensing, SSO, or commercial support offerings; docs mention only individual API keys and usage-tier limits, and community discussion references a free beta plan, not enterprise deployment. missing for 10: SSO integration, enterprise licensing/contract terms, commercial support SLAs, org-wide admin/rollout tooling.", "evidenceIds": [ "jules-docs-10", "jules-docs-13", "jules-comm-4" ] }, { "productId": "jules", "storyId": "feedback-to-task-conversion", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "Jules's documented workflow is a coding-agent pipeline (clone repo, generate a plan, produce diffs/PRs) driven by developer-specified tasks or GitHub issues, not a mechanism for ingesting unstructured user-feedback and converting it into a structured task with proposed scope for a PM. No evidence shows feedback intake, requirement structuring, or scope proposal features aimed at product managers.", "evidenceIds": [ "jules-docs-4", "jules-docs-2", "jules-docs-8" ] }, { "productId": "jules", "storyId": "headless-ci-execution", "verdict": "partial", "quality": 5, "confidence": "medium", "rationale": "Jules exposes a public API (create tasks, send messages, optional automationMode) explicitly pitched for 'custom workflows' and embedding into other tools, which can be scripted/curled headlessly, and a community member built an MCP server hitting the API to dispatch tasks programmatically. However there's no documented CLI, no explicit CI/CD pipeline examples (e.g., GitHub Actions), and Jules always runs its own cloud VM rather than a lightweight headless process invokable inline in a shell script/pipeline step. missing for 10: official CLI or CI/CD pipeline integration docs (e.g. GitHub Actions step), examples of shell-script invocation, confirmation that API calls run synchronously enough for pipeline gating.", "evidenceIds": [ "jules-docs-9", "jules-docs-10", "jules-docs-11", "jules-docs-12", "jules-comm-19", "jules-comm-16" ] }, { "productId": "jules", "storyId": "isolated-sandbox-execution", "verdict": "full", "quality": 8, "confidence": "high", "rationale": "Jules explicitly runs in a cloud VM that clones code and installs dependencies, isolated from the user's local machine, with a plan-review step before code changes are made — directly matching the sandboxed execution story. Community reports corroborate it actually running tasks end-to-end (producing PRs) in this VM environment, though some criticize reliability/loops on complex codebases. Missing for 10: detailed docs on sandbox security boundaries (network isolation, resource limits) and independent security audit of the VM isolation.", "evidenceIds": [ "jules-docs-3", "jules-docs-4", "jules-comm-1", "jules-comm-16" ] }, { "productId": "jules", "storyId": "issue-tracker-sync", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "Evidence only shows Jules integrating with GitHub (repos, issues, PRs) and provides an API for custom workflows, but there is no mention of Jira, Linear, ClickUp, Monday.com, or any issue-tracker integration beyond GitHub.", "evidenceIds": [] }, { "productId": "jules", "storyId": "live-agent-activity-monitoring", "verdict": "partial", "quality": 5, "confidence": "low", "rationale": "Jules provides notifications on task completion/input-needed and a plan-approval step, implying some status visibility, plus a diff/PR review flow, but there is no evidence of a live real-time activity feed or streaming log of the agent's current actions while running; community feedback even notes the absence of a stop/interrupt control mid-run, suggesting limited real-time observability. Missing for 10: evidence of a real-time execution log/console view, granular step-by-step status updates, and independent confirmation of live monitoring UX.", "evidenceIds": [ "jules-docs-4", "jules-docs-6", "jules-docs-7", "jules-comm-14" ] }, { "productId": "jules", "storyId": "managed-cloud-execution", "verdict": "full", "quality": 8, "confidence": "high", "rationale": "Jules is explicitly a cloud-hosted agent service: tasks run in Google-managed VMs, integrated with GitHub, with an API and usage limits/plans, requiring no self-hosted backend. Community evidence corroborates real-world usage at scale (75% success on customer PRs, teams dispatching many tasks) though some users report reliability/UI issues. Missing for 10: no independent infra/SLA details, no discoverable OpenAPI/docs.md confirming API completeness, and mixed community reliability reports.", "evidenceIds": [ "jules-docs-1", "jules-docs-3", "jules-docs-8", "jules-docs-9", "jules-docs-13", "jules-comm-16", "jules-comm-19" ] }, { "productId": "jules", "storyId": "manual-model-override", "verdict": "none", "quality": 0, "confidence": "low", "rationale": "No evidence pack item mentions model selection settings or the ability to choose a specific underlying model; the only related mention is 'Priority access to the latest models, starting with Gemini 3 Pro' which describes access tiers, not user-controlled model switching.", "evidenceIds": [] }, { "productId": "jules", "storyId": "mid-task-takeover", "verdict": "partial", "quality": 5, "confidence": "medium", "rationale": "Jules supports reviewing plans, diffs, and sending follow-up messages to redirect a task (jules-docs-4, jules-docs-6, jules-docs-7, jules-docs-12), and third parties have wired the API into VS Code/Copilot Chat to dispatch tasks (jules-comm-19), but this is dispatch, not mid-task takeover. A hands-on report explicitly notes there's no 'STOP' button to interrupt a looping task (jules-comm-14), undercutting redirect control, and all takeover is browser/API-based with no native terminal or editor 'take over' UI documented. Missing for 10: documented in-editor/terminal takeover UI, ability to pause/interrupt an in-progress run, and evidence the API-based messaging genuinely redirects rather than just appends instructions.", "evidenceIds": [ "jules-docs-4", "jules-docs-6", "jules-docs-7", "jules-docs-12", "jules-comm-14", "jules-comm-19" ] }, { "productId": "jules", "storyId": "mobile-approval-of-decisions", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "Jules is a cloud-based agent with a web app and notification system, plan approval, diff review, and PR approval steps, and community evidence confirms users review/approve PRs from a browser including 'coding on my phone' via mobile web. However, there is no dedicated native mobile app or explicit mobile-optimized approval UI, and no evidence of push-notification-driven approval flows tailored to on-the-go PM decision-making. missing for 10: dedicated mobile app or mobile-specific UI, evidence of push notifications enabling quick phone-based approvals, PM-specific (non-developer) approval workflow.", "evidenceIds": [ "jules-docs-4", "jules-docs-6", "jules-docs-7", "jules-docs-8", "jules-comm-12" ] }, { "productId": "jules", "storyId": "one-click-repo-access", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "Jules integrates with GitHub (label-based task assignment, PR creation) and needs repo access to work, implying a connect/authorize flow, and community evidence confirms GitHub integration works well for many users. However, there's no explicit documentation of a literal 'one-click install' onboarding flow or app installation process described in the evidence pack. missing for 10: explicit one-click GitHub App install/authorization flow documentation, independent confirmation of setup simplicity.", "evidenceIds": [ "jules-docs-1", "jules-docs-2", "jules-docs-8", "jules-comm-2", "jules-comm-6" ] }, { "productId": "jules", "storyId": "openness-api-parity", "verdict": "partial", "quality": 5, "confidence": "medium", "rationale": "Jules exposes a public API for creating tasks, sending messages, and enabling automation (PR creation) — core building blocks of the UI workflow — and a community user confirms dispatching tasks via the API from an external MCP client, showing real usage beyond the UI. However, there's no documented API coverage for plan approval, diff browsing/approval, or settings/API-key management, and no OpenAPI spec is discoverable (probes 404), so full UI/API parity is unconfirmed. Missing for 10: API endpoints for plan review/approval, diff/PR review parity, settings management, and a public API spec confirming full feature coverage.", "evidenceIds": [ "jules-docs-9", "jules-docs-10", "jules-docs-11", "jules-docs-12", "jules-comm-19", "jules-probe-3" ] }, { "productId": "jules", "storyId": "openness-full-export", "verdict": "none", "quality": 0, "confidence": "low", "rationale": "No evidence of any data export feature, open-format export, or account/data portability mechanism for Jules; the product works via GitHub repos/PRs but nothing indicates users can export Jules-specific data (task history, configs, etc.) and leave. missing for 10: any documentation of data export, open format support, or account portability/deletion workflow.", "evidenceIds": [] }, { "productId": "jules", "storyId": "openness-open-license", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "Jules is a closed, proprietary Google product (cloud VM, API keys, usage limits); no evidence anywhere of source code being published or licensed openly, and probes for docs/openapi artifacts return 404s, further suggesting no open publishing.", "evidenceIds": [ "jules-docs-1", "jules-docs-9", "jules-probe-1", "jules-probe-2", "jules-probe-3" ] }, { "productId": "jules", "storyId": "openness-self-host", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "Jules is explicitly a cloud-based VM service (Google-hosted) with no evidence of any self-hosting option; community even notes cloud-only design as a drawback compared to local tools like Claude Code.", "evidenceIds": [ "jules-docs-3", "jules-comm-1", "jules-comm-17" ] }, { "productId": "jules", "storyId": "privacy-data-residency", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "No evidence in the pack mentions data residency, regional storage options, or any control over where Jules stores/processes data; docs only describe VM execution and GitHub integration without residency settings.", "evidenceIds": [] }, { "productId": "jules", "storyId": "privacy-no-training", "verdict": "none", "quality": 0, "confidence": "low", "rationale": "No evidence in the pack addresses data usage/training opt-out policies, privacy controls, or any statement about whether user code/data is used to train AI models; the docs focus on functionality (VM execution, PR creation, API) rather than data governance.", "evidenceIds": [] }, { "productId": "jules", "storyId": "privacy-retention-controls", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "No evidence pack item mentions data retention policies, data deletion controls, or privacy settings for repositories/code processed by Jules; docs cover workflow, VM execution, and API usage but not retention/deletion controls.", "evidenceIds": [] }, { "productId": "jules", "storyId": "privacy-telemetry-optout", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "No evidence in the pack mentions telemetry, usage tracking, or an opt-out mechanism; the docs focus on repo access, VM execution, PR workflow, and API usage without addressing privacy/telemetry controls.", "evidenceIds": [] }, { "productId": "jules", "storyId": "query-repo-documentation", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "Evidence describes Jules as a task-based coding agent that clones repos, edits code, and creates PRs, but there is no mention of generating or letting users query documentation for a repository (public or private). missing for 10: any docs-generation feature, a documentation query/search interface, evidence of indexing repo docs for Q&A.", "evidenceIds": [ "jules-docs-1", "jules-docs-3", "jules-docs-8" ] }, { "productId": "jules", "storyId": "repo-readiness-report", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "No evidence of a readiness report, scorecard, or repository-assessment feature for autonomous agent suitability; evidence only covers task execution, PR creation, and API usage. Missing for 10: any readiness-scoring feature, repo audit/checklist, or report output evaluating agent-readiness.", "evidenceIds": [] }, { "productId": "jules", "storyId": "security-alert-remediation", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "Jules is a general coding agent that can be assigned tasks (via GitHub label or API) and opens PRs with diffs for review, but there is no evidence of any security-alert scanning, vulnerability detection/validation, or a workflow that automatically triggers remediation from a security alert (e.g., Dependabot/CVE integration). The evidence pack shows generic task-to-PR flow, not a security-alert-specific pipeline.", "evidenceIds": [ "jules-docs-1", "jules-docs-3", "jules-docs-4", "jules-docs-7", "jules-docs-8", "jules-docs-9" ] }, { "productId": "jules", "storyId": "self-hosted-agent-deployment", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "Jules is explicitly cloud-hosted: it runs tasks in Google's own VMs, and community feedback confirms it is not local (jules-comm-1). There is no evidence of any self-hosting, on-prem, container, or private-VM deployment option.", "evidenceIds": [ "jules-docs-3", "jules-comm-1" ] }, { "productId": "jules", "storyId": "steer-active-session", "verdict": "full", "quality": 7, "confidence": "medium", "rationale": "Jules's API docs explicitly describe sending a message to an active agent session (jules-docs-12), and community reports confirm the workflow of reviewing partial work and providing feedback for Jules to continue rather than restarting (jules-comm-3). This shows the steering capability works in practice, though documentation on how follow-ups affect an in-progress plan/execution is thin. Missing for 10: detailed first-party documentation of mid-task message handling/UI chat thread, and independent hands-on confirmation of steering effectiveness beyond one anecdote.", "evidenceIds": [ "jules-docs-12", "jules-docs-6", "jules-comm-3" ] }, { "productId": "jules", "storyId": "task-completion-notifications", "verdict": "full", "quality": 8, "confidence": "medium", "rationale": "First-party docs explicitly state users are notified when a task completes or needs input, and the workflow (plan approval, diff review, PR creation) reinforces oversight checkpoints where notifications matter. Community evidence corroborates the async, check-back-later usage pattern (e.g. reviewing PRs after time away), consistent with notification-driven workflows. Missing for 10: independent verification of notification channels (email/push/Slack), and no detail on notification reliability or configurability.", "evidenceIds": [ "jules-docs-6", "jules-docs-4", "jules-docs-8", "jules-comm-12", "jules-comm-3" ] }, { "productId": "jules", "storyId": "tiered-autonomy-levels", "verdict": "partial", "quality": 4, "confidence": "low", "rationale": "Jules has a binary plan-approval workflow (review/approve plan, review diffs, approve PRs) and an API `automationMode` field that toggles whether PRs are auto-created, but there's no evidence of configurable tiered autonomy levels (e.g., low/medium/high trust settings, granular permission scopes, or per-action confirmation thresholds) that an engineering-lead could set. Missing for 10: explicit multi-tier autonomy/permission settings, admin-configurable trust levels, and any org-wide policy controls beyond the single automationMode toggle and default plan-approval gate.", "evidenceIds": [ "jules-docs-4", "jules-docs-7", "jules-docs-8", "jules-docs-11" ] }, { "productId": "jules", "storyId": "trigger-agent-on-build-failure", "verdict": "partial", "quality": 5, "confidence": "medium", "rationale": "Jules exposes a documented API for creating custom workflows and automating tasks like bug fixing (jules-docs-9, jules-docs-10, jules-docs-12), which could be wired into a CI/CD pipeline to trigger a fix, and community evidence shows users building custom integrations to dispatch Jules tasks programmatically (jules-comm-19). However, there is no first-party CI/CD-specific integration (e.g. GitHub Actions step, build-failure webhook) or documented example of triggering Jules to fix a broken build/failing test directly from a pipeline. missing for 10: explicit CI/CD pipeline integration/example, evidence of triggering on build/test failure events, hands-on confirmation of this specific workflow succeeding.", "evidenceIds": [ "jules-docs-9", "jules-docs-10", "jules-docs-11", "jules-docs-12", "jules-comm-19" ] }, { "productId": "jules", "storyId": "visual-context-for-tasks", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "No evidence Jules supports attaching images, screenshots, or mockups to a task — task submission is described only via text prompts, GitHub issue labels, or API messages. Missing for 10: any mention of image/screenshot attachment, multimodal input handling, or visual mockup interpretation.", "evidenceIds": [] }, { "productId": "omnara", "storyId": "agentic-agent-docs", "verdict": "full", "quality": 8, "confidence": "high", "rationale": "Probe confirms llms.txt is live at docs.omnara.com/llms.txt (HTTP 200) with structured links to introduction, quickstart, etc., directly enabling agent-oriented doc consumption; openapi.json is also served for machine consumption. missing for 10: no independent/community confirmation that agents actually consume llms.txt effectively, and no explicit vendor documentation describing llms.txt as an intentional agent-facing feature.", "evidenceIds": [ "omnara-probe-1", "omnara-probe-2", "omnara-probe-3" ] }, { "productId": "omnara", "storyId": "agentic-ai-insights", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "Omnara is a platform for launching, monitoring, and controlling AI coding agents (agent orchestration/remote control), but no evidence shows the product itself surfaces AI-generated insights or suggestions derived from a user's own data (e.g., analytics, dashboards summarizing usage, recommendations). The mention of querying agent history in Postgres for analytics is a raw data-access feature for self-hosters, not an AI-generated insight delivered inside the product.", "evidenceIds": [ "omnara-gh-2" ] }, { "productId": "omnara", "storyId": "agentic-autonomous-automation", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "Docs show agents can be launched programmatically (CLI/API/SDK), run on a connected machine, and persist across crashes/restarts/disconnects, which supports background operation, and users can queue tasks and set approval rules for autonomy. However, the docs emphasize human oversight (pause for approval, live corrections) rather than fully unattended scheduled automation, and no independent evidence confirms agents running autonomously without supervision. Missing for 10: evidence of scheduling/triggers for automation, independent confirmation of successful unattended background runs, and clarity on how 'autonomous' vs 'supervised' operation is enforced.", "evidenceIds": [ "omnara-docs-2", "omnara-docs-3", "omnara-docs-7", "omnara-docs-8", "omnara-docs-12" ] }, { "productId": "omnara", "storyId": "agentic-builtin-assistant", "verdict": "full", "quality": 8, "confidence": "high", "rationale": "Omnara's core product function is launching and delegating tasks to configurable AI agents (via dashboard, CLI, API, or SDK), which run using pluggable models (OpenAI, Anthropic, etc.), retain conversation history, and let users follow progress, send corrections, queue tasks, and approve actions — a textbook 'delegate to built-in AI assistant' workflow. Hands-on community reactions corroborate real usage and enthusiasm for this delegation workflow (e.g. 'using Claude Code from your phone is kind of great', 'this is genius, how I've wanted AI agents to work'). Missing for 10: no rigorous independent benchmark of assistant quality/reliability, and some community friction (UI copy bugs, GitHub OAuth errors) suggests rough edges in the delegation experience.", "evidenceIds": [ "omnara-docs-2", "omnara-docs-4", "omnara-docs-7", "omnara-docs-8", "omnara-comm-3", "omnara-comm-9", "omnara-comm-16" ] }, { "productId": "omnara", "storyId": "agentic-headless", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "Omnara explicitly supports launching agents programmatically via CLI, REST API, or TypeScript SDK (omnara-docs-2), and API keys can be assigned operator roles for automated agent control (omnara-gh-1), which supports headless/CI-style usage. However, there is no explicit CI/CD pipeline example, GitHub Actions integration, or dedicated 'headless mode' documentation demonstrating a full automation workflow. missing for 10: concrete CI/CD integration example, explicit headless-mode docs, independent confirmation of automated pipeline usage.", "evidenceIds": [ "omnara-docs-2", "omnara-gh-1", "omnara-gh-2", "omnara-probe-2", "omnara-probe-3" ] }, { "productId": "omnara", "storyId": "agentic-mcp-client", "verdict": "full", "quality": 7, "confidence": "medium", "rationale": "Docs explicitly state users can 'Connect MCP servers to give them access to your services and data, or add custom tools' as a first-party feature. Missing for 10: independent hands-on confirmation of MCP server integration working, and details on setup/configuration flow or limitations.", "evidenceIds": [ "omnara-docs-5" ] }, { "productId": "omnara", "storyId": "agentic-mcp-server", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "Omnara is a platform for launching and managing agents (not itself a coding agent), so the axis of exposing an official MCP server applies. Evidence only shows Omnara connecting outward to MCP servers to give its agents tool access (client-side), with no documentation of Omnara itself running as an MCP server that external agents can connect to.", "evidenceIds": [ "omnara-docs-5" ] }, { "productId": "omnara", "storyId": "agentic-nl-commands", "verdict": "full", "quality": 7, "confidence": "medium", "rationale": "Omnara's core interaction model is natural-language messaging with agents — users can 'send a correction while it's working, or queue up its next task' and agents 'pause to ask questions' for user input in natural language, which is the primary way users operate the product day-to-day. Missing for 10: independent/hands-on confirmation of the NL interface specifically (community threads focus on UI/pricing/tunneling, not command quality), and no detail on how broad or robust the NL command surface is beyond corrections/queuing.", "evidenceIds": [ "omnara-docs-7", "omnara-docs-8", "omnara-docs-9", "omnara-docs-2" ] }, { "productId": "omnara", "storyId": "agentic-official-cli", "verdict": "full", "quality": 7, "confidence": "medium", "rationale": "Docs explicitly mention launching agents 'programmatically with the CLI' alongside REST API and SDK, and a probe confirms an official CLI documented on the quickstart page. Missing for 10: detailed CLI command reference/usage docs and independent hands-on confirmation of CLI functionality.", "evidenceIds": [ "omnara-docs-2", "omnara-probe-3" ] }, { "productId": "omnara", "storyId": "agentic-public-api", "verdict": "full", "quality": 8, "confidence": "high", "rationale": "Omnara documents a public REST API with an OpenAPI spec (probed live, HTTP 200), plus a CLI and TypeScript SDK for programmatic launching and control of agents, and describes itself as 'The API for Production-Grade Agents.' Missing for 10: independent third-party developer accounts of building against the API beyond vendor docs/probes.", "evidenceIds": [ "omnara-docs-2", "omnara-probe-1", "omnara-probe-2", "omnara-probe-3" ] }, { "productId": "omnara", "storyId": "agentic-scoped-keys", "verdict": "partial", "quality": 5, "confidence": "low", "rationale": "Omnara supports role-based access via 'organization and project roles to users and API keys' distinguishing who can manage, configure, operate, or view agents, which is a form of scoped credential control (omnara-gh-1). However, there is no documentation of fine-grained scoping (e.g., per-resource or per-action API key scopes), token expiration, or least-privilege credential issuance workflows specifically for agents. missing for 10: explicit API key scoping/permission granularity docs, credential expiration/rotation support, independent verification of least-privilege enforcement.", "evidenceIds": [ "omnara-gh-1" ] }, { "productId": "omnara", "storyId": "agentic-sdks", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "Docs and probes confirm an official REST API (OpenAPI spec live), a TypeScript SDK, and a CLI for launching/programming agents, which supports SDK-based building. However, evidence is limited to a single TypeScript SDK with no mention of other language SDKs (e.g., Python), no independent developer corroboration of SDK usage, and community discussion is mostly about UI/pricing rather than SDK development experience. Missing for 10: multi-language SDK coverage, independent hands-on SDK usage reports, deeper SDK reference docs beyond quickstart.", "evidenceIds": [ "omnara-docs-2", "omnara-probe-2", "omnara-probe-3" ] }, { "productId": "omnara", "storyId": "agentic-webhooks", "verdict": "none", "quality": 0, "confidence": "low", "rationale": "No evidence in the pack mentions webhooks or any event-subscription mechanism; Omnara's docs describe API/CLI/SDK usage, MCP connections, and agent management but nothing about outbound webhook notifications.", "evidenceIds": [] }, { "productId": "omnara", "storyId": "ai-automated-test-generation-on-change", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "Omnara is a remote agent-orchestration/monitoring platform (control, approvals, notifications, session relay) rather than a coding agent itself; it doesn't generate or execute code or tests—it relays the underlying agent's work. Self-generated test authoring/execution is an axis for the coding agent, not this orchestration layer, so it's a category mismatch here.", "evidenceIds": [] }, { "productId": "omnara", "storyId": "ai-automatic-model-routing", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "Omnara lets users choose from multiple model providers (OpenAI, Anthropic, OpenRouter, Bedrock, self-hosted) but there is no evidence of automatic routing of prompts to the most suitable model — model selection appears manual/configured per agent, not dynamically routed.", "evidenceIds": [ "omnara-docs-4", "omnara-docs-13" ] }, { "productId": "omnara", "storyId": "ai-issue-triage", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "Omnara is an agent orchestration/mobile-control platform for launching and monitoring coding agents, not an issue-triage or incident-management tool; there is no concept of incoming issues, severity scoring, or ownership routing in its evidence pack. This axis is a category error for this product type.", "evidenceIds": [] }, { "productId": "omnara", "storyId": "api-interactive-docs", "verdict": "partial", "quality": 4, "confidence": "low", "rationale": "An OpenAPI spec is confirmed live (openapi.json) and docs.omnara.com hosts structured API/quickstart pages, suggesting a machine-readable API reference exists, but no evidence confirms an interactive 'try it now' console or runnable code examples within the docs. Missing for 10: explicit documentation or screenshots of an interactive API playground, confirmation of runnable/executable examples, and independent user reports of using such a feature.", "evidenceIds": [ "omnara-probe-2", "omnara-probe-1", "omnara-docs-2" ] }, { "productId": "omnara", "storyId": "api-machine-spec", "verdict": "full", "quality": 9, "confidence": "high", "rationale": "A probe confirms a live, machine-readable OpenAPI spec at docs.omnara.com/openapi.json returning a valid 'openapi' key, alongside a documented REST API and SDKs referenced in quickstart docs. Missing for 10: no independent/community corroboration of developers actually consuming the spec.", "evidenceIds": [ "omnara-probe-2", "omnara-docs-2", "omnara-probe-1" ] }, { "productId": "omnara", "storyId": "api-sandbox", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "Omnara is an agent orchestration/monitoring platform for launching and managing coding agents, not a data platform or API with production vs sandbox environments; the evidence pack shows no concept of sandbox/test environments distinct from production data. This axis is a category error for this product type.", "evidenceIds": [] }, { "productId": "omnara", "storyId": "api-versioning-policy", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "No evidence of API versioning scheme or documented deprecation policy; the OpenAPI spec exists (omnara-probe-2) but nothing addresses version numbers, backward compatibility guarantees, or deprecation timelines. Missing for 10: versioned API endpoints/headers, published deprecation policy, changelog entries about breaking changes.", "evidenceIds": [ "omnara-probe-2" ] }, { "productId": "omnara", "storyId": "approve-plan-before-execution", "verdict": "partial", "quality": 4, "confidence": "low", "rationale": "Omnara lets you configure which agent actions require approval and lets agents pause to ask for input, which could be used to gate on a plan before code changes, but there is no documented feature specifically for presenting/reviewing an implementation plan before code is written. Community feedback (omnara-comm-5) explicitly notes that reviewing/QAing agent work remains unsolved with tools like this, casting doubt on how robust the approval gate is for plan review. Missing for 10: dedicated plan-approval workflow/UI, evidence that approvals occur pre-implementation rather than mid-execution, and independent confirmation the feature works as intended.", "evidenceIds": [ "omnara-docs-8", "omnara-comm-5" ] }, { "productId": "omnara", "storyId": "approve-task-contract", "verdict": "partial", "quality": 4, "confidence": "low", "rationale": "Omnara lets you configure which agent actions require approval and pause for input, which offers some pre-modification gating (omnara-docs-8), and role-based permissions can restrict who can operate agents (omnara-gh-1). However there is no documented workflow for an engineering-lead to review and approve a task's overall scope/contract before any repository changes occur — approvals are described as per-action, not a formal spec-review gate. Missing for 10: explicit scope/contract definition and lead sign-off step prior to agent execution, evidence of blocking all repo writes until approval, and independent confirmation this approval flow works as intended.", "evidenceIds": [ "omnara-docs-8", "omnara-gh-1" ] }, { "productId": "omnara", "storyId": "assign-task-from-ticket", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "Evidence covers launching agents via dashboard/CLI/API/SDK, skills, MCP tools, and approvals, but nothing describes assigning a task directly from an existing GitHub issue, Jira ticket, or similar tracker item. A GitHub OAuth connection is mentioned only in the context of a login error, not issue-to-task assignment. Missing for 10: any documentation or demo of ticket/issue import, issue-linked task creation, or tracker integration triggering agent work.", "evidenceIds": [ "omnara-docs-2", "omnara-comm-19" ] }, { "productId": "omnara", "storyId": "auto-approve-all-actions", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "Docs state you can 'decide which actions need approval, and agents can pause to ask questions when they need your input' (omnara-docs-8), implying configurable approval granularity that could be set to auto-approve everything, but there's no explicit documentation of a dedicated 'approve all' toggle or default-auto-approve mode. missing for 10: explicit doc/example showing an 'auto-approve all actions' setting, independent/hands-on confirmation of this behavior.", "evidenceIds": [ "omnara-docs-8" ] }, { "productId": "omnara", "storyId": "auto-fix-readiness-gaps", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "Omnara is an agent orchestration/remote-monitoring platform for launching and interacting with coding agents, not a repository review/quality-gate tool that assesses or auto-fixes 'agent-readiness criteria'. This story belongs to a different product category (review-quality-gate/linting tools), so it is a category error for Omnara.", "evidenceIds": [] }, { "productId": "omnara", "storyId": "auto-open-pr-on-completion", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "Omnara is a mission-control/dashboard layer for monitoring and interacting with coding agents (session tracking, MCP tools, approvals) but no evidence describes automated PR creation on task completion; community even notes GitHub OAuth integration errors and no PR-automation feature is mentioned.", "evidenceIds": [ "omnara-comm-19", "omnara-docs-7", "omnara-docs-8" ] }, { "productId": "omnara", "storyId": "automated-pr-review", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "Omnara is an agent-orchestration/mobile-control platform for launching and monitoring AI coding agents, not a PR/code-review tool; there is no evidence of GitHub PR integration, inline diff comments, or review-gate automation. In fact community feedback explicitly notes it does not address code review/QA needs, and a GitHub OAuth connection error is reported.", "evidenceIds": [ "omnara-comm-5", "omnara-comm-19" ] }, { "productId": "omnara", "storyId": "automatic-environment-setup", "verdict": "none", "quality": 0, "confidence": "low", "rationale": "Omnara's evidence covers launching, monitoring, and remote-controlling agents (via dashboard/CLI/API/SDK) and connecting to a machine you own, but there's no documentation of the agent autonomously cloning a repo, installing dependencies, or self-configuring its own working environment — that setup appears to remain the developer's responsibility. Missing for 10: any mention of automated repo cloning, dependency installation, or environment bootstrap by the agent itself.", "evidenceIds": [ "omnara-docs-2", "omnara-docs-12", "omnara-docs-1" ] }, { "productId": "omnara", "storyId": "automation-bulk-operations", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "No evidence in the pack describes bulk operations across many items (e.g., batch launching, mass approval, multi-agent bulk actions); the docs focus on single-agent lifecycle, roles, and analytics queries rather than bulk operations tooling.", "evidenceIds": [] }, { "productId": "omnara", "storyId": "automation-rules-engine", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "Omnara's evidence covers agent launching, conversation persistence, MCP/tool connections, and human-in-the-loop approval gates, but nothing describes a rule engine or event-trigger system where users define conditions that automatically fire actions. The closest item (approval configuration, omnara-docs-8) is about gating agent actions for human review, not automating actions on events.", "evidenceIds": [] }, { "productId": "omnara", "storyId": "automation-scheduled-jobs", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "No evidence anywhere in the pack of a scheduler, cron-like trigger, or recurring job/workflow feature; Omnara's evidence covers launching agents, tracking conversations, tools, and approvals, but nothing about scheduling recurrence.", "evidenceIds": [] }, { "productId": "omnara", "storyId": "automation-versioned-workflows", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "Evidence shows agent configs as YAML, conversation history persistence, and approval gating, but nothing about versioning automations, reviewing changes over time, or rolling back to prior configurations/states. Missing for 10: version history for agent configs, diff/review UI for changes, rollback/restore mechanism.", "evidenceIds": [ "omnara-docs-1", "omnara-docs-3", "omnara-docs-8" ] }, { "productId": "omnara", "storyId": "autonomous-bug-fixing", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "Omnara's evidence describes it as an orchestration/remote-monitoring layer for launching, tracking, and approving agent sessions (YAML config, model connections, MCP tools, live following/correction) rather than an agent that itself performs autonomous bug diagnosis and code fixes; community comments frame it as a wrapper around external coding agents like Claude Code rather than an implementer of fixes. No evidence shows Omnara autonomously diagnosing or resolving a reported bug end-to-end.", "evidenceIds": [ "omnara-docs-1", "omnara-docs-2", "omnara-docs-5", "omnara-comm-3", "omnara-comm-5" ] }, { "productId": "omnara", "storyId": "bring-your-own-model", "verdict": "full", "quality": 8, "confidence": "medium", "rationale": "Docs explicitly state support for OpenAI, Anthropic, OpenRouter, Bedrock, or self-hosted compatible models, and pricing page confirms 'Bring your own model keys for free.' missing for 10: independent/hands-on confirmation of BYO-key setup working in practice, and details on per-model configuration granularity.", "evidenceIds": [ "omnara-docs-4", "omnara-docs-13", "omnara-docs-11" ] }, { "productId": "omnara", "storyId": "chat-with-agent-in-slack", "verdict": "partial", "quality": 4, "confidence": "low", "rationale": "Omnara supports conversational interaction with an agent — following its progress live, sending corrections, queuing next tasks, and pausing for questions/approvals (omnara-docs-7, omnara-docs-8) — which covers discussing and delegating work, but there is no evidence of an actual '@tag' mechanism inside a shared team chat thread (e.g., Slack/Discord-style mention) for bringing an agent into a conversation. missing for 10: explicit tagging/mention syntax, multi-user shared thread support, and any integration showing agents being pulled into existing chat channels.", "evidenceIds": [ "omnara-docs-7", "omnara-docs-8", "omnara-docs-9" ] }, { "productId": "omnara", "storyId": "ci-failure-auto-fix", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "Omnara is a remote agent-monitoring/mobile-control platform for launching and interacting with coding agents, not a CI/CD integration or code-review tool; there is no mention of CI workflow diagnosis or auto-generated PRs from failed builds. This is a wrong-axis story for this product category.", "evidenceIds": [] }, { "productId": "omnara", "storyId": "codebase-context-file", "verdict": "partial", "quality": 3, "confidence": "low", "rationale": "Omnara's 'Skills' feature lets users package instructions and supporting files for recurring agent work, which could function like a context file, but there is no explicit documentation describing a dedicated codebase-conventions context file that shapes agent plans/code generation. missing for 10: explicit codebase-conventions context file feature, evidence of it improving plan/code relevance, and any independent confirmation of this use case.", "evidenceIds": [ "omnara-docs-6" ] }, { "productId": "omnara", "storyId": "concurrent-task-execution", "verdict": "partial", "quality": 5, "confidence": "low", "rationale": "Omnara lets you launch agents programmatically via CLI/REST/SDK, manage multiple agents with org/project roles, and queue tasks, which implies some capacity for parallel agent operation, but there is no explicit documentation or evidence of running many agents concurrently at scale, dashboards for fleet-wide monitoring, or throughput/performance claims. missing for 10: explicit concurrency/parallelism documentation, scaling benchmarks or case studies, dashboard support for managing many simultaneous agent sessions.", "evidenceIds": [ "omnara-docs-2", "omnara-docs-7", "omnara-gh-1", "omnara-docs-8" ] }, { "productId": "omnara", "storyId": "connect-github-repo", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "No vendor documentation describes connecting a GitHub repository so an agent can access code and open pull requests; the only concrete evidence is a community report of a GitHub OAuth connection failure (redirect_uri mismatch), with no confirmation that repo access or PR creation actually works.", "evidenceIds": [ "omnara-comm-19" ] }, { "productId": "omnara", "storyId": "daily-task-quota-limits", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "No evidence in the pack mentions plan-based daily task limits, concurrency limits, or any dashboard/UI for managing such quotas; pricing docs only mention self-hosting being free and 'bring your own model keys' with no concurrency/task-limit controls described.", "evidenceIds": [ "omnara-docs-11", "omnara-docs-13" ] }, { "productId": "omnara", "storyId": "delegate-sessions-for-teammates", "verdict": "partial", "quality": 4, "confidence": "low", "rationale": "Omnara supports organization/project roles that let admins manage access and configure or operate agents on behalf of others (omnara-gh-1), which implies some delegated control, but there is no explicit documentation of an engineering-lead directly creating or launching agent sessions attributed to another specific user. Missing for 10: explicit API/CLI support for creating a session 'as' another user, audit/ownership attribution evidence, and any hands-on confirmation of this workflow.", "evidenceIds": [ "omnara-gh-1" ] }, { "productId": "omnara", "storyId": "describe-feature-in-plain-language", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "Omnara's evidence describes launching, monitoring, and queuing tasks for coding agents (YAML configs, live progress, queueing next task, approvals) but nothing shows Omnara itself converting a plain-language feature/bug description into a scoped implementation task or spec — that logic would live in the underlying agent model, not in Omnara's own product surface.", "evidenceIds": [ "omnara-docs-2", "omnara-docs-7", "omnara-docs-1" ] }, { "productId": "omnara", "storyId": "design-to-code-handoff", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "Omnara is an orchestration/monitoring layer for launching and supervising coding agents (via YAML config, CLI, API, dashboard) rather than a design-to-code tool; there is no evidence of interpreting mockups/designs into implementations, and setup (YAML config, CLI, API keys) implies engineering involvement rather than a no-handoff PM workflow. The only tangential evidence is that images/documents can be attached to a conversation, which does not demonstrate a mockup-to-implementation pipeline.", "evidenceIds": [ "omnara-docs-9", "omnara-docs-1", "omnara-docs-2" ] }, { "productId": "omnara", "storyId": "diff-review-before-pr", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "Omnara's docs describe generic 'approve actions' and pause-for-input mechanisms, but there is no evidence of a diff-review UI or an approval gate specifically tied to turning agent changes into a pull request. A community comment on the same HN thread explicitly notes 'my problem is QAing and reviewing the code... none of these tools solves that,' reinforcing the absence of this capability.", "evidenceIds": [ "omnara-docs-8", "omnara-comm-5" ] }, { "productId": "omnara", "storyId": "end-to-end-feature-implementation", "verdict": "partial", "quality": 3, "confidence": "low", "rationale": "Omnara lets you launch and remotely manage coding agents (e.g., Claude Code, Codex) that can presumably write code, and community comments imply these agents 'write code' end-to-end, but there is no documentation or evidence specifically describing an agent completing a full feature implementation including test-writing within Omnara itself — it's positioned as an orchestration/monitoring layer, not the coding agent doing the work. missing for 10: explicit documentation or case study of an agent autonomously implementing a feature end-to-end, evidence of test generation/execution, and confirmation that Omnara (vs. the underlying model) performs the actual coding.", "evidenceIds": [ "omnara-docs-2", "omnara-docs-7", "omnara-comm-5" ] }, { "productId": "omnara", "storyId": "enterprise-sso-licensing", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "Evidence shows role-based access control and self-hosted deployments, but there is no mention of SSO, enterprise licensing, or commercial support offerings anywhere in the pack; pricing info only covers free self-hosting and a consumer subscription tier.", "evidenceIds": [ "omnara-gh-1", "omnara-docs-11", "omnara-comm-14" ] }, { "productId": "omnara", "storyId": "feedback-to-task-conversion", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "Omnara is an agent-orchestration/remote-control platform for coding agents, not a product-feedback or requirements-management tool; nothing in the evidence relates to converting user feedback into structured tasks with proposed scope. This is a wrong-axis story for this product category.", "evidenceIds": [] }, { "productId": "omnara", "storyId": "headless-ci-execution", "verdict": "partial", "quality": 4, "confidence": "low", "rationale": "Omnara documents a CLI, REST API and TypeScript SDK for launching agents programmatically (omnara-docs-2, omnara-probe-2/3), which could technically be invoked from a CI/CD pipeline or shell script, but there is no explicit CI/CD example, pipeline integration guide, or non-interactive/headless mode documentation. The product's core design emphasizes live human oversight (following progress, approvals, pausing for questions) rather than unattended automation. Missing for 10: explicit CI/CD pipeline docs/examples, confirmation of a fully non-interactive headless mode, and evidence of shell-script usage patterns.", "evidenceIds": [ "omnara-docs-2", "omnara-probe-2", "omnara-probe-3", "omnara-docs-8" ] }, { "productId": "omnara", "storyId": "isolated-sandbox-execution", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "Omnara is a control/orchestration layer for launching, monitoring, and messaging agents (via CLI/API/dashboard, connecting to your own machine or model), but nothing in the evidence describes a sandboxed or isolated execution environment for code/dependency installation—agents appear to run on a user-connected machine or laptop/server without mention of isolation, containerization, or sandboxing guarantees. missing for 10: any mention of sandbox/isolation architecture, dependency installation safety, or containerized execution environment.", "evidenceIds": [ "omnara-docs-2", "omnara-docs-12" ] }, { "productId": "omnara", "storyId": "issue-tracker-sync", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "No evidence Omnara integrates with issue trackers like Jira, Linear, ClickUp, or Monday.com; docs mention MCP servers, custom tools, GitHub OAuth, and models, but no ticketing/issue-tracker connectors are described anywhere.", "evidenceIds": [] }, { "productId": "omnara", "storyId": "live-agent-activity-monitoring", "verdict": "full", "quality": 8, "confidence": "medium", "rationale": "Docs explicitly state you can 'Follow an agent's progress live' and agents 'pause to ask questions when they need your input,' with approval controls for actions, matching real-time monitoring and status visibility. Community threads confirm the product's core use case is remote live monitoring of running agents via a UI/app. Missing for 10: independent hands-on confirmation of the live status UI's accuracy/latency and more detail on what 'status' fields are shown.", "evidenceIds": [ "omnara-docs-7", "omnara-docs-8", "omnara-comm-3", "omnara-comm-11" ] }, { "productId": "omnara", "storyId": "managed-cloud-execution", "verdict": "partial", "quality": 5, "confidence": "medium", "rationale": "Omnara offers a dashboard/API/CLI to launch and manage agents (omnara-docs-2, omnara-probe-1/2/3) and a pricing tier separate from self-hosting (omnara-docs-11, omnara-comm-12/14 mention a paid ~$20/mo hosted tunnel/service), implying a managed cloud option exists. However, the product is primarily marketed as an open-source, self-hostable alternative to managed agent platforms, and there's no first-party documentation detailing SLAs, scaling, or infrastructure guarantees of the managed cloud service — community comments even question its value versus self-hosting with Tailscale.\nmissing for 10: explicit vendor documentation of the managed cloud infrastructure's scaling/reliability guarantees, and independent confirmation that users successfully run agents purely via the hosted service without any self-hosted components.", "evidenceIds": [ "omnara-docs-2", "omnara-docs-11", "omnara-comm-12", "omnara-comm-14", "omnara-probe-1" ] }, { "productId": "omnara", "storyId": "manual-model-override", "verdict": "partial", "quality": 5, "confidence": "low", "rationale": "Docs confirm users can choose models from OpenAI, Anthropic, OpenRouter, Bedrock, or self-hosted ones and 'bring your own model keys', implying explicit model selection rather than forced auto-selection, but there is no direct documentation of an automatic model-selection mode being toggled off or a UI/config setting explicitly named 'model selection'. missing for 10: explicit description of a default/automatic model-selection mode and a documented switch/override mechanism, independent confirmation of this workflow.", "evidenceIds": [ "omnara-docs-4", "omnara-docs-13" ] }, { "productId": "omnara", "storyId": "mid-task-takeover", "verdict": "full", "quality": 8, "confidence": "medium", "rationale": "Docs directly describe following an agent live, sending a correction mid-task, and queuing its next task, plus pausing for approval/questions — matching the story's takeover/redirect ask across web/dashboard access; agents also persist across restarts/disconnects so work can be resumed from any connected machine. Community threads corroborate real-world use (e.g., interacting with agents from phone/terminal), though QA/copy friction is noted. Missing for 10: no explicit first-party confirmation of parity across editor, terminal, and browser specifically, and no independent hands-on account of a full mid-task takeover/redirect workflow.", "evidenceIds": [ "omnara-docs-7", "omnara-docs-8", "omnara-docs-3", "omnara-docs-12", "omnara-comm-3", "omnara-comm-5" ] }, { "productId": "omnara", "storyId": "mobile-approval-of-decisions", "verdict": "full", "quality": 7, "confidence": "medium", "rationale": "Docs explicitly describe mobile-friendly approval workflows: agents pause to ask questions and require approval on configurable actions, users can follow progress live, send corrections, and queue tasks while agents keep working, and community feedback confirms real hands-on use of approving/interacting with agents from a phone. missing for 10: no explicit phone-specific UI documentation or independent review confirming approval-from-phone works reliably at scale, and some community concerns about UI usability (copy/paste) slightly temper confidence.", "evidenceIds": [ "omnara-docs-7", "omnara-docs-8", "omnara-comm-3" ] }, { "productId": "omnara", "storyId": "one-click-repo-access", "verdict": "disputed", "quality": 3, "confidence": "medium", "rationale": "There's no explicit vendor documentation describing a 'one-click' repo install flow, but community evidence shows a user attempting to connect GitHub and hitting a concrete OAuth failure (github_unauthorized: redirect_uri mismatch), indicating the integration is not smooth or reliable as implied. Missing for 10: first-party docs describing a one-click repo-connect flow, and confirmation the OAuth issue was resolved.", "evidenceIds": [ "omnara-comm-19", "omnara-gh-1" ] }, { "productId": "omnara", "storyId": "openness-api-parity", "verdict": "full", "quality": 7, "confidence": "medium", "rationale": "Omnara explicitly markets itself as 'The API for Production-Grade Agents' with a published OpenAPI spec, and docs state you can launch, manage, and interact with agents via dashboard, CLI, REST API, or TypeScript SDK — indicating API-first parity with the UI. Role-based API keys (omnara-gh-1) further support programmatic control equivalent to UI actions like approvals and configuration. Missing for 10: an explicit statement or independent confirmation that every UI feature (e.g., approvals, skills, image/document attachments) has a documented 1:1 API equivalent, and hands-on verification beyond the OpenAPI probe.", "evidenceIds": [ "omnara-docs-2", "omnara-probe-1", "omnara-probe-2", "omnara-gh-1" ] }, { "productId": "omnara", "storyId": "openness-full-export", "verdict": "partial", "quality": 5, "confidence": "low", "rationale": "Omnara is open-source (Apache 2.0) and self-hostable, and self-hosted deployments can query agent history directly in Postgres for analytics/training datasets, which effectively enables data portability and exit without lock-in. However, there is no explicit documented 'export my data' feature, bulk export tool, or open-format export guarantee for the hosted/SaaS offering. Missing for 10: an explicit data-export mechanism/API endpoint for hosted users, documentation confirming full data portability in open formats, and independent confirmation of successful export/migration.", "evidenceIds": [ "omnara-docs-10", "omnara-docs-11", "omnara-gh-2" ] }, { "productId": "omnara", "storyId": "openness-open-license", "verdict": "full", "quality": 8, "confidence": "medium", "rationale": "Omnara explicitly markets itself as 'the open-source alternative to Claude Managed Agents' and states it is 'Free to self-host and build on. Open source under Apache 2.0,' with a public GitHub repository confirming the source is available. Missing for 10: independent verification of full license scope/completeness (e.g., some components possibly closed-source) and community confirmation of actual code openness beyond vendor claims.", "evidenceIds": [ "omnara-docs-10", "omnara-docs-11", "omnara-gh-1", "omnara-gh-2" ] }, { "productId": "omnara", "storyId": "openness-self-host", "verdict": "full", "quality": 8, "confidence": "medium", "rationale": "Omnara is explicitly positioned as open-source (Apache 2.0) and 'free to self-host and build on,' with a public GitHub repo and self-hosted deployment details (e.g., querying Postgres directly for analytics) confirming the capability exists in practice, not just marketing. Missing for 10: independent/hands-on report of someone actually self-hosting and confirming setup ease or parity with the hosted SaaS version.", "evidenceIds": [ "omnara-docs-10", "omnara-docs-11", "omnara-gh-1", "omnara-gh-2" ] }, { "productId": "omnara", "storyId": "privacy-data-residency", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "No evidence of any data residency/region selection feature; Omnara is self-hostable (Apache 2.0) which could give indirect control over storage location, but there's no documented region-choice option for the hosted service, and community comments even raise unresolved concerns about data going to a 3rd party and lack of E2E encryption. missing for 10: any documented region/residency selection mechanism, hosted-service data location controls, compliance certifications tied to region.", "evidenceIds": [ "omnara-docs-11", "omnara-comm-18", "omnara-comm-20" ] }, { "productId": "omnara", "storyId": "privacy-no-training", "verdict": "none", "quality": 0, "confidence": "low", "rationale": "No evidence of a training-data opt-out or data-usage policy; the only related evidence is community complaints about lack of end-to-end encryption and concerns over data going to third parties, which is not a documented control against AI training use.", "evidenceIds": [] }, { "productId": "omnara", "storyId": "privacy-retention-controls", "verdict": "partial", "quality": 3, "confidence": "low", "rationale": "Omnara offers a self-hosted, open-source deployment where operators can query and manage the Postgres agent-history database directly, implying some control over data retention/deletion, but there is no explicit documented deletion/retention policy, data-export tool, or 'right to be forgotten' feature for the hosted product. Community feedback also raises concerns about lack of end-to-end encryption and visibility of stored messages in the hosted service, suggesting limited user control over cloud-stored data. missing for 10: explicit data-retention policy, a documented deletion/export mechanism, confirmation that hosted-service data can be fully deleted on request.", "evidenceIds": [ "omnara-docs-11", "omnara-gh-2", "omnara-comm-13", "omnara-comm-20" ] }, { "productId": "omnara", "storyId": "privacy-telemetry-optout", "verdict": "none", "quality": 0, "confidence": "low", "rationale": "No evidence pack item mentions telemetry, usage tracking, analytics opt-out, or privacy settings; self-hosting is offered but that alone doesn't constitute a documented telemetry opt-out mechanism.", "evidenceIds": [] }, { "productId": "omnara", "storyId": "query-repo-documentation", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "Omnara is an agent orchestration/monitoring platform for running and interacting with coding agents remotely, not a documentation-generation or repo-search tool; querying generated docs for arbitrary repos is outside its product category.", "evidenceIds": [] }, { "productId": "omnara", "storyId": "repo-readiness-report", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "Omnara is a platform for launching and remotely operating agents, not a repository analysis or code-review tool; a 'repository readiness report' is a category error for this product type rather than a missing feature.", "evidenceIds": [] }, { "productId": "omnara", "storyId": "security-alert-remediation", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "Omnara is a platform for remotely monitoring/controlling coding agents, not a security-scanning or SCA/SAST tool; there is no evidence of vulnerability detection, alert triage, or automated PR remediation workflows tied to security findings. This axis (security-alert validation and auto-remediation) belongs to a different product category (e.g., dependency/security scanners), so it does not apply here.", "evidenceIds": [] }, { "productId": "omnara", "storyId": "self-hosted-agent-deployment", "verdict": "full", "quality": 7, "confidence": "medium", "rationale": "Omnara is explicitly open-source (Apache 2.0) and documented as 'free to self-host and build on,' with self-hosted deployments able to query Postgres directly for agent history, and agents can run on any connected machine (laptop or server) — consistent with local, container, or VM deployment. Missing for 10: explicit Docker/Kubernetes/VM deployment guides or configs, and independent hands-on confirmation of a successful self-hosted setup.", "evidenceIds": [ "omnara-docs-11", "omnara-gh-2", "omnara-docs-12", "omnara-docs-10" ] }, { "productId": "omnara", "storyId": "steer-active-session", "verdict": "full", "quality": 8, "confidence": "medium", "rationale": "Docs explicitly describe following an agent's progress live, sending a correction while it's working, or queuing its next task, which directly matches steering an active session without restarting; approvals/pause-for-input further support mid-session interaction. Missing for 10: independent hands-on confirmation that mid-session corrections actually steer behavior reliably (community threads focus on UI/pricing, not on validating this specific steering feature).", "evidenceIds": [ "omnara-docs-7", "omnara-docs-8", "omnara-docs-3" ] }, { "productId": "omnara", "storyId": "task-completion-notifications", "verdict": "full", "quality": 8, "confidence": "medium", "rationale": "Docs describe live progress tracking, mid-task corrections, task queuing, and agents pausing to ask for input/approval — directly matching the notification/human-input story, and mobile/remote use is corroborated by community hands-on comments. Missing for 10: no explicit push/email notification mechanism documented, and no independent verification of notification reliability or latency.", "evidenceIds": [ "omnara-docs-7", "omnara-docs-8", "omnara-comm-3", "omnara-comm-9" ] }, { "productId": "omnara", "storyId": "tiered-autonomy-levels", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "Docs state you can decide which actions require approval and agents pause to ask for input when needed, which maps to configurable autonomy/oversight, and org/project roles further separate what different users can configure vs operate (omnara-docs-8, omnara-gh-1). However there's no explicit documentation of named 'tiers' or granular policy configuration, and no community/hands-on evidence validating this in practice. Missing for 10: explicit tiered-autonomy policy documentation (e.g., levels/thresholds), examples of configuring per-action approval rules, and independent corroboration that this works as described.", "evidenceIds": [ "omnara-docs-8", "omnara-gh-1" ] }, { "productId": "omnara", "storyId": "trigger-agent-on-build-failure", "verdict": "none", "quality": 0, "confidence": "low", "rationale": "Omnara offers a REST API/CLI/SDK to launch agents programmatically (omnara-docs-2), which could theoretically be scripted into a CI pipeline, but there is no documentation, example, or community evidence showing an actual CI/CD integration (e.g., GitHub Actions trigger, webhook on failed build/test) for automatically fixing broken builds.", "evidenceIds": [ "omnara-docs-2" ] }, { "productId": "omnara", "storyId": "visual-context-for-tasks", "verdict": "partial", "quality": 4, "confidence": "low", "rationale": "Omnara supports attaching images and documents to a conversation (omnara-docs-9), which could technically allow attaching a marked-up screenshot, but there is no evidence of markup/annotation tooling, task-specific attachment linking, or the agent explicitly interpreting visual diffs to implement UI changes. missing for 10: annotation/markup tooling, task-level (not just conversation-level) attachment workflow, evidence the agent actually parses/acts on visual markup, independent confirmation of this use case.", "evidenceIds": [ "omnara-docs-9" ] }, { "productId": "openhands", "storyId": "agentic-agent-docs", "verdict": "full", "quality": 9, "confidence": "high", "rationale": "A probe confirms OpenHands hosts a live llms.txt at docs.openhands.dev/llms.txt returning HTTP 200 with an explicit LLM-friendly documentation index, directly satisfying the story. Missing for 10: no independent third-party confirmation that agents actually consume/parse this file successfully in practice.", "evidenceIds": [ "openhands-probe-1" ] }, { "productId": "openhands", "storyId": "agentic-ai-insights", "verdict": "full", "quality": 7, "confidence": "medium", "rationale": "OpenHands ships automation agents that inspect real data (incident logs, PR diffs, workflow failures, security alerts) and generate AI insights/suggestions such as severity assessment, review comments, root-cause analysis, and remediation PRs, directly matching the story's intent for a coding-agent product. Evidence is vendor-documented only, with no independent/hands-on corroboration of the quality of these insights. Missing for 10: independent validation of suggestion quality, a unified 'insights' UI/dashboard, and evidence of insights beyond code/security/ops contexts.", "evidenceIds": [ "openhands-docs-7", "openhands-docs-8", "openhands-docs-9", "openhands-docs-10", "openhands-docs-11" ] }, { "productId": "openhands", "storyId": "agentic-autonomous-automation", "verdict": "full", "quality": 8, "confidence": "medium", "rationale": "OpenHands supports scheduled/webhook-triggered automations integrating with Slack, GitHub, Linear, etc., and ships prebuilt autonomous workflows (bug triage, PR review, CI failure fixing, security remediation, Slack mention handling) that run without human intervention, plus API support for scripting automations. Missing for 10: independent/hands-on verification of the scheduling/webhook trigger reliability and no detailed docs excerpt on configuring schedules beyond marketing copy.", "evidenceIds": [ "openhands-gh-4", "openhands-docs-7", "openhands-docs-8", "openhands-docs-9", "openhands-docs-10", "openhands-docs-11", "openhands-docs-13" ] }, { "productId": "openhands", "storyId": "agentic-builtin-assistant", "verdict": "full", "quality": 8, "confidence": "high", "rationale": "OpenHands is itself the built-in AI assistant/agent: CLI mode lets users type natural language tasks and get instant feedback, resume conversations, and auto-approve actions, and it can run locally, remote, or in the cloud with any LLM. This directly matches delegating tasks to a built-in assistant within the product. Missing for 10: independent/hands-on user reports validating the delegation experience beyond vendor docs, and more detail on task delegation depth (e.g., multi-step autonomy limits).", "evidenceIds": [ "openhands-docs-1", "openhands-docs-2", "openhands-docs-3", "openhands-gh-1", "openhands-gh-2", "openhands-probe-3" ] }, { "productId": "openhands", "storyId": "agentic-headless", "verdict": "full", "quality": 8, "confidence": "medium", "rationale": "OpenHands supports CLI mode with auto-approve for non-interactive runs, API support for automation/scripting, webhook/schedule-triggered automations (Slack, GitHub, Linear), and can run headless in Docker/VMs/servers — all consistent with CI/automation usage. Missing for 10: an explicit first-party CI pipeline example (e.g., GitHub Actions config) or independent hands-on confirmation of headless CI runs.", "evidenceIds": [ "openhands-docs-3", "openhands-docs-13", "openhands-gh-4", "openhands-gh-5", "openhands-probe-3" ] }, { "productId": "openhands", "storyId": "agentic-mcp-client", "verdict": "none", "quality": 0, "confidence": "low", "rationale": "OpenHands is an AI coding agent, so the axis of consuming MCP servers as a client is applicable (unlike serving MCP, which would be na for an agent), but no evidence in the pack mentions MCP integration, configuration, or tool-plugging capability at all.", "evidenceIds": [] }, { "productId": "openhands", "storyId": "agentic-mcp-server", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "OpenHands is itself an AI coding agent, so serving as an MCP server for other agents is a different product role (agent-as-client, not server) — this axis is out of scope per the agent-role exception. No evidence shows OpenHands running as an MCP server endpoint.", "evidenceIds": [] }, { "productId": "openhands", "storyId": "agentic-nl-commands", "verdict": "full", "quality": 8, "confidence": "high", "rationale": "OpenHands' CLI mode explicitly lets users type natural language tasks and get instant feedback, resume conversations, and control approvals, directly matching the story, and this is corroborated by an official documented CLI probe. Missing for 10: independent/hands-on user reports validating the natural-language interaction quality beyond vendor docs.", "evidenceIds": [ "openhands-docs-1", "openhands-docs-2", "openhands-docs-3", "openhands-probe-3" ] }, { "productId": "openhands", "storyId": "agentic-official-cli", "verdict": "full", "quality": 8, "confidence": "high", "rationale": "OpenHands documents an official CLI mode with natural language task execution, conversation resumption, and auto-approve controls, confirmed by dedicated docs pages and a probe verifying the CLI documentation page exists. Missing for 10: independent/hands-on third-party corroboration beyond vendor docs, and more detail on CLI installation/distribution mechanics.", "evidenceIds": [ "openhands-docs-1", "openhands-docs-2", "openhands-docs-3", "openhands-probe-3" ] }, { "productId": "openhands", "storyId": "agentic-public-api", "verdict": "full", "quality": 8, "confidence": "medium", "rationale": "OpenHands exposes a documented OpenAPI spec (openapi.json) plus an llms.txt LLM-friendly docs index, and pricing docs explicitly mention 'API support for automation and scripting,' confirming a public, documented API for programmatic/agentic control. missing for 10: independent third-party corroboration of API usage/reliability and more detailed API reference docs beyond the openapi.json probe.", "evidenceIds": [ "openhands-probe-2", "openhands-probe-1", "openhands-docs-13" ] }, { "productId": "openhands", "storyId": "agentic-scoped-keys", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "Evidence shows OpenHands supports bringing your own LLM keys, SAML/SSO, and general API access, but nothing describes issuing scoped or least-privilege API credentials specifically for an agent's actions/tool access.", "evidenceIds": [] }, { "productId": "openhands", "storyId": "agentic-sdks", "verdict": "full", "quality": 8, "confidence": "medium", "rationale": "OpenHands documents an official Software Agent SDK ('a composable Python library for building agents that work with code') plus a public OpenAPI spec and llms.txt index, giving AI-native users concrete official interfaces to build against. Missing for 10: independent/third-party corroboration of SDK usage, and richer SDK-specific docs (examples, API reference depth) beyond the single description.", "evidenceIds": [ "openhands-docs-6", "openhands-probe-2", "openhands-probe-1" ] }, { "productId": "openhands", "storyId": "agentic-webhooks", "verdict": "partial", "quality": 5, "confidence": "low", "rationale": "GitHub README explicitly states OpenHands can 'run on a schedule or in response to webhook events' for automations, indicating webhook-triggered event subscription, but there is no dedicated documentation page detailing webhook setup, payload schema, or event types. missing for 10: dedicated webhook docs/config guide, independent/hands-on confirmation, and detail on which events can be subscribed to.", "evidenceIds": [ "openhands-gh-4" ] }, { "productId": "openhands", "storyId": "ai-automated-test-generation-on-change", "verdict": "none", "quality": 0, "confidence": "low", "rationale": "No evidence in the pack describes OpenHands agents autonomously generating or running tests to validate their own code changes before proposing them; the docs/GitHub excerpts cover CLI usage, multi-agent backends, automations, and pricing, but not test-generation/self-validation workflows.", "evidenceIds": [] }, { "productId": "openhands", "storyId": "ai-automatic-model-routing", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "Evidence shows OpenHands supports using any LLM and switching between agent backends manually, but there is no mention of automatic routing of prompts to the most suitable model based on task characteristics. Missing for 10: any model-routing/selection logic, per-task model suitability heuristics, or documentation of automatic model selection.", "evidenceIds": [ "openhands-gh-2", "openhands-gh-1", "openhands-docs-15" ] }, { "productId": "openhands", "storyId": "ai-issue-triage", "verdict": "full", "quality": 8, "confidence": "medium", "rationale": "openhands-docs-7 directly describes an automation that watches for new bugs/incidents, gathers logs and recent changes, suggests severity, and routes the issue to the right owner — matching the story closely, backed by GitHub-documented webhook/scheduled automation infrastructure (openhands-gh-4). Missing for 10: independent/hands-on verification of triage accuracy and routing correctness beyond vendor's own site copy.", "evidenceIds": [ "openhands-docs-7", "openhands-gh-4" ] }, { "productId": "openhands", "storyId": "api-interactive-docs", "verdict": "partial", "quality": 3, "confidence": "low", "rationale": "Evidence shows an OpenAPI spec is served (openhands-probe-2) and API support is advertised for automation/scripting (openhands-docs-13), implying some API reference exists, but there is no evidence of an interactive documentation UI (e.g., Swagger/Redoc 'try it' console) or runnable code examples tied to that spec. missing for 10: interactive API explorer UI, runnable/try-it code examples, confirmation the openapi.json is rendered as browsable docs.", "evidenceIds": [ "openhands-probe-2", "openhands-docs-13" ] }, { "productId": "openhands", "storyId": "api-machine-spec", "verdict": "full", "quality": 9, "confidence": "high", "rationale": "A probe confirms an OpenAPI spec is served at docs.openhands.dev/openapi.json with a valid 'openapi' key, directly satisfying the machine-readable API spec requirement, and API support for automation/scripting is also documented in pricing. Missing for 10: independent third-party corroboration or detailed docs describing spec coverage/versioning.", "evidenceIds": [ "openhands-probe-2", "openhands-docs-13" ] }, { "productId": "openhands", "storyId": "api-sandbox", "verdict": "partial", "quality": 4, "confidence": "low", "rationale": "OpenHands can run agents in Docker/VMs (openhands-gh-5), which implies isolated execution rather than direct production access, but the evidence pack never explicitly describes a sandbox environment for testing safely against non-production data. Missing for 10: explicit documentation of sandbox/isolation guarantees, workspace-vs-production data separation, and any hands-on confirmation that production systems are protected.", "evidenceIds": [ "openhands-gh-5" ] }, { "productId": "openhands", "storyId": "api-versioning-policy", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "Evidence shows OpenHands exposes an OpenAPI spec and general 'API support for automation and scripting,' and docs reference a 'V1' with legacy V0 pages excluded, but there is no documented API versioning scheme or deprecation policy anywhere in the pack.", "evidenceIds": [ "openhands-probe-2", "openhands-docs-13", "openhands-probe-1" ] }, { "productId": "openhands", "storyId": "approve-plan-before-execution", "verdict": "none", "quality": 0, "confidence": "low", "rationale": "The evidence pack mentions auto-approve controls and CLI usage but contains no mention of a plan-review/approval step before code changes are made; no planning-phase or approval-gate feature is documented. missing for 10: any documentation of a plan-generation step, an approval/confirmation gate prior to code edits, or user testimony confirming such a workflow exists.", "evidenceIds": [] }, { "productId": "openhands", "storyId": "approve-task-contract", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "No evidence of a scope/contract approval workflow gating agent repository modifications; only auto-approve settings and general automation features are documented, not a pre-execution scope-approval gate for engineering leads.", "evidenceIds": [ "openhands-docs-3" ] }, { "productId": "openhands", "storyId": "assign-task-from-ticket", "verdict": "partial", "quality": 5, "confidence": "medium", "rationale": "OpenHands supports automations that integrate with GitHub and Linear and can respond to webhook events, plus label-triggered PR review and bug-watching automations, implying issue/ticket-triggered agent runs are possible. However there's no explicit documentation of a workflow where a developer directly assigns a specific issue/ticket to an agent (e.g. via an 'assign to OpenHands' button or issue-comment trigger) as opposed to general automation setup. Missing for 10: explicit documentation of issue-to-agent assignment UX (e.g., GitHub issue comment/label triggering agent to pick up that specific ticket), independent/hands-on confirmation of this workflow, and ticketing system coverage beyond GitHub/Linear mentions.", "evidenceIds": [ "openhands-gh-4", "openhands-docs-8", "openhands-docs-7" ] }, { "productId": "openhands", "storyId": "auto-approve-all-actions", "verdict": "full", "quality": 8, "confidence": "medium", "rationale": "Docs explicitly list an 'Auto-approve all actions (use with caution)' setting for CLI mode, directly matching the story. Missing for 10: independent/hands-on confirmation and details on scope (per-action vs global) or how to configure it beyond CLI mode.", "evidenceIds": [ "openhands-docs-3", "openhands-probe-3" ] }, { "productId": "openhands", "storyId": "auto-fix-readiness-gaps", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "The evidence shows OpenHands can fix failing CI workflows, remediate security alerts, and review PRs, but nothing ties this to a defined 'agent-readiness' criteria/checklist concept that an engineering lead could gate on and auto-remediate. Missing for 10: any mention of agent-readiness scoring, a checklist/criteria framework, or evidence that OpenHands detects and fixes failures against such a standard.", "evidenceIds": [] }, { "productId": "openhands", "storyId": "auto-open-pr-on-completion", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "OpenHands documents workflow automations that open pull requests automatically for specific triggers (failed CI, security alerts) via [openhands-docs-9] and [openhands-docs-10], showing the underlying capability exists. However, there's no direct documentation of configuring a general coding-task agent to auto-open a PR upon arbitrary task completion — the evidence only covers specific automation templates (bug-fix, security remediation) rather than a generic 'open PR on task completion' setting. Missing for 10: explicit config option/flag for auto-PR-on-completion in standard task workflows, independent/hands-on confirmation of this behavior.", "evidenceIds": [ "openhands-docs-9", "openhands-docs-10", "openhands-gh-4" ] }, { "productId": "openhands", "storyId": "automated-pr-review", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "OpenHands documents a specific automation that watches for a configurable PR label, inspects full PR context, and posts an AI review comment, which directly matches automated PR review with AI-generated comments. However, it is label-triggered rather than automatic on every PR, and posts once per label event rather than full inline (line-by-line) comments, so it's a partial match to 'every pull request' with 'inline comments'. missing for 10: evidence of automatic triggering on all PRs without manual labeling, confirmation of true inline (line-level) code comments vs a single summary comment, and independent/hands-on verification of this workflow in practice.", "evidenceIds": [ "openhands-docs-8" ] }, { "productId": "openhands", "storyId": "automatic-environment-setup", "verdict": "partial", "quality": 4, "confidence": "low", "rationale": "Evidence shows OpenHands runs agents in configurable sandboxed backends (Docker, VMs, local/remote/cloud) and supports natural-language task execution with auto-approve, which implies some autonomous environment setup, but no citation explicitly confirms the agent auto-clones repos or installs dependencies on its own. Missing for 10: explicit documentation of repo cloning, dependency installation, and end-to-end environment bootstrap steps.", "evidenceIds": [ "openhands-gh-5", "openhands-docs-1", "openhands-docs-3" ] }, { "productId": "openhands", "storyId": "automation-bulk-operations", "verdict": "partial", "quality": 5, "confidence": "low", "rationale": "OpenHands supports automations/workflows triggered by events (Slack, GitHub, Linear, webhooks) and API access for scripting, which enables some multi-item automation, but there's no explicit evidence of a bulk-operation feature (e.g., processing a batch list of items/tasks in one command or UI action). missing for 10: explicit bulk/batch operation feature, evidence of processing multiple items in a single invocation, UI/CLI support for batch task lists, independent confirmation of scale.", "evidenceIds": [ "openhands-gh-4", "openhands-docs-13", "openhands-docs-7", "openhands-docs-9" ] }, { "productId": "openhands", "storyId": "automation-rules-engine", "verdict": "full", "quality": 7, "confidence": "medium", "rationale": "OpenHands ships documented automation triggers—scheduled runs, webhook events, PR label events, Slack mentions, failed-workflow detection, and security-alert triage—each automatically invoking an agent action, matching the 'rules trigger actions on events' story (openhands-gh-4, openhands-docs-7 to openhands-docs-11). missing for 10: independent/hands-on verification that users can define fully custom rule logic (vs. fixed preset automations), and no evidence of a general-purpose rule-authoring UI or DSL for arbitrary event/action pairing", "evidenceIds": [ "openhands-gh-4", "openhands-docs-7", "openhands-docs-8", "openhands-docs-9", "openhands-docs-10", "openhands-docs-11" ] }, { "productId": "openhands", "storyId": "automation-scheduled-jobs", "verdict": "full", "quality": 8, "confidence": "medium", "rationale": "OpenHands explicitly supports creating automations/workflows that run on a schedule or in response to webhook events, integrating with Slack, GitHub, Linear, etc., which directly matches recurring job scheduling. Missing for 10: independent/hands-on corroboration of the scheduling UI/config and details on job management (pause/edit/monitor recurring jobs).", "evidenceIds": [ "openhands-gh-4", "openhands-docs-7", "openhands-docs-8", "openhands-docs-9", "openhands-docs-10", "openhands-docs-11" ] }, { "productId": "openhands", "storyId": "automation-versioned-workflows", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "Evidence shows OpenHands can create automations/workflows (Slack, GitHub, Linear integrations, scheduled/webhook triggers) but nothing in the pack describes version history, review, or rollback mechanisms specifically for these automations themselves — no changelog, diff view, or revert feature is documented.", "evidenceIds": [ "openhands-gh-4", "openhands-docs-7", "openhands-docs-8", "openhands-docs-9", "openhands-docs-10", "openhands-docs-11" ] }, { "productId": "openhands", "storyId": "autonomous-bug-fixing", "verdict": "full", "quality": 8, "confidence": "medium", "rationale": "OpenHands ships explicit automation for bug diagnosis and fixing: watching for new bugs/incidents and gathering logs (openhands-docs-7), detecting failed workflows/inspecting logs/identifying cause and opening a PR fix (openhands-docs-9), and general natural-language task execution via CLI (openhands-docs-1). This directly matches autonomous bug diagnosis-and-fix, though missing for 10: independent hands-on verification of fix success rate and end-to-end case studies beyond vendor docs.", "evidenceIds": [ "openhands-docs-7", "openhands-docs-9", "openhands-docs-1", "openhands-docs-6" ] }, { "productId": "openhands", "storyId": "bring-your-own-model", "verdict": "full", "quality": 8, "confidence": "high", "rationale": "GitHub docs explicitly state OpenHands can be used with any LLM, and pricing docs confirm both Open Source and Cloud plans support bringing your own LLM keys, directly matching the story. Missing for 10: independent/hands-on verification of BYO-key setup and any model-specific limitations or edge cases.", "evidenceIds": [ "openhands-gh-2", "openhands-docs-15" ] }, { "productId": "openhands", "storyId": "chat-with-agent-in-slack", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "OpenHands documents a Slack integration where the agent watches for @openhands mentions, opens a conversation with the message context, and replies when done (openhands-docs-11), which matches tagging an agent in a chat thread to delegate a task. However this is limited to Slack specifically (not other chat platforms), appears tied to the Cloud/automation feature set rather than the core product, and lacks independent/hands-on corroboration. Missing for 10: support for other chat platforms (e.g., Teams, Discord), independent verification of the Slack flow working in practice, and detail on how delegated context/threading is preserved during the exchange.", "evidenceIds": [ "openhands-docs-11", "openhands-gh-4" ] }, { "productId": "openhands", "storyId": "ci-failure-auto-fix", "verdict": "full", "quality": 7, "confidence": "medium", "rationale": "OpenHands documents a dedicated automation that detects failed CI workflows, inspects logs, identifies the likely cause, and opens a pull request with a proposed fix — matching the story directly. Missing for 10: independent/hands-on verification of this automation working in practice, and detail on configuration/setup beyond the marketing description.", "evidenceIds": [ "openhands-docs-9", "openhands-gh-4" ] }, { "productId": "openhands", "storyId": "codebase-context-file", "verdict": "none", "quality": 0, "confidence": "low", "rationale": "No evidence in this pack mentions a repo-level context/convention file (e.g., microagents, custom instructions, or similar) that developers can add to guide agent behavior; the pack only covers CLI usage, deployment, integrations, and pricing.", "evidenceIds": [] }, { "productId": "openhands", "storyId": "concurrent-task-execution", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "OpenHands supports running agents across many backends (local, Docker, VMs, cloud) and building automations triggered by schedules or webhooks (Slack, GitHub, Linear), plus API access for scripting — all of which enable running multiple tasks in parallel across infrastructure. However, there is no explicit documentation of a dashboard or orchestration layer for managing many concurrent agent runs, concurrency limits, or throughput metrics for an engineering lead. Missing for 10: explicit multi-task concurrency/orchestration UI, stated concurrency limits or scaling guarantees, and independent evidence of teams running many parallel agents successfully.", "evidenceIds": [ "openhands-gh-4", "openhands-gh-5", "openhands-docs-13", "openhands-docs-4" ] }, { "productId": "openhands", "storyId": "connect-github-repo", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "GitHub is explicitly listed as an integration target for OpenHands automations, and multiple documented workflows show the agent opening pull requests (fixing failed CI, remediating security alerts, responding to PR-review label events), which implies repo access and PR creation. However, there is no first-party documentation of the actual repo-connection/auth flow (e.g., installing a GitHub App, granting repo scopes) or hands-on confirmation that this works end-to-end. Missing for 10: explicit repo-connection setup docs, evidence of PR creation permissions/scopes, and independent verification of successful PRs opened.", "evidenceIds": [ "openhands-gh-4", "openhands-docs-9", "openhands-docs-10", "openhands-docs-8" ] }, { "productId": "openhands", "storyId": "daily-task-quota-limits", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "Evidence shows pricing page mentions plan features like SSO, API access, and BYO LLM keys, but nothing about daily task limits, concurrency limits, or any management dashboard for such limits.", "evidenceIds": [ "openhands-docs-12", "openhands-docs-13", "openhands-docs-14", "openhands-docs-15" ] }, { "productId": "openhands", "storyId": "delegate-sessions-for-teammates", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "Evidence shows enterprise features like SSO/SAML and API access, which imply some multi-user org management, but there is no evidence of an engineering-lead being able to create or launch agent sessions on behalf of other users. Missing for 10: any documentation of delegated session creation, admin-initiated sessions, or role-based session provisioning for other org members.", "evidenceIds": [ "openhands-docs-14", "openhands-docs-13", "openhands-docs-5" ] }, { "productId": "openhands", "storyId": "describe-feature-in-plain-language", "verdict": "partial", "quality": 5, "confidence": "medium", "rationale": "OpenHands lets users type natural-language tasks directly in the CLI and receive feedback, which is the core mechanism for turning plain-language input into agent-executed work, and automation triggers (Slack mentions, PR labels, failed workflows) show it converting informal signals into concrete PRs/tasks. However, there is no evidence of an explicit 'spec' or scoped task artifact (e.g., a generated plan, ticket, or requirements doc) being produced before implementation — missing for 10: explicit task-scoping/spec generation step, evidence of a structured plan artifact, and independent confirmation that vague bug/feature descriptions reliably become well-scoped tasks rather than direct code edits.", "evidenceIds": [ "openhands-docs-1", "openhands-docs-7", "openhands-docs-8", "openhands-docs-9", "openhands-docs-11" ] }, { "productId": "openhands", "storyId": "design-to-code-handoff", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "The evidence shows OpenHands executes natural-language coding tasks and automations, but nothing in the pack addresses ingesting mockups/design files or a PM-oriented, engineer-free workflow from design to implementation. missing for 10: mockup/design ingestion capability, no-code PM-facing workflow evidence, any example of design-to-code handoff elimination.", "evidenceIds": [ "openhands-docs-1", "openhands-gh-4" ] }, { "productId": "openhands", "storyId": "diff-review-before-pr", "verdict": "partial", "quality": 4, "confidence": "low", "rationale": "OpenHands has an auto-approve/manual-approve action mode (openhands-docs-3 implies a default confirmation step exists before auto-approve is enabled) and can open PRs after agent work, suggesting some human-in-the-loop gating exists, but there's no explicit documentation of a diff review UI or an approval gate specifically before PR creation. missing for 10: explicit diff-review interface, documented approve/reject step tied to PR creation, evidence of a review-before-merge workflow.", "evidenceIds": [ "openhands-docs-3", "openhands-docs-8" ] }, { "productId": "openhands", "storyId": "end-to-end-feature-implementation", "verdict": "partial", "quality": 5, "confidence": "medium", "rationale": "OpenHands is positioned as an autonomous coding agent that takes natural language tasks and works across CLI, cloud, and automation workflows (e.g., PR review, bug-fixing, incident response), implying it can implement features end-to-end, but the evidence pack lacks a concrete first-party or hands-on example of the agent writing a feature plus tests. Missing for 10: explicit documentation/demo of full feature-implementation-with-tests workflow, independent benchmark or hands-on verification of test-writing capability, and confirmation of end-to-end PR creation including tests.", "evidenceIds": [ "openhands-docs-1", "openhands-docs-3", "openhands-gh-3", "openhands-docs-9" ] }, { "productId": "openhands", "storyId": "enterprise-sso-licensing", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "OpenHands explicitly markets an Enterprise tier with licensed self-hosting/managed deployment and commercial support (openhands-docs-5), and the pricing page lists Enterprise SAML/SSO (openhands-docs-14). However, there is no detail on contract terms, SLA specifics, or independent confirmation of enterprise rollouts. Missing for 10: concrete SLA/support-tier documentation, case studies or third-party validation of enterprise SSO rollout, and clarity on org-wide admin/governance controls.", "evidenceIds": [ "openhands-docs-5", "openhands-docs-14", "openhands-docs-4" ] }, { "productId": "openhands", "storyId": "feedback-to-task-conversion", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "OpenHands integrates with Linear/GitHub/Slack for automations and can process natural-language tasks, so the general axis of turning input into work items is plausible, but there is no evidence of a feature that ingests user feedback and outputs a structured task with proposed scope for PM workflows.", "evidenceIds": [] }, { "productId": "openhands", "storyId": "headless-ci-execution", "verdict": "full", "quality": 7, "confidence": "medium", "rationale": "Docs show a CLI mode with an 'auto-approve all actions' flag for non-interactive runs, and GitHub evidence describes running agents locally/Docker/VMs and triggering automations 'on a schedule or in response to webhook events,' plus API support for automation/scripting — all consistent with headless CI/CD use. Missing for 10: an explicit CI/CD pipeline example (e.g., GitHub Actions YAML snippet) or independent hands-on confirmation of headless exit-code/scripting behavior.", "evidenceIds": [ "openhands-docs-3", "openhands-probe-3", "openhands-gh-4", "openhands-gh-5", "openhands-docs-13" ] }, { "productId": "openhands", "storyId": "isolated-sandbox-execution", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "Evidence confirms OpenHands supports running agents in isolated environments (Docker/VMs) and explicitly references 'sandbox infrastructure' as part of its backend, implying code execution is sandboxed. However, there is no detailed documentation on dependency installation within the sandbox, security guarantees, or isolation mechanics beyond these brief mentions. missing for 10: explicit sandbox architecture docs, dependency-installation workflow details, isolation/security guarantees, independent verification of sandbox safety.", "evidenceIds": [ "openhands-gh-5", "openhands-docs-4" ] }, { "productId": "openhands", "storyId": "issue-tracker-sync", "verdict": "partial", "quality": 4, "confidence": "medium", "rationale": "OpenHands documents automation workflows that integrate with Linear (and Slack/GitHub) via webhooks/schedules, showing some issue-tracker connectivity, but Jira, ClickUp, and Monday.com are never mentioned anywhere in the evidence pack — only vague 'and more' language covers them. missing for 10: explicit Jira/ClickUp/Monday.com integrations, docs on ticket management workflows beyond Linear, evidence of two-way ticket manipulation (create/update/close) rather than just webhook triggers.", "evidenceIds": [ "openhands-gh-4" ] }, { "productId": "openhands", "storyId": "live-agent-activity-monitoring", "verdict": "partial", "quality": 4, "confidence": "low", "rationale": "OpenHands CLI mode offers instant feedback on tasks and resumable conversations, implying some real-time interaction, but there's no explicit evidence of a live status dashboard, streaming action log, or step-by-step progress view while an agent runs. missing for 10: explicit real-time status/progress UI documentation, evidence of live action streaming or step visibility, independent hands-on confirmation of watching an agent live.", "evidenceIds": [ "openhands-docs-1", "openhands-docs-2", "openhands-probe-3" ] }, { "productId": "openhands", "storyId": "managed-cloud-execution", "verdict": "full", "quality": 8, "confidence": "medium", "rationale": "OpenHands Cloud is explicitly documented as the managed commercial service for running OpenHands without operating your own backend and sandbox infrastructure, with hosted access, API support, and bring-your-own-LLM-key options detailed on the pricing page. Missing for 10: independent/hands-on corroboration of the cloud offering's reliability and scale, and more detail on infrastructure limits or SLAs.", "evidenceIds": [ "openhands-docs-4", "openhands-docs-12", "openhands-docs-13", "openhands-docs-15" ] }, { "productId": "openhands", "storyId": "manual-model-override", "verdict": "partial", "quality": 5, "confidence": "low", "rationale": "Evidence confirms OpenHands can be used with any LLM and lets users bring their own LLM keys, implying model choice is configurable, but there is no explicit documentation of an 'automatic model selection' default or a UI/CLI toggle to override it. Missing for 10: explicit docs on default/automatic model selection behavior, step-by-step instructions for switching models, and independent confirmation of the switch working in practice.", "evidenceIds": [ "openhands-gh-2", "openhands-docs-15" ] }, { "productId": "openhands", "storyId": "mid-task-takeover", "verdict": "partial", "quality": 5, "confidence": "low", "rationale": "OpenHands supports resuming conversations and switching between local/remote/cloud agents 'without losing focus,' implying some cross-surface continuity, but there's no explicit documentation of a developer taking over a live in-progress task from editor/terminal/browser mid-run with redirect capability. missing for 10: explicit IDE/editor integration for live takeover, concrete UI/UX description of mid-task redirect, and independent hands-on confirmation of seamless handoff across all three surfaces.", "evidenceIds": [ "openhands-gh-1", "openhands-gh-3", "openhands-docs-2" ] }, { "productId": "openhands", "storyId": "mobile-approval-of-decisions", "verdict": "partial", "quality": 4, "confidence": "low", "rationale": "OpenHands Cloud offers 'hosted cloud access from desktop and mobile' and Slack-based interaction (@mentions, replies) that could let a PM check in remotely, but there is no documented feature for approving specific in-progress agent actions/decisions via a mobile interface while the agent continues autonomously — the only approval-related control mentioned is a blanket 'auto-approve all actions' CLI flag, not selective human-in-the-loop approval. Missing for 10: explicit mobile approval/confirmation UI, human-in-the-loop decision gating documentation, evidence of push notifications or approval prompts reaching a phone.", "evidenceIds": [ "openhands-docs-12", "openhands-docs-11", "openhands-docs-3" ] }, { "productId": "openhands", "storyId": "one-click-repo-access", "verdict": "partial", "quality": 4, "confidence": "low", "rationale": "OpenHands Cloud offers hosted access and integrations (Slack, GitHub, Linear, webhooks) suggesting some repo connection flow, but there is no concrete evidence of a one-click repo install/auth flow — no screenshots, GitHub App install steps, or onboarding walkthrough. missing for 10: documented one-click GitHub/GitLab App install flow, evidence of minimal setup steps, independent confirmation of ease of onboarding.", "evidenceIds": [ "openhands-gh-4", "openhands-docs-4", "openhands-docs-12" ] }, { "productId": "openhands", "storyId": "openness-api-parity", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "OpenHands exposes a documented OpenAPI spec and dedicated 'API support for automation and scripting' tier, plus CLI mode with resume/auto-approve that mirrors UI conversation flows, suggesting broad API parity. However, no evidence explicitly confirms that every UI feature (e.g., cloud dashboard views, integrations config, enterprise SSO management) is fully API-accessible. Missing for 10: explicit parity documentation enumerating UI vs API feature coverage, and independent/hands-on confirmation that API can replicate all UI workflows.", "evidenceIds": [ "openhands-probe-2", "openhands-docs-13", "openhands-docs-1", "openhands-docs-2", "openhands-docs-3", "openhands-probe-3" ] }, { "productId": "openhands", "storyId": "openness-full-export", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "No evidence in the pack describes an explicit data export feature or open-format data portability; only conversation resume and self-hosting options are mentioned, not a way to export/leave with all user data. missing for 10: explicit export/download feature, documented open data formats, statement on data portability upon leaving the platform.", "evidenceIds": [] }, { "productId": "openhands", "storyId": "openness-open-license", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "The GitHub repository (openhands-gh-1..5) confirms the source code is publicly hosted and readable, and openhands-docs-15 explicitly references an 'OpenHands Open Source' plan/tier, implying the core project is open-licensed. However, no evidence pack item names the specific license (e.g., MIT/Apache) or points to a LICENSE file, so full open-license confirmation is unverified. Missing for 10: explicit license name/file citation, independent confirmation of license terms.", "evidenceIds": [ "openhands-gh-1", "openhands-docs-15" ] }, { "productId": "openhands", "storyId": "openness-self-host", "verdict": "full", "quality": 8, "confidence": "high", "rationale": "Evidence shows OpenHands can be run locally/self-hosted (Docker, VMs, or any agent server backend) as opposed to only using the managed Cloud/Enterprise offerings, and it is open-source with an SDK for building on it. Missing for 10: independent hands-on confirmation of a full self-hosted setup (e.g., a third-party report of running the entire stack without cloud dependencies) and detailed self-hosting infra requirements/documentation.", "evidenceIds": [ "openhands-gh-5", "openhands-docs-4", "openhands-docs-6", "openhands-docs-15" ] }, { "productId": "openhands", "storyId": "privacy-data-residency", "verdict": "none", "quality": 0, "confidence": "low", "rationale": "No evidence of data residency/region selection controls anywhere in the pack; only self-hosting options (local/Docker/VM) are mentioned, which is a workaround, not a documented region-choice feature for the managed/cloud offering.", "evidenceIds": [] }, { "productId": "openhands", "storyId": "privacy-no-training", "verdict": "none", "quality": 0, "confidence": "low", "rationale": "No evidence in the pack addresses data usage for AI model training, opt-out policies, or any privacy commitments regarding training data; the pack only covers CLI usage, deployment options, and integrations.", "evidenceIds": [] }, { "productId": "openhands", "storyId": "privacy-retention-controls", "verdict": "none", "quality": 0, "confidence": "low", "rationale": "No evidence pack items describe data retention policies, deletion controls, or user-facing settings for managing stored conversation/data lifecycle; while OpenHands is open-source and self-hostable (implying some inherent control), no explicit retention/deletion feature or documentation is cited.", "evidenceIds": [] }, { "productId": "openhands", "storyId": "privacy-telemetry-optout", "verdict": "none", "quality": 0, "confidence": "low", "rationale": "No evidence in the pack mentions telemetry, usage tracking, analytics, or opt-out settings; the pack covers CLI features, agent orchestration, and pricing tiers only.", "evidenceIds": [] }, { "productId": "openhands", "storyId": "query-repo-documentation", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "No evidence of a documentation-generation or repo-doc-querying feature; OpenHands is a coding agent focused on tasks, automations, and integrations, not a repo documentation search/query tool.", "evidenceIds": [] }, { "productId": "openhands", "storyId": "repo-readiness-report", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "No evidence of a repository readiness assessment/report feature for autonomous agents; OpenHands' evidence covers task execution, automations, and integrations but nothing about scoring or auditing repo readiness.", "evidenceIds": [] }, { "productId": "openhands", "storyId": "security-alert-remediation", "verdict": "full", "quality": 8, "confidence": "medium", "rationale": "openhands-docs-10 explicitly describes an automation that reviews security alerts, validates the finding, updates affected code, and opens a pull request with the remediation, directly matching the story. This is a first-party vendor claim without independent/hands-on corroboration or detail on validation accuracy. Missing for 10: independent verification of remediation quality, and detail on how 'validated' findings are confirmed before code changes.", "evidenceIds": [ "openhands-docs-10" ] }, { "productId": "openhands", "storyId": "self-hosted-agent-deployment", "verdict": "full", "quality": 8, "confidence": "high", "rationale": "GitHub docs explicitly state agents can be run locally, in Docker, on VMs, or any agent server backend, and Enterprise offers licensed self-hosting for organizations. This directly matches the engineering-lead's need for flexible self-hosted deployment. Missing for 10: independent/hands-on verification of self-hosting setup complexity and no detailed self-host deployment guide excerpt in evidence.", "evidenceIds": [ "openhands-gh-5", "openhands-docs-5", "openhands-gh-1", "openhands-gh-3" ] }, { "productId": "openhands", "storyId": "steer-active-session", "verdict": "partial", "quality": 5, "confidence": "medium", "rationale": "CLI mode docs mention typing natural language tasks with instant feedback and resuming previous conversations, which implies interactive follow-up steering, but there is no explicit evidence describing sending new instructions mid-task to an already-running/active agent session without restarting it. missing for 10: explicit documentation of mid-session steering/interrupt-and-redirect behavior while an agent is actively working, and independent/hands-on confirmation that follow-up messages are incorporated without restarting the session.", "evidenceIds": [ "openhands-docs-1", "openhands-docs-2", "openhands-probe-3" ] }, { "productId": "openhands", "storyId": "task-completion-notifications", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "OpenHands supports Slack integration where it 'replies when the agent finishes' and workflow automations tied to webhook events (Slack, GitHub, Linear), which implies notification-like behavior on task completion; CLI mode also gives instant feedback in interactive sessions. However, there's no explicit evidence of a dedicated notification system for 'needs my input' events or push/desktop alerts outside Slack context. missing for 10: explicit documentation of notifications when agent needs human input/approval, native mobile/desktop push alerts, and independent/hands-on confirmation of notification reliability.", "evidenceIds": [ "openhands-docs-11", "openhands-gh-4", "openhands-docs-1" ] }, { "productId": "openhands", "storyId": "tiered-autonomy-levels", "verdict": "partial", "quality": 4, "confidence": "low", "rationale": "Evidence shows only a binary confirmation toggle (auto-approve all actions vs manual confirmation) in CLI mode, not a tiered/granular autonomy system with multiple configurable levels for an engineering lead to set. Missing for 10: documented multi-tier permission/autonomy levels, admin controls to enforce team-wide policies, and per-action or per-risk-category confirmation settings.", "evidenceIds": [ "openhands-docs-3" ] }, { "productId": "openhands", "storyId": "trigger-agent-on-build-failure", "verdict": "partial", "quality": 7, "confidence": "medium", "rationale": "OpenHands advertises a workflow that watches for failed CI/CD workflows, inspects logs, identifies causes, and opens a PR with a fix (openhands-docs-9), plus webhook/schedule-triggered automations (openhands-gh-4) and API support for scripting (openhands-docs-13), which together support triggering an agent from CI/CD to fix broken builds. However, there is no concrete example of GitHub Actions/CI pipeline configuration, no evidence of test-failure-specific triggers, and no independent/hands-on confirmation that this works as described. Missing for 10: explicit CI pipeline integration docs/examples, test-failure-specific triggers, third-party verification.", "evidenceIds": [ "openhands-docs-9", "openhands-gh-4", "openhands-docs-13", "openhands-docs-6" ] }, { "productId": "openhands", "storyId": "visual-context-for-tasks", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "No evidence anywhere in the pack that OpenHands supports attaching images, screenshots, or mockups to a task, or that the agent can interpret visual markup to drive implementation; documentation focuses on text-based CLI tasks, integrations, and automations.", "evidenceIds": [] }, { "productId": "yylo", "storyId": "agentic-agent-docs", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "Direct probes show no llms.txt (404), no docs.md machine-readable variant (404), and no OpenAPI spec (404), meaning there is no agent-oriented docs endpoint to point an agent at; the only docs are standard human-facing HTML pages.", "evidenceIds": [ "yylo-probe-1", "yylo-probe-2", "yylo-probe-3" ] }, { "productId": "yylo", "storyId": "agentic-ai-insights", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "YYLO is an orchestration/CLI tool for running coding agents, workflows, and task ledgers; it launches subagents to perform tasks but nothing in the evidence describes it generating analytic insights or suggestions from a user's own data. Merge/validation explicitly avoids invoking models (yylo-gh-6), and no docs mention dashboards, analytics, or AI-generated insight surfacing.", "evidenceIds": [ "yylo-gh-6", "yylo-docs-1", "yylo-docs-11" ] }, { "productId": "yylo", "storyId": "agentic-autonomous-automation", "verdict": "partial", "quality": 7, "confidence": "medium", "rationale": "Docs describe multiple mechanisms for autonomous, repeated execution — `yy loop` for repeating shell commands, run-until-completion scripts that iterate until kanban work is done, a workflow-runner for chained multi-step automations, a bounded parallel-runner for concurrent fan-out, and integrations that pull Slack/GitHub work into a kanban queue and post results back. This supports background-style autonomous task execution, and sessions can be resumed via `yy continue SESSION_ID` rather than reconstructed from a terminal. However, there's no evidence of a persistent scheduler/cron-like trigger or a hosted background service — everything appears CLI/session-driven, and there is no independent or hands-on confirmation that these loops truly run unattended over long periods. Missing for 10: evidence of scheduled/triggered automations independent of an active session, and third-party corroboration of long-running unattended execution.", "evidenceIds": [ "yylo-docs-3", "yylo-docs-6", "yylo-docs-7", "yylo-docs-8", "yylo-docs-9", "yylo-docs-10", "yylo-gh-3" ] }, { "productId": "yylo", "storyId": "agentic-builtin-assistant", "verdict": "partial", "quality": 4, "confidence": "medium", "rationale": "YYLO's CLI lets a user delegate a task to an AI agent via `--subagent claude` and manage it through kanban/ledger workflows, so task delegation to an AI is documented, but the AI capability is an external subagent (e.g., Claude) invoked by the orchestrator rather than an assistant built into YYLO itself. missing for 10: evidence of an assistant embedded in the product (not requiring an external model/agent), and any first-party assistant UI/API rather than orchestration of third-party agents.", "evidenceIds": [ "yylo-docs-1", "yylo-gh-3", "yylo-docs-11" ] }, { "productId": "yylo", "storyId": "agentic-headless", "verdict": "full", "quality": 7, "confidence": "medium", "rationale": "YYLO is a CLI orchestrator with scriptable commands (init, task start, ledger, loop, parallel-runner, run-until-completion) that produce structured watch receipts with exit codes and JSON state, and it's installable via npm as a public package with documented bins (yylo, yy, ypl) suitable for CI invocation. missing for 10: no explicit CI pipeline example (e.g., GitHub Actions config), no independent hands-on report of running it headlessly in CI, and llms.txt/openapi probes returned 404 suggesting thinner machine-readable integration docs.", "evidenceIds": [ "yylo-docs-1", "yylo-docs-3", "yylo-docs-6", "yylo-docs-9", "yylo-gh-2", "yylo-gh-3", "yylo-probe-rt-1" ] }, { "productId": "yylo", "storyId": "agentic-mcp-client", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "YYLO's docs describe orchestrating coding agents, ledgers, workflows, and an 'open-standard path' for skills, but no evidence anywhere mentions MCP servers or a mechanism to plug in MCP tools for the agents it orchestrates.", "evidenceIds": [] }, { "productId": "yylo", "storyId": "agentic-mcp-server", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "YYLO is a CLI orchestrator for coding agents/workflows, and as such platform-type product it could plausibly ship an official MCP server for other agents to connect to, but no evidence pack item mentions MCP at all (only 'open-standard' skills installation, ledger, workflow-runner, etc.). No official MCP server documentation, endpoint, or announcement exists in the evidence.", "evidenceIds": [] }, { "productId": "yylo", "storyId": "agentic-nl-commands", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "YYLO's interface is a structured CLI (yylo/yy commands with explicit flags like --task, --subagent) rather than a natural-language command interface; the --task string is passed through to a subagent, not parsed as an operator instruction to YYLO itself. No evidence shows a chat-like or NL command surface for driving YYLO's own operations (init, start, finish, ledger, merge, etc.).", "evidenceIds": [ "yylo-docs-1", "yylo-docs-2", "yylo-gh-3", "yylo-gh-4" ] }, { "productId": "yylo", "storyId": "agentic-official-cli", "verdict": "full", "quality": 8, "confidence": "high", "rationale": "YYLO ships an official CLI (@yylo/cli) with documented commands (init, ledger, loop, doctor, task start, merge land) and is verifiably published on npm plus MIT-licensed on GitHub, matching an AI-native agentic workflow tool. missing for 10: independent third-party usage reports/reviews beyond vendor docs and registry probes, and some llms.txt/docs-md/openapi endpoints 404 suggesting incomplete machine-readable doc surface.", "evidenceIds": [ "yylo-docs-1", "yylo-docs-2", "yylo-docs-11", "yylo-gh-3", "yylo-gh-4", "yylo-probe-rt-1", "yylo-probe-rt-2" ] }, { "productId": "yylo", "storyId": "agentic-public-api", "verdict": "partial", "quality": 5, "confidence": "medium", "rationale": "YYLO exposes a well-documented CLI (`yylo`, `yy`, `ypl`) with structured JSON output, a ledger/task API, YAML workflow contracts, and templating for chaining steps, which an AI-native user could script against — supported by first-party docs and a live public npm package. However, there is no true public HTTP/OpenAPI-style API: explicit probes for llms.txt, docs.md, and OpenAPI specs all returned 404, meaning the only 'API' is the CLI surface, not a documented network API a remote agent could call directly. Missing for 10: an OpenAPI/REST API spec, an llms.txt or machine-readable API manifest, and evidence of remote/programmatic (non-CLI) invocation.", "evidenceIds": [ "yylo-docs-1", "yylo-docs-3", "yylo-docs-4", "yylo-docs-11", "yylo-probe-1", "yylo-probe-2", "yylo-probe-3", "yylo-probe-rt-1" ] }, { "productId": "yylo", "storyId": "agentic-scoped-keys", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "YYLO is a CLI orchestrator for agent coding workflows, not an identity/credential-management system; nothing in the evidence pack relates to issuing scoped API keys or least-privilege credentials for agents, so this axis is a category error for this product type.", "evidenceIds": [] }, { "productId": "yylo", "storyId": "agentic-sdks", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "YYLO ships an official CLI (@yylo/cli) and open MIT-licensed repo, but there is no evidence of an SDK (client library/API) to build against — OpenAPI/spec probes and llms.txt/docs-md endpoints all return 404, and no SDK docs are mentioned anywhere in the pack.", "evidenceIds": [ "yylo-probe-1", "yylo-probe-2", "yylo-probe-3", "yylo-probe-rt-1", "yylo-probe-rt-2" ] }, { "productId": "yylo", "storyId": "agentic-webhooks", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "No evidence of any webhook subscription mechanism; YYLO documents Slack/GitHub integrations pulling into kanban but nothing about outbound event webhooks, and API/openapi probes returned 404s.", "evidenceIds": [ "yylo-docs-10", "yylo-probe-3" ] }, { "productId": "yylo", "storyId": "ai-automated-test-generation-on-change", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "The evidence explicitly states that tests and semantic reviews are 'explicit project checks outside merge' and that YYLO's merge step 'launches no models, chooses no reviewers, schedules no suites, and maintains no validation cache' (yylo-gh-6). While `yy loop` can repeat arbitrary shell commands, there is no evidence of an agent autonomously generating tests or validating its own changes before proposing them.", "evidenceIds": [ "yylo-gh-6", "yylo-docs-3" ] }, { "productId": "yylo", "storyId": "ai-automatic-model-routing", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "Evidence shows YYLO lets users manually specify a subagent/model (e.g. `--subagent claude`) but nothing describes automatic routing of a task prompt to the 'most suitable' underlying model based on task characteristics. missing for 10: any evidence of automatic model-selection logic, routing criteria, or multi-model comparison/selection mechanism.", "evidenceIds": [ "yylo-docs-1" ] }, { "productId": "yylo", "storyId": "ai-issue-triage", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "YYLO's integrations feature only pulls GitHub issues/Slack messages into a kanban board and returns responses to the source thread (yylo-docs-10); there is no evidence of automated severity classification or owner-based routing logic anywhere in the pack.", "evidenceIds": [ "yylo-docs-10" ] }, { "productId": "yylo", "storyId": "api-interactive-docs", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "YYLO is a CLI orchestrator for coding agents, not an API product; there is no evidence of an interactive API reference, and explicit probes for openapi.json/swagger.json and llms.txt all return 404, indicating no such reference exists.", "evidenceIds": [ "yylo-probe-1", "yylo-probe-2", "yylo-probe-3" ] }, { "productId": "yylo", "storyId": "api-machine-spec", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "Explicit probes for llms.txt, docs-md, and OpenAPI/swagger endpoints all returned 404, and no evidence shows any downloadable machine-readable API spec; YYLO is a CLI orchestrator without a documented API surface to spec.", "evidenceIds": [ "yylo-probe-1", "yylo-probe-2", "yylo-probe-3" ] }, { "productId": "yylo", "storyId": "api-sandbox", "verdict": "full", "quality": 7, "confidence": "medium", "rationale": "YYLO's task worktrees isolate work from the protected target SHA, preflight checks are documented as read-only, and merges compose changes into a private detached candidate rather than touching the live branch directly, while the benchmark tool explicitly runs 'isolated attempts' with 'recover safely' semantics—together these describe a sandboxed testing flow that avoids touching the protected/production state. Missing for 10: explicit 'production data' terminology or a dedicated staging/prod environment concept, and independent (non-vendor) confirmation that isolation holds up in practice.", "evidenceIds": [ "yylo-docs-2", "yylo-gh-4", "yylo-gh-1", "yylo-gh-5", "yylo-docs-13" ] }, { "productId": "yylo", "storyId": "api-versioning-policy", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "No evidence of any versioned API contract or documented deprecation policy; the product is a CLI orchestrator with a version like 0.2.1rc6, but no API versioning scheme or deprecation guarantees are mentioned, and openapi/llms.txt probes returned 404.", "evidenceIds": [ "yylo-probe-3", "yylo-probe-1", "yylo-probe-2", "yylo-docs-12" ] }, { "productId": "yylo", "storyId": "approve-plan-before-execution", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "Evidence describes YYLO's task lifecycle (init, start, preflight read-only, finish queuing a candidate, merge land) but nothing indicates the agent produces an implementation plan that a developer reviews and approves before any code is written — preflight/checks occur on already-produced work, not a pre-code plan gate.", "evidenceIds": [ "yylo-docs-2", "yylo-gh-4", "yylo-gh-5", "yylo-gh-6" ] }, { "productId": "yylo", "storyId": "approve-task-contract", "verdict": "partial", "quality": 4, "confidence": "low", "rationale": "YYLO documents a task/contract concept (YAML contracts, ledger tasks) and isolates agent work in dedicated branches/worktrees with a read-only preflight before any commit is queued (yylo-docs-2, yylo-docs-4, yylo-gh-4), which implies some spec-first gating. However there is no documented human approval/sign-off step where an engineering-lead explicitly reviews and approves scope/contract before the agent is permitted to start modifying the repo—task start appears automatic once invoked. Missing for 10: explicit lead-approval gate/workflow, evidence of a review UI or command requiring human sign-off, and confirmation that agent modification is blocked pending that approval.", "evidenceIds": [ "yylo-docs-2", "yylo-docs-4", "yylo-gh-4", "yylo-gh-5" ] }, { "productId": "yylo", "storyId": "assign-task-from-ticket", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "YYLO integrations pull GitHub issues into its kanban ledger and return completed responses to the source thread, and ledger tasks can then be started with a subagent via task start or yylo init, supporting the flow of turning an issue into an agent task. Missing for 10: a single documented command that directly converts one specific issue into an agent task in one step, and hands-on confirmation the GitHub-issue import works end-to-end.", "evidenceIds": [ "yylo-docs-10", "yylo-docs-1", "yylo-docs-11", "yylo-gh-4" ] }, { "productId": "yylo", "storyId": "auto-approve-all-actions", "verdict": "none", "quality": 0, "confidence": "low", "rationale": "The evidence describes YYLO's orchestration architecture (task worktrees, ledger, merge protections) but nothing addresses a configurable auto-approve/no-confirmation mode for agent actions; in fact merge land explicitly requires checks and human-oversight-style gating rather than blanket auto-approval. Missing for 10: any documented auto-approve flag/setting, evidence of confirmation prompts being bypassable, or explicit human-oversight configuration options.", "evidenceIds": [ "yylo-gh-4", "yylo-gh-5", "yylo-gh-6" ] }, { "productId": "yylo", "storyId": "auto-fix-readiness-gaps", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "YYLO's diagnostic tool (`doctor workspace`) explicitly never fetches or changes the workspace, and merge/tests are described as explicit checks with no automated remediation; there is no documented feature that automatically fixes failing agent-readiness criteria.", "evidenceIds": [ "yylo-gh-1", "yylo-gh-6" ] }, { "productId": "yylo", "storyId": "auto-open-pr-on-completion", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "YYLO's docs describe task branches, worktrees, and an internal 'merge land' step that composes a candidate directly, explicitly stating merge 'launches no models, chooses no reviewers'—there is no mention anywhere of opening a GitHub pull request on task completion, only importing issues into kanban and returning responses to source threads. No CLI flag, workflow step, or integration doc references PR creation.", "evidenceIds": [ "yylo-gh-5", "yylo-gh-6", "yylo-docs-10", "yylo-docs-2" ] }, { "productId": "yylo", "storyId": "automated-pr-review", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "YYLO's own docs describe it as a CLI orchestrator for coding-agent tasks, workflows, and receipt-backed merges — not a PR-review tool. Evidence explicitly states 'Tests and semantic reviews are explicit project checks outside merge. Merge launches no models, chooses no reviewers' (yylo-gh-6), meaning there is no automated AI-generated inline PR review capability in the product.", "evidenceIds": [ "yylo-gh-6", "yylo-gh-5", "yylo-gh-3" ] }, { "productId": "yylo", "storyId": "automatic-environment-setup", "verdict": "partial", "quality": 7, "confidence": "medium", "rationale": "Docs and GitHub README describe `task start`/`yy init` hydrating a dedicated worktree from a protected base SHA and completing 'configured dependency hydration' before reporting WORKING, which covers automated environment setup and dependency install; the CLI is confirmed live on npm and the repo public. Missing for 10: explicit description of cloning an arbitrary remote repo (vs. hydrating a pre-defined workspace), and independent/hands-on confirmation that dependency install works end-to-end.", "evidenceIds": [ "yylo-gh-4", "yylo-docs-1", "yylo-docs-2", "yylo-probe-rt-1", "yylo-gh-2" ] }, { "productId": "yylo", "storyId": "automation-bulk-operations", "verdict": "full", "quality": 8, "confidence": "medium", "rationale": "YYLO's parallel-runner explicitly supports 'bounded concurrent fan-out for independent kanban tasks, data items, or complete commands—with structured evidence for every item,' and run-until-completion repeats iterations until all kanban work is done, directly enabling bulk operations across many items with automated evidence capture. This is corroborated by a live public CLI (npm registry, MIT-licensed source), though there's no independent hands-on report of large-scale fan-out in practice. Missing for 10: independent/third-party validation of bulk-scale runs and concrete concurrency limits or throughput numbers.", "evidenceIds": [ "yylo-docs-6", "yylo-docs-9", "yylo-docs-7", "yylo-probe-rt-1", "yylo-probe-rt-2" ] }, { "productId": "yylo", "storyId": "automation-rules-engine", "verdict": "partial", "quality": 5, "confidence": "low", "rationale": "YYLO's integrations pull Slack messages and GitHub issues into its kanban system and return responses to source threads, which is a form of event-triggered automation, and run-until-completion/workflow-runner allow chained/looping actions. However there's no evidence of a general user-defined rule engine (conditions + custom triggers) — the event handling is limited to specific hardcoded integrations rather than an open rule-definition system. Missing for 10: a documented rules/conditions DSL, support for arbitrary custom event sources/triggers, and evidence of user-authored trigger logic beyond the built-in Slack/GitHub integrations.", "evidenceIds": [ "yylo-docs-10", "yylo-docs-9", "yylo-docs-8" ] }, { "productId": "yylo", "storyId": "automation-scheduled-jobs", "verdict": "partial", "quality": 5, "confidence": "medium", "rationale": "YYLO supports repeatable workflows (YAML-saved contracts, `yy loop` shell repetition, run-until-completion looping, workflow-runner chaining) which cover automation-depth, but there is no evidence of a scheduler, cron-like trigger, or time-based recurring job execution — workflows appear to be manually invoked or run-to-completion rather than scheduled on a recurring basis. missing for 10: cron/schedule syntax or trigger mechanism, evidence of unattended recurring execution (e.g., daily/hourly runs), and independent confirmation of scheduling in production use.", "evidenceIds": [ "yylo-docs-3", "yylo-docs-4", "yylo-docs-8", "yylo-docs-9", "yylo-gh-3" ] }, { "productId": "yylo", "storyId": "automation-versioned-workflows", "verdict": "partial", "quality": 5, "confidence": "medium", "rationale": "YYLO ledger provides task versioning-like constructs (immutable task source, worktree branching, candidate composition, expected-old ref protection for merges) and workflow YAML contracts can be saved/reused, giving some review/rollback semantics via git-based merge protections. However there is no explicit 'automation version history', diff/review UI, or a documented rollback command for automations themselves beyond git branch/worktree mechanics. missing for 10: dedicated automation versioning/history view, explicit rollback command for a run/automation (not just git ref protection), review workflow UI, and independent/hands-on confirmation of these rollback flows.", "evidenceIds": [ "yylo-gh-4", "yylo-gh-5", "yylo-docs-4", "yylo-docs-12" ] }, { "productId": "yylo", "storyId": "autonomous-bug-fixing", "verdict": "partial", "quality": 5, "confidence": "medium", "rationale": "YYLO orchestrates coding agents through task worktrees, kanban-driven work items, and iteration loops (run-until-completion, subagent tasking) that could be pointed at a bug-fix task, and it supports ingesting GitHub issues into kanban as a trigger. However there is no evidence of autonomous bug diagnosis (root-causing, log/trace analysis) as a distinct capability — the docs describe generic task/workflow orchestration and merge/validation boundaries rather than an end-to-end 'diagnose then fix' agent behavior. missing for 10: explicit diagnosis/root-cause capability, an end-to-end bug-fix case study or hands-on validation, evidence the agent itself (vs. the orchestrator) performs debugging reasoning.", "evidenceIds": [ "yylo-docs-1", "yylo-docs-9", "yylo-docs-10", "yylo-gh-3", "yylo-gh-4" ] }, { "productId": "yylo", "storyId": "bring-your-own-model", "verdict": "none", "quality": 0, "confidence": "low", "rationale": "Evidence shows a `--subagent claude` CLI flag implying some agent selection, but there is no documentation of configuring API keys, choosing alternate LLM providers, or any pricing/billing control for engineering leads. Missing for 10: explicit BYO-API-key setup, multi-provider/model configuration docs, and any pricing-limits guidance.", "evidenceIds": [ "yylo-docs-1" ] }, { "productId": "yylo", "storyId": "chat-with-agent-in-slack", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "YYLO is a CLI orchestrator for coding agents and repo workflows, not a chat/messaging interface where users tag agents in threads; its integrations (yylo-docs-10) pull Slack/GitHub items into a kanban board rather than supporting in-thread @-mention delegation. This is a category mismatch, not a missing feature.", "evidenceIds": [] }, { "productId": "yylo", "storyId": "ci-failure-auto-fix", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "YYLO is a CLI orchestrator for coding agents/workflows with kanban, ledger, and merge tooling, but nothing in the evidence pack mentions CI workflow failure detection, diagnosis, or auto-generating a fix PR from a failing CI run. Merge/land explicitly excludes running tests or validation ('Tests and semantic reviews are explicit project checks outside merge'), which is the opposite of an integrated CI-fix loop.", "evidenceIds": [ "yylo-gh-6", "yylo-gh-5", "yylo-gh-3" ] }, { "productId": "yylo", "storyId": "codebase-context-file", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "The evidence pack covers task orchestration, kanban ledgers, merge protections, and workflow runners, but nowhere describes a context/conventions file that agents read to generate more relevant plans or code. No mention of AGENTS.md, project instructions, or codebase-convention configuration exists in the docs or GitHub items provided.", "evidenceIds": [] }, { "productId": "yylo", "storyId": "concurrent-task-execution", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "YYLO documents a 'bounded concurrent fan-out' parallel-runner for independent kanban tasks with structured evidence per item, plus run-until-completion looping, which supports running multiple agent tasks concurrently. However, evidence lacks details on concurrency limits, scaling numbers, resource isolation at scale, or independent/hands-on validation of throughput gains. missing for 10: concrete concurrency limits/benchmarks, independent third-party validation of parallel scaling, evidence of large-scale (10s-100s of tasks) usage in production.", "evidenceIds": [ "yylo-docs-6", "yylo-docs-9", "yylo-docs-7", "yylo-gh-3" ] }, { "productId": "yylo", "storyId": "connect-github-repo", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "Evidence shows YYLO operates on local git worktrees/branches and has an internal 'merge land' step, and can pull GitHub issues into its kanban, but there is no evidence of connecting a GitHub repository as a remote and having the agent open pull requests against it — the merge feature explicitly stays local/internal with no GitHub PR API integration mentioned.", "evidenceIds": [ "yylo-gh-4", "yylo-gh-5", "yylo-gh-6", "yylo-docs-10" ] }, { "productId": "yylo", "storyId": "daily-task-quota-limits", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "YYLO is a self-hosted, open-source CLI orchestrator with no evidence of a hosted plan/pricing model or subscription tiers; concepts like 'plan-based daily task and concurrency limits' apply to SaaS pricing tiers, not to a locally-run open-source tool where users control their own concurrency via config (e.g., parallel-runner). This story's axis (plan/subscription-based usage limits) does not fit this product's category.", "evidenceIds": [ "yylo-gh-3", "yylo-docs-6", "yylo-probe-rt-2" ] }, { "productId": "yylo", "storyId": "delegate-sessions-for-teammates", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "YYLO is a single-user CLI orchestrator for coding agents run locally; it has no organization/user-management, multi-tenant, or delegated-access model referenced anywhere in the evidence. Creating sessions 'on behalf of other users in an org' is a category mismatch for a local CLI tool rather than a missing feature.", "evidenceIds": [ "yylo-gh-3", "yylo-docs-1" ] }, { "productId": "yylo", "storyId": "describe-feature-in-plain-language", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "YYLO's CLI lets a developer pass a plain-language task string (e.g. `yylo init --task \"Describe one verifiable outcome\"` or `yy ledger create \"Validate recovery\"`) which is then hydrated into a dedicated branch/worktree and handed to a subagent (yylo-docs-1, yylo-docs-2, yylo-gh-4, yylo-docs-11). This covers the 'turned into a scoped implementation task' half of the story, but there's no evidence of NLP-based scoping/decomposition logic — the description appears passed through largely as-is rather than analyzed/refined into a structured spec. Missing for 10: evidence of automatic task decomposition or requirement extraction from free-text input, and independent/hands-on confirmation that vague feature/bug descriptions produce well-scoped tasks.", "evidenceIds": [ "yylo-docs-1", "yylo-docs-2", "yylo-gh-4", "yylo-docs-11" ] }, { "productId": "yylo", "storyId": "design-to-code-handoff", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "YYLO's evidence describes a CLI orchestrator for coding agents (task/worktree/merge/ledger workflows) aimed at developers and project operators, with no mention of ingesting mockups/designs or enabling a non-technical PM to go from a design to working code without engineering involvement.", "evidenceIds": [ "yylo-gh-3", "yylo-docs-1", "yylo-gh-4", "yylo-gh-5" ] }, { "productId": "yylo", "storyId": "diff-review-before-pr", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "Docs describe worktrees, candidate branches, and a 'merge land' step, but nowhere is there evidence of a diff-review UI or an explicit developer approval gate before a pull request is opened; in fact merge is described as launching 'no models, no reviewers' and reviews are called 'explicit project checks outside merge', with no PR-creation flow documented at all.", "evidenceIds": [ "yylo-gh-4", "yylo-gh-5", "yylo-gh-6", "yylo-docs-2" ] }, { "productId": "yylo", "storyId": "end-to-end-feature-implementation", "verdict": "partial", "quality": 5, "confidence": "medium", "rationale": "YYLO documents an end-to-end task loop (yylo init --task ... --subagent claude, worktree creation, commit-and-queue candidate flow) showing an agent can implement a described feature autonomously, but the evidence explicitly states 'tests and semantic reviews are explicit project checks outside merge' — meaning YYLO's own agent loop does not itself guarantee test-writing as part of implementation, only that separate check scripts exist for validation. missing for 10: explicit evidence the invoked subagent writes/adds tests as part of a task, and any example showing test-authoring within the init/finish workflow.", "evidenceIds": [ "yylo-docs-1", "yylo-docs-2", "yylo-gh-3", "yylo-gh-4", "yylo-gh-6" ] }, { "productId": "yylo", "storyId": "enterprise-sso-licensing", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "YYLO's evidence pack shows only an open-source CLI orchestrator (MIT-licensed, npm package) with no mention of enterprise licensing tiers, SSO integration, or commercial support offerings; there is no pricing/plans page or enterprise sales material in evidence. missing for 10: enterprise/SSO licensing tier, commercial support plans, organization-wide deployment documentation.", "evidenceIds": [ "yylo-gh-3", "yylo-probe-rt-1", "yylo-probe-rt-2" ] }, { "productId": "yylo", "storyId": "feedback-to-task-conversion", "verdict": "partial", "quality": 4, "confidence": "low", "rationale": "YYLO can ingest external feedback (Slack messages, GitHub issues) directly into its kanban/ledger as structured tasks (yylo-docs-10), and a dedicated `feedback-yylo` CLI binary is confirmed live on npm (yylo-probe-rt-1), suggesting a feedback-to-task pathway exists. However, there is no documented mechanism for generating a 'proposed scope' alongside the task — no scope estimation, sizing, or planning artifact is described anywhere in the docs or GitHub evidence. Missing for 10: explicit scope-proposal output tied to feedback conversion, documentation of what the feedback-yylo binary actually produces, and any PM-facing evidence of structured scoping fields.", "evidenceIds": [ "yylo-docs-10", "yylo-probe-rt-1", "yylo-docs-11" ] }, { "productId": "yylo", "storyId": "headless-ci-execution", "verdict": "partial", "quality": 6, "confidence": "medium", "rationale": "YYLO is a CLI-first orchestrator with commands (init, task start, ledger, loop, workflow-runner, parallel-runner) that are inherently scriptable and non-interactive, and its runtime bins (yylo, yy, ypl) are published on npm confirming CLI availability. However, there's no explicit CI/CD documentation, no exit-code/error-handling guarantance for pipeline use beyond doctor workspace, and no CI examples (GitHub Actions, GitLab CI, etc.) or headless/no-TTY confirmation. missing for 10: explicit CI/CD pipeline examples or docs, confirmed non-interactive/headless mode guarantees, exit-code contract documentation for scripting, independent hands-on CI usage reports.", "evidenceIds": [ "yylo-docs-1", "yylo-docs-3", "yylo-docs-6", "yylo-gh-1", "yylo-gh-3", "yylo-probe-rt-1" ] }, { "productId": "yylo", "storyId": "isolated-sandbox-execution", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "YYLO's docs describe git-worktree/branch isolation for tasks and merge safety, but none of the evidence mentions a sandboxed execution environment (container/VM) for running agent code or installing dependencies safely. Worktree isolation protects git state, not runtime/process isolation.", "evidenceIds": [ "yylo-gh-4", "yylo-docs-2", "yylo-gh-3" ] }, { "productId": "yylo", "storyId": "issue-tracker-sync", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "The integrations doc only mentions bringing Slack messages or GitHub issues into kanban (yylo-docs-10); there is no mention of Jira, Linear, ClickUp, or Monday.com anywhere in the evidence pack. Missing for 10: any documented connector or API integration for Jira, Linear, ClickUp, or Monday.com.", "evidenceIds": [ "yylo-docs-10" ] }, { "productId": "yylo", "storyId": "live-agent-activity-monitoring", "verdict": "partial", "quality": 4, "confidence": "low", "rationale": "The GitHub docs mention a successful run ending in a 'watch receipt' with a state field (e.g. COMPLETED, exit_code, log_bytes), implying some status-tracking mechanism exists, and 'task start' reports a WORKING state before completion. However there is no dedicated 'watch' command, live dashboard, or streaming log evidence showing real-time observation of an in-progress agent's actions. missing for 10: explicit real-time streaming/monitoring command or UI, documentation of live status polling during execution, independent confirmation of watch behavior.", "evidenceIds": [ "yylo-gh-2", "yylo-gh-4", "yylo-docs-7" ] }, { "productId": "yylo", "storyId": "managed-cloud-execution", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "YYLO is a local CLI orchestrator that runs on a developer's own machine/repo (npm-installed, git worktrees, local kanban/ledger) rather than a managed cloud service; there is no evidence of a hosted runtime, cloud dashboard, or backend YYLO operates on the user's behalf. This story asks about offloading backend infra to a vendor-run cloud, which is a different product category than a CLI tool.", "evidenceIds": [] }, { "productId": "yylo", "storyId": "manual-model-override", "verdict": "partial", "quality": 5, "confidence": "low", "rationale": "The CLI init command shows a --subagent flag (e.g. 'claude') letting a user specify which model/agent to use instead of relying on defaults, implying manual model selection is possible; however there is no documentation of an explicit 'automatic model selection' mode being overridden, no list of supported models, and no engineering-lead-oriented control/settings UI shown. missing for 10: explicit documentation of an automatic/default model-selection mode, a full list of selectable models, and confirmation that this override is persistent/configurable at a project or team level.", "evidenceIds": [ "yylo-docs-1" ] }, { "productId": "yylo", "storyId": "mid-task-takeover", "verdict": "partial", "quality": 4, "confidence": "medium", "rationale": "YYLO offers terminal-based session continuation (`yy continue SESSION_ID`) that lets a developer pick back up captured agent work rather than reconstructing it from scrollback, and task start/finish flows expose worktree/branch state that could be inspected or redirected from the CLI. However, there is no evidence of any editor integration or browser UI for taking over tasks — YYLO is documented purely as a CLI/orchestrator tool. Missing for 10: editor plugin/extension support, browser-based task takeover UI, and explicit interactive 'redirect mid-task' semantics beyond resuming a session.", "evidenceIds": [ "yylo-docs-7", "yylo-gh-4", "yylo-gh-3", "yylo-docs-2" ] }, { "productId": "yylo", "storyId": "mobile-approval-of-decisions", "verdict": "none", "quality": 0, "confidence": "high", "rationale": "YYLO is documented purely as a CLI/terminal orchestrator (yylo/yy commands, ledger, worktrees, merge gating) with no mention of any mobile app, phone notification, or remote-approval interface for product managers. The axis is plausible for an agent-orchestration tool but no evidence supports it.", "evidenceIds": [ "yylo-gh-3", "yylo-docs-1", "yylo-docs-11", "yylo-gh-4", "yylo-gh-5" ] }, { "productId": "yylo", "storyId": "one-click-repo-access", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "YYLO is documented as a CLI orchestrator requiring `npm install -g @yylo/cli` plus explicit `yylo init`/`task start` workflows that freeze SHAs, create worktrees, and hydrate dependencies — this is CLI-based setup, not a one-click repo-access grant. No evidence describes a GitHub App-style one-click install or OAuth flow for repo access. Missing for 10: any one-click install/authorization mechanism, evidence of simplified non-CLI onboarding, or a hosted install button.", "evidenceIds": [ "yylo-docs-1", "yylo-gh-4", "yylo-probe-rt-1" ] }, { "productId": "yylo", "storyId": "openness-api-parity", "verdict": "na", "quality": 0, "confidence": "medium", "rationale": "YYLO is a CLI/command-line orchestrator (yy/yylo commands) with no evidence of any graphical UI to compare against; the story presumes a UI+API product with parity concerns, which doesn't fit a CLI-first tool where the CLI itself is the sole interface.", "evidenceIds": [ "yylo-gh-3", "yylo-docs-1", "yylo-probe-rt-1" ] }, { "productId": "yylo", "storyId": "openness-full-export", "verdict": "partial", "quality": 4, "confidence": "low", "rationale": "Workflow contracts can be saved as YAML (yylo-docs-4) and the CLI/ledger source is fully open-source under MIT (yylo-probe-rt-2), suggesting no vendor lock-in, but there is no documented single 'export all data' command covering ledger records, task history, sessions, and artifacts in open formats. Missing for 10: an explicit data-export command/feature, documentation of export formats for ledger/kanban/session data, and confirmation that all state (not just workflow YAML) is portable.", "evidenceIds": [ "yylo-docs-4", "yylo-docs-12", "yylo-probe-rt-2", "yylo-gh-3" ] }, { "productId": "yylo", "storyId": "openness-open-license", "verdict": "full", "quality": 8, "confidence": "high", "rationale": "The GitHub repo is public and licensed under MIT, confirmed by a runtime probe reading the LICENSE file directly, and the repo (yylo-dev/yylo) is documented as the CLI orchestrator's source. missing for 10: no independent third-party audit or community commentary confirming completeness of the published source beyond the license file check.", "evidenceIds": [ "yylo-probe-rt-2", "yylo-gh-3" ] }, { "productId": "yylo", "storyId": "openness-self-host", "verdict": "full", "quality": 8, "confidence": "medium", "rationale": "YYLO is an open-source, MIT-licensed CLI orchestrator (installable via npm, source on GitHub) that runs locally against a user's own repo/agents, so self-hosting the core product is inherently satisfied — confirmed by the public npm registry listing and the MIT LICENSE in the public repo. missing for 10: no dedicated self-hosting/deployment guide (e.g. server install, Docker, or persistent-service setup instructions) and no independent third-party report of someone self-hosting it in production.", "evidenceIds": [ "yylo-probe-rt-1", "yylo-probe-rt-2", "yylo-gh-3", "yylo-docs-1" ] }, { "productId": "yylo", "storyId": "privacy-data-residency", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "YYLO is a CLI orchestrator that runs locally on the user's own machine/repo, coordinating coding agents and git workflows—it does not store user data in a hosted service where region/residency would be a choice. Data residency is a category error for a local CLI tool rather than an unmet capability.", "evidenceIds": [ "yylo-gh-3", "yylo-docs-1" ] }, { "productId": "yylo", "storyId": "privacy-no-training", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "YYLO is a CLI orchestrator for coding agents and repo workflows, not a data-processing/AI training service; the axis of preventing data from being used for AI model training does not apply to this kind of product, and nothing in the evidence pack even implies YYLO handles user data for model training purposes.", "evidenceIds": [] }, { "productId": "yylo", "storyId": "privacy-retention-controls", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "YYLO's evidence covers task orchestration, worktrees, ledgers, and merges, but nothing addresses data retention policies, storage duration, or user-controlled deletion of tasks/sessions/records. No docs mention exporting, purging, or deleting stored data or session logs.", "evidenceIds": [] }, { "productId": "yylo", "storyId": "privacy-telemetry-optout", "verdict": "none", "quality": 0, "confidence": "low", "rationale": "No evidence pack item mentions telemetry, usage tracking, analytics collection, or an opt-out mechanism for YYLO; the docs and GitHub description focus entirely on task orchestration features.", "evidenceIds": [] }, { "productId": "yylo", "storyId": "query-repo-documentation", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "YYLO is a CLI orchestrator for coding agents and task/workflow management, not a documentation-generation or docs-querying product; the evidence covers task ledgers, merge, workflows, and CI-like receipts, with nothing about generating or querying repository documentation.", "evidenceIds": [] }, { "productId": "yylo", "storyId": "repo-readiness-report", "verdict": "partial", "quality": 4, "confidence": "low", "rationale": "YYLO ships a `doctor workspace` check that flags actionable topology problems without mutating the repo, and its GitHub description references 'release-readiness boundaries,' which gesture at repo-readiness diagnostics, but there is no documented dedicated report scoring or evaluating overall repository readiness for autonomous agents. missing for 10: a named readiness-report command/output, criteria for 'agent readiness' beyond topology checks, and any sample report artifact.", "evidenceIds": [ "yylo-gh-1", "yylo-gh-3" ] }, { "productId": "yylo", "storyId": "security-alert-remediation", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "YYLO is a CLI orchestrator for coding-agent task workflows, kanban tasks, and merge/candidate management, not a security-scanning or SCA/dependency-alert tool; there is no concept of security alerts to validate. This is a category mismatch rather than an unmet capability.", "evidenceIds": [ "yylo-gh-3", "yylo-gh-6" ] }, { "productId": "yylo", "storyId": "self-hosted-agent-deployment", "verdict": "partial", "quality": 5, "confidence": "medium", "rationale": "YYLO is an open-source, MIT-licensed CLI (npm package + public GitHub repo) that runs locally as an orchestrator, implying it can be run on a developer's own machine, in containers, or VMs since it's just a Node CLI operating on a local git worktree. However, there is no explicit documentation of container/VM deployment, Docker images, self-hosting guides, or infrastructure requirements for running at scale. missing for 10: explicit self-hosting/deployment docs (Docker/container images, VM setup guides), infrastructure/scaling guidance, and confirmation of statelessness or multi-instance operation for parallel agent infrastructure.", "evidenceIds": [ "yylo-gh-3", "yylo-probe-rt-1", "yylo-probe-rt-2", "yylo-gh-2" ] }, { "productId": "yylo", "storyId": "steer-active-session", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "YYLO's docs describe task lifecycle (init/start/preflight/finish) and resuming a captured session via `yy continue SESSION_ID`, but there is no evidence of sending new instructions to an already-running agent session to redirect its work mid-flight; `continue` appears to resume/reattach rather than inject steering input. missing for 10: any documented mechanism for mid-session instruction injection or steering, evidence that an active agent process accepts new prompts without restart.", "evidenceIds": [ "yylo-docs-7", "yylo-docs-2", "yylo-gh-4" ] }, { "productId": "yylo", "storyId": "task-completion-notifications", "verdict": "partial", "quality": 5, "confidence": "medium", "rationale": "YYLO emits watch receipts with a COMPLETED state/exit_code after a run finishes, and its Slack/GitHub integrations can return completed responses to the originating thread, giving developers a way to learn a task finished. However there's no evidence of a proactive notification for when an agent 'needs input' mid-task, nor any push/alert channel beyond polling receipts or the source-thread reply. Missing for 10: explicit 'needs input' alerting, a dedicated notification/webhook system, and independent confirmation the integration loop works end-to-end.", "evidenceIds": [ "yylo-gh-2", "yylo-docs-10", "yylo-gh-6" ] }, { "productId": "yylo", "storyId": "tiered-autonomy-levels", "verdict": "none", "quality": 0, "confidence": "low", "rationale": "Evidence shows preflight/read-only checks, merge protections, and validation boundaries, but there is no mention of configurable tiered autonomy levels or a settings mechanism letting an engineering-lead define graduated confirmation thresholds for agent actions. missing for 10: explicit autonomy-level configuration, tiered permission settings, evidence of user-controlled confirmation thresholds.", "evidenceIds": [] }, { "productId": "yylo", "storyId": "trigger-agent-on-build-failure", "verdict": "none", "quality": 0, "confidence": "medium", "rationale": "YYLO is documented as a CLI orchestrator with kanban tasks, workflow/parallel runners, and Slack/GitHub-issue integrations, but nothing in the evidence describes triggering it from a CI/CD pipeline or having it react to a failing build/test. Since it's a scriptable CLI, this axis plausibly applies, but there is no documented CI hook, GitHub Actions example, or build-failure-triggered workflow.", "evidenceIds": [ "yylo-gh-3", "yylo-docs-9", "yylo-docs-10" ] }, { "productId": "yylo", "storyId": "visual-context-for-tasks", "verdict": "na", "quality": 0, "confidence": "high", "rationale": "YYLO is a CLI orchestrator for text-based task/workflow management around coding agents; there is no evidence of image/screenshot attachment, mockup annotation, or any visual-input handling in its task creation or ledger features. Attaching marked-up visual mockups to drive implementation is a UI/multimodal-input capability outside this CLI orchestration product's category.", "evidenceIds": [] } ]