Codegen vs YYLO
Codegen
Codegen, Inc.
Codegen wins · 30–12 (19 drawn)
Agenticness — how well agents can access and operate the productAgenticness
How well agents can access and operate the product
Agent access
ai-native userPoint an agent at llms.txt or agent-oriented docs
weight 2 · round to CodegenA 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.
- [probe] “PROBE llms.txt: HTTP 200 at https://docs.codegen.com/llms.txt # Codegen ## Docs - [Codegen](https://docs.codegen.com/introduction/overview…”
- [claimed-docs] “Analyze requirements, implement features, fix bugs, write tests, and improve documentation based on your prompts.”
- [claimed-docs] “Programmatically interact with agents using the Python SDK for advanced automation.”
YYLOnone0/10Direct 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.
ai-native userRun the product headlessly / in CI for automation
weight 2 · round to CodegenCodegen 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.
- [claimed-docs] “Programmatically interact with agents using the Python SDK for advanced automation.”
- [claimed-docs] “agent = Agent(org_id="...", token="...") # Run an agent with a prompt task = agent.run(prompt="Leave a review on PR #123")”
- [claimed-docs] “Create and manage AI agents that can write code, fix bugs, and handle development tasks across your repositories with full programmatic cont…”
- [claimed-docs] “programmatically retrieve detailed agent traces for analysis and debugging”
- [claimed-docs] “Trigger agents from CI/CD pipelines when builds fail”
- [claimed-docs] “Use it to view agents, pull their work, create new agents, and run Claude Code with full telemetry and monitoring.”
- [probe] “PROBE openapi: all candidate paths 404 (https://docs.codegen.com/openapi.json, https://docs.codegen.com/swagger.json, https://docs.codegen.c…”
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.
- [claimed-docs] “npm install -g @yylo/cli@0.2.2 yylo init --task "Describe one verifiable outcome" --subagent claude”
- [claimed-docs] “`yy loop` repeats arbitrary shell commands sequentially.”
- [claimed-docs] “Bounded concurrent fan-out for independent kanban tasks, data items, or complete commands—with structured evidence for every item.”
- [claimed-docs] “Repeat bounded YYLO iterations until no open kanban work remains.”
- [github] “A successful run prints the installed YYLO version, initializes `.juno_task/`, then emits a watch receipt with `"state":"COMPLETED"`, `"exit…”
- [github] “YYLO is a command-line orchestrator for coding agents, repeatable workflows, and receipt-backed repository changes. It is for developers who…”
- [probe] “PROBE runtime (recorded 2026-09-14): `npm view @yylo/cli name version bin` — the official CLI is live on the public npm registry (@yylo/cli …”
ai-native userPlug MCP servers into this product so it can use their tools
weight 3 · round to CodegenCodegen'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.
- [claimed-docs] “Connect with Slack, Linear, Figma, databases, and extend capabilities with custom MCP tools.”
- [claimed-docs] “Log local Claude Code instances to the cloud and provision MCP servers across your org.”
ai-native userUse an official CLI
weight 2 · round drawnCodegen 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.
- [claimed-docs] “Use it to view agents, pull their work, create new agents, and run Claude Code with full telemetry and monitoring.”
- [claimed-docs] “Pull agent work - Download branches and code changes created by agents directly to your local environment”
- [claimed-docs] “Manage organizations - Switch between organizations and configure repositories”
- [probe] “official CLI documented at https://docs.codegen.com/introduction/cli”
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.
- [claimed-docs] “npm install -g @yylo/cli@0.2.2 yylo init --task "Describe one verifiable outcome" --subagent claude”
- [claimed-docs] “Start hydrates an exact-base task worktree. Preflight is read-only, finish queues the committed candidate”
- [claimed-docs] “yy ledger create "Validate recovery" --status todo --tags auth yy ledger ready --sort asc yy ledger get TASK_ID”
- [github] “YYLO is a command-line orchestrator for coding agents, repeatable workflows, and receipt-backed repository changes. It is for developers who…”
- [github] “`task start` freezes the protected target SHA, creates a dedicated branch/worktree, and completes configured dependency hydration before rep…”
- [probe] “PROBE runtime (recorded 2026-09-14): `npm view @yylo/cli name version bin` — the official CLI is live on the public npm registry (@yylo/cli …”
- [probe] “PROBE runtime (recorded 2026-09-14): `curl -s https://raw.githubusercontent.com/yylo-dev/yylo/HEAD/LICENSE | head -3` returns 'MIT License /…”
ai-native userDrive the product through a documented public API
weight 3 · round to CodegenCodegen 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.
- [claimed-docs] “Programmatically interact with agents using the Python SDK for advanced automation.”
- [claimed-docs] “Use it to view agents, pull their work, create new agents, and run Claude Code with full telemetry and monitoring.”
- [claimed-docs] “agent = Agent(org_id="...", token="...") # Run an agent with a prompt task = agent.run(prompt="Leave a review on PR #123")”
- [claimed-docs] “Create and manage AI agents that can write code, fix bugs, and handle development tasks across your repositories with full programmatic cont…”
- [claimed-docs] “programmatically retrieve detailed agent traces for analysis and debugging”
- [claimed-docs] “Trigger agents from CI/CD pipelines when builds fail”
- [probe] “PROBE openapi: all candidate paths 404 (https://docs.codegen.com/openapi.json, https://docs.codegen.com/swagger.json, https://docs.codegen.c…”
- [probe] “official CLI documented at https://docs.codegen.com/introduction/cli”
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.
- [claimed-docs] “npm install -g @yylo/cli@0.2.2 yylo init --task "Describe one verifiable outcome" --subagent claude”
- [claimed-docs] “`yy loop` repeats arbitrary shell commands sequentially.”
- [claimed-docs] “Save the same contract as YAML for a reusable workflow.”
- [claimed-docs] “yy ledger create "Validate recovery" --status todo --tags auth yy ledger ready --sort asc yy ledger get TASK_ID”
- [probe] “PROBE llms.txt: HTTP 404 at https://yylo.dev/llms.txt”
- [probe] “PROBE docs-md: HTTP 404 at https://yylo.dev/docs/yylo.md”
- [probe] “PROBE openapi: all candidate paths 404 (https://yylo.dev/openapi.json, https://yylo.dev/swagger.json, https://yylo.dev/api/openapi.json, htt…”
- [probe] “PROBE runtime (recorded 2026-09-14): `npm view @yylo/cli name version bin` — the official CLI is live on the public npm registry (@yylo/cli …”
ai-native userBuild against official SDKs
weight 2 · round to CodegenCodegen 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).
- [claimed-docs] “Programmatically interact with agents using the Python SDK for advanced automation.”
- [claimed-docs] “agent = Agent(org_id="...", token="...") # Run an agent with a prompt task = agent.run(prompt="Leave a review on PR #123")”
- [claimed-docs] “Create and manage AI agents that can write code, fix bugs, and handle development tasks across your repositories with full programmatic cont…”
- [claimed-docs] “programmatically retrieve detailed agent traces for analysis and debugging”
- [claimed-docs] “Trigger agents from CI/CD pipelines when builds fail”
- [probe] “PROBE openapi: all candidate paths 404 (https://docs.codegen.com/openapi.json, https://docs.codegen.com/swagger.json, https://docs.codegen.c…”
- [probe] “official CLI documented at https://docs.codegen.com/introduction/cli”
YYLOnone0/10YYLO 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.
- [probe] “PROBE llms.txt: HTTP 404 at https://yylo.dev/llms.txt”
- [probe] “PROBE docs-md: HTTP 404 at https://yylo.dev/docs/yylo.md”
- [probe] “PROBE openapi: all candidate paths 404 (https://yylo.dev/openapi.json, https://yylo.dev/swagger.json, https://yylo.dev/api/openapi.json, htt…”
- [probe] “PROBE runtime (recorded 2026-09-14): `npm view @yylo/cli name version bin` — the official CLI is live on the public npm registry (@yylo/cli …”
- [probe] “PROBE runtime (recorded 2026-09-14): `curl -s https://raw.githubusercontent.com/yylo-dev/yylo/HEAD/LICENSE | head -3` returns 'MIT License /…”
ai-native userSubscribe to events via webhooks
weight 2 · round drawnCodegennone0/10No 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.
YYLOnone0/10No 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.
- [claimed-docs] “Bring Slack messages or GitHub issues into kanban and return completed responses to their source thread.”
- [probe] “PROBE openapi: all candidate paths 404 (https://yylo.dev/openapi.json, https://yylo.dev/swagger.json, https://yylo.dev/api/openapi.json, htt…”
Agentic features
ai-native userGet AI-generated insights and suggestions from my data inside the product
weight 2 · round to CodegenCodegen 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.
- [claimed-docs] “Reads the PR diff Summarizes changes across files Runs security and secrets scan Checks test coverage Posts a review comment on failure”
- [claimed-docs] “Review PRs, suggest changes, comment on issues, create branches, commit code, and manage repositories.”
- [claimed-docs] “Connect with Slack, Linear, Figma, databases, and extend capabilities with custom MCP tools.”
YYLOnone0/10YYLO 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.
- [github] “Tests and semantic reviews are explicit project checks outside merge. Merge launches no models, chooses no reviewers, schedules no suites, a…”
- [claimed-docs] “npm install -g @yylo/cli@0.2.2 yylo init --task "Describe one verifiable outcome" --subagent claude”
- [claimed-docs] “yy ledger create "Validate recovery" --status todo --tags auth yy ledger ready --sort asc yy ledger get TASK_ID”
ai-native userSet up automations that run autonomously in the background
weight 2 · round to CodegenCodegen 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.
- [claimed-docs] “Trigger agents from CI/CD pipelines when builds fail”
- [claimed-docs] “Reads the PR diff Summarizes changes across files Runs security and secrets scan Checks test coverage Posts a review comment on failure”
- [claimed-docs] “Send notifications, ask for clarification, report progress, and interact directly with your team in Slack channels.”
- [claimed-docs] “Update statuses, add comments, link PRs to issues, and create new tasks. Support for Jira, Linear, Clickup and Monday.com.”
- [claimed-docs] “Programmatically interact with agents using the Python SDK for advanced automation.”
- [claimed-docs] “agent = Agent(org_id="...", token="...") # Run an agent with a prompt task = agent.run(prompt="Leave a review on PR #123")”
- [claimed-docs] “Create and manage AI agents that can write code, fix bugs, and handle development tasks across your repositories with full programmatic cont…”
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.
- [claimed-docs] “`yy loop` repeats arbitrary shell commands sequentially.”
- [claimed-docs] “Bounded concurrent fan-out for independent kanban tasks, data items, or complete commands—with structured evidence for every item.”
- [claimed-docs] “Continue a captured session with `yy continue SESSION_ID`; do not reconstruct work from tmux scrollback.”
- [claimed-docs] “Choose Workflow Runner when a step consumes `{{ steps.<id>.response }}`, a generated file, or a session from an earlier step.”
- [claimed-docs] “Repeat bounded YYLO iterations until no open kanban work remains.”
- [claimed-docs] “Bring Slack messages or GitHub issues into kanban and return completed responses to their source thread.”
- [github] “YYLO is a command-line orchestrator for coding agents, repeatable workflows, and receipt-backed repository changes. It is for developers who…”
ai-native userDelegate tasks to a built-in AI assistant inside the product
weight 3 · round to CodegenCodegen'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.
- [claimed-docs] “Analyze requirements, implement features, fix bugs, write tests, and improve documentation based on your prompts.”
- [claimed-docs] “Send notifications, ask for clarification, report progress, and interact directly with your team in Slack channels.”
- [claimed-docs] “Review PRs, suggest changes, comment on issues, create branches, commit code, and manage repositories.”
- [claimed-docs] “agent = Agent(org_id="...", token="...") # Run an agent with a prompt task = agent.run(prompt="Leave a review on PR #123")”
- [claimed-docs] “Create and manage AI agents that can write code, fix bugs, and handle development tasks across your repositories with full programmatic cont…”
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.
- [claimed-docs] “npm install -g @yylo/cli@0.2.2 yylo init --task "Describe one verifiable outcome" --subagent claude”
- [github] “YYLO is a command-line orchestrator for coding agents, repeatable workflows, and receipt-backed repository changes. It is for developers who…”
- [claimed-docs] “yy ledger create "Validate recovery" --status todo --tags auth yy ledger ready --sort asc yy ledger get TASK_ID”
ai-native userOperate the product with natural-language commands
weight 2 · round to CodegenCodegen 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.
- [claimed-docs] “Analyze requirements, implement features, fix bugs, write tests, and improve documentation based on your prompts.”
- [claimed-docs] “Send notifications, ask for clarification, report progress, and interact directly with your team in Slack channels.”
- [claimed-docs] “Update statuses, add comments, link PRs to issues, and create new tasks. Support for Jira, Linear, Clickup and Monday.com.”
- [claimed-docs] “Review PRs, suggest changes, comment on issues, create branches, commit code, and manage repositories.”
- [claimed-docs] “agent = Agent(org_id="...", token="...") # Run an agent with a prompt task = agent.run(prompt="Leave a review on PR #123")”
- [claimed-docs] “Create and manage AI agents that can write code, fix bugs, and handle development tasks across your repositories with full programmatic cont…”
YYLOnone0/10YYLO'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.).
- [claimed-docs] “npm install -g @yylo/cli@0.2.2 yylo init --task "Describe one verifiable outcome" --subagent claude”
- [claimed-docs] “Start hydrates an exact-base task worktree. Preflight is read-only, finish queues the committed candidate”
- [github] “YYLO is a command-line orchestrator for coding agents, repeatable workflows, and receipt-backed repository changes. It is for developers who…”
- [github] “`task start` freezes the protected target SHA, creates a dedicated branch/worktree, and completes configured dependency hydration before rep…”
Api quality
ai-native userExplore an interactive API reference with runnable examples
weight 2 · round drawnCodegennone0/10Codegen 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.
- [claimed-docs] “Create and manage AI agents that can write code, fix bugs, and handle development tasks across your repositories with full programmatic cont…”
- [claimed-docs] “programmatically retrieve detailed agent traces for analysis and debugging”
- [claimed-docs] “Trigger agents from CI/CD pipelines when builds fail”
- [probe] “PROBE openapi: all candidate paths 404 (https://docs.codegen.com/openapi.json, https://docs.codegen.com/swagger.json, https://docs.codegen.c…”
YYLOnone0/10YYLO 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.
ai-native userDownload a machine-readable API spec (OpenAPI or equivalent)
weight 2 · round drawnCodegennone0/10Codegen 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.
- [probe] “PROBE openapi: all candidate paths 404 (https://docs.codegen.com/openapi.json, https://docs.codegen.com/swagger.json, https://docs.codegen.c…”
- [claimed-docs] “Create and manage AI agents that can write code, fix bugs, and handle development tasks across your repositories with full programmatic cont…”
YYLOnone0/10Explicit 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.
ai-native userTest against a sandbox environment without touching production data
weight 1 · round to YYLOCodegen 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.
- [claimed-docs] “Safely run code, install dependencies, and test changes in robust isolated environments”
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.
- [claimed-docs] “Start hydrates an exact-base task worktree. Preflight is read-only, finish queues the committed candidate”
- [github] “`task start` freezes the protected target SHA, creates a dedicated branch/worktree, and completes configured dependency hydration before rep…”
- [github] “`doctor workspace` is intentionally nonzero when it finds an actionable topology problem; it never fetches or changes the workspace.”
- [github] “`merge land` selects one immutable task source, composes in a private detached candidate, and uses Git expected-old ref protection. A moved …”
- [claimed-docs] “Plan immutable experiments, execute isolated attempts, retain evaluator provenance, recover safely, and produce bounded reports.”
ai-native userRely on versioned APIs with a documented deprecation policy
weight 2 · round drawnCodegennone0/10There'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.
- [claimed-docs] “Create and manage AI agents that can write code, fix bugs, and handle development tasks across your repositories with full programmatic cont…”
- [probe] “PROBE openapi: all candidate paths 404 (https://docs.codegen.com/openapi.json, https://docs.codegen.com/swagger.json, https://docs.codegen.c…”
YYLOnone0/10No 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.
- [probe] “PROBE openapi: all candidate paths 404 (https://yylo.dev/openapi.json, https://yylo.dev/swagger.json, https://yylo.dev/api/openapi.json, htt…”
- [probe] “PROBE llms.txt: HTTP 404 at https://yylo.dev/llms.txt”
- [probe] “PROBE docs-md: HTTP 404 at https://yylo.dev/docs/yylo.md”
- [claimed-docs] “The 0.2.1rc6 channel adds ID-first general Records and typed task, wiki, workflow, and artifact profiles.”
Automation depth — how much of the product can run unattendedAutomation depth
How much of the product can run unattended
ai-native userPerform bulk operations across many items at once
weight 2 · round to YYLOThe 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.
- [claimed-docs] “Programmatically interact with agents using the Python SDK for advanced automation.”
- [claimed-docs] “agent = Agent(org_id="...", token="...") # Run an agent with a prompt task = agent.run(prompt="Leave a review on PR #123")”
- [claimed-docs] “Create and manage AI agents that can write code, fix bugs, and handle development tasks across your repositories with full programmatic cont…”
- [claimed-docs] “Trigger agents from CI/CD pipelines when builds fail”
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.
- [claimed-docs] “Bounded concurrent fan-out for independent kanban tasks, data items, or complete commands—with structured evidence for every item.”
- [claimed-docs] “Repeat bounded YYLO iterations until no open kanban work remains.”
- [claimed-docs] “Continue a captured session with `yy continue SESSION_ID`; do not reconstruct work from tmux scrollback.”
- [probe] “PROBE runtime (recorded 2026-09-14): `npm view @yylo/cli name version bin` — the official CLI is live on the public npm registry (@yylo/cli …”
- [probe] “PROBE runtime (recorded 2026-09-14): `curl -s https://raw.githubusercontent.com/yylo-dev/yylo/HEAD/LICENSE | head -3` returns 'MIT License /…”
ai-native userDefine rules that trigger actions automatically on events
weight 3 · round drawnCodegen 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.
- [claimed-docs] “Trigger agents from CI/CD pipelines when builds fail”
- [claimed-docs] “Reads the PR diff Summarizes changes across files Runs security and secrets scan Checks test coverage Posts a review comment on failure”
- [claimed-docs] “Programmatically interact with agents using the Python SDK for advanced automation.”
- [claimed-docs] “Create and manage AI agents that can write code, fix bugs, and handle development tasks across your repositories with full programmatic cont…”
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.
- [claimed-docs] “Bring Slack messages or GitHub issues into kanban and return completed responses to their source thread.”
- [claimed-docs] “Repeat bounded YYLO iterations until no open kanban work remains.”
- [claimed-docs] “Choose Workflow Runner when a step consumes `{{ steps.<id>.response }}`, a generated file, or a session from an earlier step.”
ai-native userSchedule recurring jobs or workflows
weight 2 · round to YYLOCodegennone0/10No 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.
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.
- [claimed-docs] “`yy loop` repeats arbitrary shell commands sequentially.”
- [claimed-docs] “Save the same contract as YAML for a reusable workflow.”
- [claimed-docs] “Choose Workflow Runner when a step consumes `{{ steps.<id>.response }}`, a generated file, or a session from an earlier step.”
- [claimed-docs] “Repeat bounded YYLO iterations until no open kanban work remains.”
- [github] “YYLO is a command-line orchestrator for coding agents, repeatable workflows, and receipt-backed repository changes. It is for developers who…”
ai-native userVersion, review, and roll back my automations
weight 1 · round to YYLOCodegennone0/10Evidence 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.
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.
- [github] “`task start` freezes the protected target SHA, creates a dedicated branch/worktree, and completes configured dependency hydration before rep…”
- [github] “`merge land` selects one immutable task source, composes in a private detached candidate, and uses Git expected-old ref protection. A moved …”
- [claimed-docs] “Save the same contract as YAML for a reusable workflow.”
- [claimed-docs] “The 0.2.1rc6 channel adds ID-first general Records and typed task, wiki, workflow, and artifact profiles.”
Autonomous implementation — end-to-end implementation by the agent — multi-file changes, task completionAutonomous implementation
End-to-end implementation by the agent — multi-file changes, task completion
End to end feature delivery
ai-native userHave an agent automatically generate and run tests to validate its own code changes before proposing them
weight 2 · round to CodegenDocs 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.
- [claimed-docs] “Analyze requirements, implement features, fix bugs, write tests, and improve documentation based on your prompts.”
- [claimed-docs] “Safely run code, install dependencies, and test changes in robust isolated environments”
- [claimed-docs] “Reads the PR diff Summarizes changes across files Runs security and secrets scan Checks test coverage Posts a review comment on failure”
YYLOnone0/10The 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.
- [github] “Tests and semantic reviews are explicit project checks outside merge. Merge launches no models, chooses no reviewers, schedules no suites, a…”
- [claimed-docs] “`yy loop` repeats arbitrary shell commands sequentially.”
developerHave an agent autonomously diagnose and fix a reported bug
weight 3 · round to CodegenDocs 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.
- [claimed-docs] “Analyze requirements, implement features, fix bugs, write tests, and improve documentation based on your prompts.”
- [claimed-docs] “Update statuses, add comments, link PRs to issues, and create new tasks. Support for Jira, Linear, Clickup and Monday.com.”
- [claimed-docs] “Safely run code, install dependencies, and test changes in robust isolated environments”
- [claimed-docs] “Create and manage AI agents that can write code, fix bugs, and handle development tasks across your repositories with full programmatic cont…”
- [claimed-docs] “Trigger agents from CI/CD pipelines when builds fail”
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.
- [claimed-docs] “npm install -g @yylo/cli@0.2.2 yylo init --task "Describe one verifiable outcome" --subagent claude”
- [claimed-docs] “Repeat bounded YYLO iterations until no open kanban work remains.”
- [claimed-docs] “Bring Slack messages or GitHub issues into kanban and return completed responses to their source thread.”
- [github] “YYLO is a command-line orchestrator for coding agents, repeatable workflows, and receipt-backed repository changes. It is for developers who…”
- [github] “`task start` freezes the protected target SHA, creates a dedicated branch/worktree, and completes configured dependency hydration before rep…”
product-managerGo from a mockup or design to a working implementation without an engineering handoff
weight 2 · round to CodegenCodegen'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.
- [claimed-docs] “Connect with Slack, Linear, Figma, databases, and extend capabilities with custom MCP tools.”
- [claimed-docs] “Send notifications, ask for clarification, report progress, and interact directly with your team in Slack channels.”
- [claimed-docs] “Review PRs, suggest changes, comment on issues, create branches, commit code, and manage repositories.”
- [claimed-docs] “Add the Codegen Slack App to communicate with the agent directly in your workspace.”
- [claimed-docs] “Connect your Linear workspace to enable agent interactions with your issues.”
YYLOnone0/10YYLO'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.
- [github] “YYLO is a command-line orchestrator for coding agents, repeatable workflows, and receipt-backed repository changes. It is for developers who…”
- [claimed-docs] “npm install -g @yylo/cli@0.2.2 yylo init --task "Describe one verifiable outcome" --subagent claude”
- [github] “`task start` freezes the protected target SHA, creates a dedicated branch/worktree, and completes configured dependency hydration before rep…”
- [github] “`merge land` selects one immutable task source, composes in a private detached candidate, and uses Git expected-old ref protection. A moved …”
developerHave an agent implement a requested feature end-to-end, including writing tests
weight 3 · round to CodegenDocs 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.
- [claimed-docs] “Analyze requirements, implement features, fix bugs, write tests, and improve documentation based on your prompts.”
- [claimed-docs] “Safely run code, install dependencies, and test changes in robust isolated environments”
- [claimed-docs] “Review PRs, suggest changes, comment on issues, create branches, commit code, and manage repositories.”
- [claimed-docs] “Create and manage AI agents that can write code, fix bugs, and handle development tasks across your repositories with full programmatic cont…”
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.
- [claimed-docs] “npm install -g @yylo/cli@0.2.2 yylo init --task "Describe one verifiable outcome" --subagent claude”
- [claimed-docs] “Start hydrates an exact-base task worktree. Preflight is read-only, finish queues the committed candidate”
- [github] “YYLO is a command-line orchestrator for coding agents, repeatable workflows, and receipt-backed repository changes. It is for developers who…”
- [github] “`task start` freezes the protected target SHA, creates a dedicated branch/worktree, and completes configured dependency hydration before rep…”
- [github] “Tests and semantic reviews are explicit project checks outside merge. Merge launches no models, chooses no reviewers, schedules no suites, a…”
Environment setup
developerHave an agent automatically clone the repo, install dependencies, and configure its own working environment
weight 2 · round to CodegenDocs 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.
- [claimed-docs] “Safely run code, install dependencies, and test changes in robust isolated environments”
- [claimed-docs] “Install the GitHub App to grant the agent access to your repositories. No complex setup required.”
- [claimed-docs] “Review PRs, suggest changes, comment on issues, create branches, commit code, and manage repositories.”
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.
- [github] “`task start` freezes the protected target SHA, creates a dedicated branch/worktree, and completes configured dependency hydration before rep…”
- [claimed-docs] “npm install -g @yylo/cli@0.2.2 yylo init --task "Describe one verifiable outcome" --subagent claude”
- [claimed-docs] “Start hydrates an exact-base task worktree. Preflight is read-only, finish queues the committed candidate”
- [probe] “PROBE runtime (recorded 2026-09-14): `npm view @yylo/cli name version bin` — the official CLI is live on the public npm registry (@yylo/cli …”
- [github] “A successful run prints the installed YYLO version, initializes `.juno_task/`, then emits a watch receipt with `"state":"COMPLETED"`, `"exit…”
Interactive takeover
developerTake over an in-progress agent task in my editor, terminal, or browser to finish or redirect the work
weight 2 · round to CodegenCodegen 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.
- [claimed-docs] “Send notifications, ask for clarification, report progress, and interact directly with your team in Slack channels.”
- [claimed-docs] “Use it to view agents, pull their work, create new agents, and run Claude Code with full telemetry and monitoring.”
- [claimed-docs] “Pull agent work - Download branches and code changes created by agents directly to your local environment”
- [claimed-docs] “agent = Agent(org_id="...", token="...") # Run an agent with a prompt task = agent.run(prompt="Leave a review on PR #123")”
- [claimed-docs] “Add the Codegen Slack App to communicate with the agent directly in your workspace.”
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.
- [claimed-docs] “Continue a captured session with `yy continue SESSION_ID`; do not reconstruct work from tmux scrollback.”
- [github] “`task start` freezes the protected target SHA, creates a dedicated branch/worktree, and completes configured dependency hydration before rep…”
- [github] “YYLO is a command-line orchestrator for coding agents, repeatable workflows, and receipt-backed repository changes. It is for developers who…”
- [claimed-docs] “Start hydrates an exact-base task worktree. Preflight is read-only, finish queues the committed candidate”
developerSend follow-up instructions to an active agent session to steer its work without restarting
weight 2 · round drawnCodegennone0/10No 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.
YYLOnone0/10YYLO'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.
- [claimed-docs] “Continue a captured session with `yy continue SESSION_ID`; do not reconstruct work from tmux scrollback.”
- [claimed-docs] “Start hydrates an exact-base task worktree. Preflight is read-only, finish queues the committed candidate”
- [github] “`task start` freezes the protected target SHA, creates a dedicated branch/worktree, and completes configured dependency hydration before rep…”
Sandbox execution
developerHave an agent safely execute code and install dependencies inside an isolated sandbox
weight 3 · round to CodegenFirst-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.
- [claimed-docs] “Safely run code, install dependencies, and test changes in robust isolated environments”
YYLOnone0/10YYLO'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.
- [github] “`task start` freezes the protected target SHA, creates a dedicated branch/worktree, and completes configured dependency hydration before rep…”
- [claimed-docs] “Start hydrates an exact-base task worktree. Preflight is read-only, finish queues the committed candidate”
- [github] “YYLO is a command-line orchestrator for coding agents, repeatable workflows, and receipt-backed repository changes. It is for developers who…”
Human oversight — keeping a human in the loop — approvals, checkpoints, interruptsHuman oversight
Keeping a human in the loop — approvals, checkpoints, interrupts
Approval controls
developerConfigure an agent to auto-approve all its actions instead of confirming each one
weight 2 · round drawnCodegennone0/10The 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.
YYLOnone0/10The 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.
- [github] “`task start` freezes the protected target SHA, creates a dedicated branch/worktree, and completes configured dependency hydration before rep…”
- [github] “`merge land` selects one immutable task source, composes in a private detached candidate, and uses Git expected-old ref protection. A moved …”
- [github] “Tests and semantic reviews are explicit project checks outside merge. Merge launches no models, chooses no reviewers, schedules no suites, a…”
product-managerApprove key agent decisions from my phone while agents continue working
weight 1 · round to CodegenCodegen 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.
- [claimed-docs] “Send notifications, ask for clarification, report progress, and interact directly with your team in Slack channels.”
- [claimed-docs] “Add the Codegen Slack App to communicate with the agent directly in your workspace.”
YYLOnone0/10YYLO 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.
- [github] “YYLO is a command-line orchestrator for coding agents, repeatable workflows, and receipt-backed repository changes. It is for developers who…”
- [claimed-docs] “npm install -g @yylo/cli@0.2.2 yylo init --task "Describe one verifiable outcome" --subagent claude”
- [claimed-docs] “yy ledger create "Validate recovery" --status todo --tags auth yy ledger ready --sort asc yy ledger get TASK_ID”
- [github] “`task start` freezes the protected target SHA, creates a dedicated branch/worktree, and completes configured dependency hydration before rep…”
- [github] “`merge land` selects one immutable task source, composes in a private detached candidate, and uses Git expected-old ref protection. A moved …”
engineering-leadSet tiered autonomy levels controlling what an agent can do without manual confirmation
weight 3 · round drawnCodegennone0/10No 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.
YYLOnone0/10Evidence 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.
Model control
ai-native userHave each task prompt automatically routed to the most suitable underlying model
weight 2 · round drawnCodegennone0/10No 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.
YYLOnone0/10Evidence 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.
- [claimed-docs] “npm install -g @yylo/cli@0.2.2 yylo init --task "Describe one verifiable outcome" --subagent claude”
engineering-leadSwitch away from automatic model selection to a specific model of my choice
weight 1 · round to YYLOCodegennone0/10No 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.
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.
- [claimed-docs] “npm install -g @yylo/cli@0.2.2 yylo init --task "Describe one verifiable outcome" --subagent claude”
Visibility monitoring
developerWatch what a running agent is doing in real time, including its current status
weight 3 · round to CodegenCodegen'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.
- [claimed-docs] “Use it to view agents, pull their work, create new agents, and run Claude Code with full telemetry and monitoring.”
- [claimed-docs] “Send notifications, ask for clarification, report progress, and interact directly with your team in Slack channels.”
- [claimed-docs] “programmatically retrieve detailed agent traces for analysis and debugging”
- [claimed-docs] “Pull agent work - Download branches and code changes created by agents directly to your local environment”
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.
- [github] “A successful run prints the installed YYLO version, initializes `.juno_task/`, then emits a watch receipt with `"state":"COMPLETED"`, `"exit…”
- [github] “`task start` freezes the protected target SHA, creates a dedicated branch/worktree, and completes configured dependency hydration before rep…”
- [claimed-docs] “Continue a captured session with `yy continue SESSION_ID`; do not reconstruct work from tmux scrollback.”
developerGet notified when an agent completes a task or needs my input
weight 2 · round to CodegenCodegen 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.
- [claimed-docs] “Send notifications, ask for clarification, report progress, and interact directly with your team in Slack channels.”
- [claimed-docs] “Update statuses, add comments, link PRs to issues, and create new tasks. Support for Jira, Linear, Clickup and Monday.com.”
- [claimed-docs] “Add the Codegen Slack App to communicate with the agent directly in your workspace.”
- [claimed-docs] “Connect your Linear workspace to enable agent interactions with your issues.”
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.
- [github] “A successful run prints the installed YYLO version, initializes `.juno_task/`, then emits a watch receipt with `"state":"COMPLETED"`, `"exit…”
- [claimed-docs] “Bring Slack messages or GitHub issues into kanban and return completed responses to their source thread.”
- [github] “Tests and semantic reviews are explicit project checks outside merge. Merge launches no models, chooses no reviewers, schedules no suites, a…”
Intent to spec — stories about intent to spec in this arenaIntent to spec
Stories about intent to spec in this arena
Natural language task intake
developerDescribe a feature or bug in plain language and have it automatically turned into a scoped implementation task
weight 3 · round to CodegenCodegen'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.
- [claimed-docs] “Analyze requirements, implement features, fix bugs, write tests, and improve documentation based on your prompts.”
- [claimed-docs] “agent = Agent(org_id="...", token="...") # Run an agent with a prompt task = agent.run(prompt="Leave a review on PR #123")”
- [claimed-docs] “Update statuses, add comments, link PRs to issues, and create new tasks. Support for Jira, Linear, Clickup and Monday.com.”
- [claimed-docs] “Send notifications, ask for clarification, report progress, and interact directly with your team in Slack channels.”
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.
- [claimed-docs] “npm install -g @yylo/cli@0.2.2 yylo init --task "Describe one verifiable outcome" --subagent claude”
- [claimed-docs] “Start hydrates an exact-base task worktree. Preflight is read-only, finish queues the committed candidate”
- [github] “`task start` freezes the protected target SHA, creates a dedicated branch/worktree, and completes configured dependency hydration before rep…”
- [claimed-docs] “yy ledger create "Validate recovery" --status todo --tags auth yy ledger ready --sort asc yy ledger get TASK_ID”
product-managerConvert user feedback submissions into structured tasks with proposed scope
weight 2 · round to YYLOCodegen 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.
- [claimed-docs] “Analyze requirements, implement features, fix bugs, write tests, and improve documentation based on your prompts.”
- [claimed-docs] “Update statuses, add comments, link PRs to issues, and create new tasks. Support for Jira, Linear, Clickup and Monday.com.”
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.
- [claimed-docs] “Bring Slack messages or GitHub issues into kanban and return completed responses to their source thread.”
- [probe] “PROBE runtime (recorded 2026-09-14): `npm view @yylo/cli name version bin` — the official CLI is live on the public npm registry (@yylo/cli …”
- [claimed-docs] “yy ledger create "Validate recovery" --status todo --tags auth yy ledger ready --sort asc yy ledger get TASK_ID”
Plan approval
developerReview and approve an agent's implementation plan before any code changes are made
weight 3 · round drawnCodegennone0/10No 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.
- [claimed-docs] “Analyze requirements, implement features, fix bugs, write tests, and improve documentation based on your prompts.”
- [claimed-docs] “Review PRs, suggest changes, comment on issues, create branches, commit code, and manage repositories.”
- [claimed-docs] “agent = Agent(org_id="...", token="...") # Run an agent with a prompt task = agent.run(prompt="Leave a review on PR #123")”
YYLOnone0/10Evidence 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.
- [claimed-docs] “Start hydrates an exact-base task worktree. Preflight is read-only, finish queues the committed candidate”
- [github] “`task start` freezes the protected target SHA, creates a dedicated branch/worktree, and completes configured dependency hydration before rep…”
- [github] “`merge land` selects one immutable task source, composes in a private detached candidate, and uses Git expected-old ref protection. A moved …”
- [github] “Tests and semantic reviews are explicit project checks outside merge. Merge launches no models, chooses no reviewers, schedules no suites, a…”
engineering-leadApprove a task's scope and contract before an agent is allowed to modify the repository
weight 2 · round to YYLOCodegennone0/10Evidence 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.
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.
- [claimed-docs] “Start hydrates an exact-base task worktree. Preflight is read-only, finish queues the committed candidate”
- [claimed-docs] “Save the same contract as YAML for a reusable workflow.”
- [github] “`task start` freezes the protected target SHA, creates a dedicated branch/worktree, and completes configured dependency hydration before rep…”
- [github] “`merge land` selects one immutable task source, composes in a private detached candidate, and uses Git expected-old ref protection. A moved …”
Ticket driven tasking
developerAssign a coding task to an agent directly from an existing issue or ticket
weight 3 · round to CodegenDocs 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.
- [claimed-docs] “Update statuses, add comments, link PRs to issues, and create new tasks. Support for Jira, Linear, Clickup and Monday.com.”
- [claimed-docs] “Connect your Linear workspace to enable agent interactions with your issues.”
- [claimed-docs] “Install the GitHub App to grant the agent access to your repositories. No complex setup required.”
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.
- [claimed-docs] “Bring Slack messages or GitHub issues into kanban and return completed responses to their source thread.”
- [claimed-docs] “npm install -g @yylo/cli@0.2.2 yylo init --task "Describe one verifiable outcome" --subagent claude”
- [claimed-docs] “yy ledger create "Validate recovery" --status todo --tags auth yy ledger ready --sort asc yy ledger get TASK_ID”
- [github] “`task start` freezes the protected target SHA, creates a dedicated branch/worktree, and completes configured dependency hydration before rep…”
Openness — open source, data portability, and self-hosting storiesOpenness
Open source, data portability, and self-hosting stories
ai-native userExport all of my data in open formats and leave
weight 3 · round to YYLOCodegennone0/10No 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.
- [claimed-docs] “Pull agent work - Download branches and code changes created by agents directly to your local environment”
- [claimed-docs] “programmatically retrieve detailed agent traces for analysis and debugging”
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.
- [claimed-docs] “Save the same contract as YAML for a reusable workflow.”
- [claimed-docs] “The 0.2.1rc6 channel adds ID-first general Records and typed task, wiki, workflow, and artifact profiles.”
- [probe] “PROBE runtime (recorded 2026-09-14): `curl -s https://raw.githubusercontent.com/yylo-dev/yylo/HEAD/LICENSE | head -3` returns 'MIT License /…”
- [github] “YYLO is a command-line orchestrator for coding agents, repeatable workflows, and receipt-backed repository changes. It is for developers who…”
ai-native userRead the product's source under an open license
weight 2 · round to YYLOCodegennone0/10No 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.
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.
- [probe] “PROBE runtime (recorded 2026-09-14): `curl -s https://raw.githubusercontent.com/yylo-dev/yylo/HEAD/LICENSE | head -3` returns 'MIT License /…”
- [github] “YYLO is a command-line orchestrator for coding agents, repeatable workflows, and receipt-backed repository changes. It is for developers who…”
ai-native userSelf-host the core product
weight 3 · round to YYLOCodegennone0/10Codegen 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'.
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.
- [probe] “PROBE runtime (recorded 2026-09-14): `npm view @yylo/cli name version bin` — the official CLI is live on the public npm registry (@yylo/cli …”
- [probe] “PROBE runtime (recorded 2026-09-14): `curl -s https://raw.githubusercontent.com/yylo-dev/yylo/HEAD/LICENSE | head -3` returns 'MIT License /…”
- [github] “YYLO is a command-line orchestrator for coding agents, repeatable workflows, and receipt-backed repository changes. It is for developers who…”
- [claimed-docs] “npm install -g @yylo/cli@0.2.2 yylo init --task "Describe one verifiable outcome" --subagent claude”
Pricing limits — free-tier ceilings, usage caps, and rate limits before you have to payPricing limits
Free-tier ceilings, usage caps, and rate limits before you have to pay
Enterprise licensing
engineering-leadLicense an enterprise deployment with SSO and commercial support for organization-wide rollout
weight 2 · round drawnCodegennone0/10No 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.
YYLOnone0/10YYLO'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.
- [github] “YYLO is a command-line orchestrator for coding agents, repeatable workflows, and receipt-backed repository changes. It is for developers who…”
- [probe] “PROBE runtime (recorded 2026-09-14): `npm view @yylo/cli name version bin` — the official CLI is live on the public npm registry (@yylo/cli …”
- [probe] “PROBE runtime (recorded 2026-09-14): `curl -s https://raw.githubusercontent.com/yylo-dev/yylo/HEAD/LICENSE | head -3` returns 'MIT License /…”
Model flexibility
engineering-leadBring my own LLM or API key so agents run on the model of my choice
weight 2 · round drawnCodegennone0/10No 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.
YYLOnone0/10Evidence 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.
- [claimed-docs] “npm install -g @yylo/cli@0.2.2 yylo init --task "Describe one verifiable outcome" --subagent claude”
Privacy posture — data-handling and privacy storiesPrivacy posture
Data-handling and privacy stories
ai-native userControl data retention and deletion
weight 2 · round drawnCodegennone0/10No 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.
ai-native userOpt out of telemetry and usage tracking
weight 2 · round drawnCodegennone0/10No 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.
- [claimed-docs] “Use it to view agents, pull their work, create new agents, and run Claude Code with full telemetry and monitoring.”
Repo integration — stories about repo integration in this arenaRepo integration
Stories about repo integration in this arena
Knowledge context
developerAdd a context file describing my codebase conventions so agents generate more relevant plans and code
weight 3 · round drawnCodegennone0/10The 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.
YYLOnone0/10The 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.
Project management integration
product-managerConnect issue trackers like Jira, Linear, ClickUp, or Monday.com so agents can manage tickets directly
weight 3 · round to CodegenDocs 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.
- [claimed-docs] “Update statuses, add comments, link PRs to issues, and create new tasks. Support for Jira, Linear, Clickup and Monday.com.”
- [claimed-docs] “Connect your Linear workspace to enable agent interactions with your issues.”
- [claimed-docs] “Connect with Slack, Linear, Figma, databases, and extend capabilities with custom MCP tools.”
YYLOnone0/10The 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.
- [claimed-docs] “Bring Slack messages or GitHub issues into kanban and return completed responses to their source thread.”
Version control integration
developerConnect a GitHub repository so an agent can access the code and open pull requests against it
weight 3 · round to CodegenCodegen 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.
- [claimed-docs] “Install the GitHub App to grant the agent access to your repositories. No complex setup required.”
- [claimed-docs] “Review PRs, suggest changes, comment on issues, create branches, commit code, and manage repositories.”
- [claimed-docs] “Create and manage AI agents that can write code, fix bugs, and handle development tasks across your repositories with full programmatic cont…”
- [claimed-docs] “Trigger agents from CI/CD pipelines when builds fail”
YYLOnone0/10Evidence 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.
- [github] “`task start` freezes the protected target SHA, creates a dedicated branch/worktree, and completes configured dependency hydration before rep…”
- [github] “`merge land` selects one immutable task source, composes in a private detached candidate, and uses Git expected-old ref protection. A moved …”
- [github] “Tests and semantic reviews are explicit project checks outside merge. Merge launches no models, chooses no reviewers, schedules no suites, a…”
- [claimed-docs] “Bring Slack messages or GitHub issues into kanban and return completed responses to their source thread.”
developerGrant an agent access to my repositories with a one-click install, without complex setup
weight 2 · round to CodegenDocs 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.
- [claimed-docs] “Install the GitHub App to grant the agent access to your repositories. No complex setup required.”
- [claimed-docs] “Review PRs, suggest changes, comment on issues, create branches, commit code, and manage repositories.”
YYLOnone0/10YYLO 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.
- [claimed-docs] “npm install -g @yylo/cli@0.2.2 yylo init --task "Describe one verifiable outcome" --subagent claude”
- [github] “`task start` freezes the protected target SHA, creates a dedicated branch/worktree, and completes configured dependency hydration before rep…”
- [probe] “PROBE runtime (recorded 2026-09-14): `npm view @yylo/cli name version bin` — the official CLI is live on the public npm registry (@yylo/cli …”
Review quality gates — quality gates on changes — review flow, required checks, merge protectionReview quality gates
Quality gates on changes — review flow, required checks, merge protection
Ci remediation
engineering-leadHave failed CI workflows automatically diagnosed and fixed with a proposed pull request
weight 3 · round to CodegenDocs 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.
- [claimed-docs] “Trigger agents from CI/CD pipelines when builds fail”
- [claimed-docs] “Analyze requirements, implement features, fix bugs, write tests, and improve documentation based on your prompts.”
- [claimed-docs] “Create and manage AI agents that can write code, fix bugs, and handle development tasks across your repositories with full programmatic cont…”
YYLOnone0/10YYLO 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.
- [github] “Tests and semantic reviews are explicit project checks outside merge. Merge launches no models, chooses no reviewers, schedules no suites, a…”
- [github] “`merge land` selects one immutable task source, composes in a private detached candidate, and uses Git expected-old ref protection. A moved …”
- [github] “YYLO is a command-line orchestrator for coding agents, repeatable workflows, and receipt-backed repository changes. It is for developers who…”
developerTrigger an agent from CI/CD pipelines to fix a broken build or failing test
weight 2 · round to CodegenDocs 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.
- [claimed-docs] “Trigger agents from CI/CD pipelines when builds fail”
- [claimed-docs] “Create and manage AI agents that can write code, fix bugs, and handle development tasks across your repositories with full programmatic cont…”
- [claimed-docs] “Programmatically interact with agents using the Python SDK for advanced automation.”
- [claimed-docs] “agent = Agent(org_id="...", token="...") # Run an agent with a prompt task = agent.run(prompt="Leave a review on PR #123")”
YYLOnone0/10YYLO 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.
- [github] “YYLO is a command-line orchestrator for coding agents, repeatable workflows, and receipt-backed repository changes. It is for developers who…”
- [claimed-docs] “Repeat bounded YYLO iterations until no open kanban work remains.”
- [claimed-docs] “Bring Slack messages or GitHub issues into kanban and return completed responses to their source thread.”
Diff review
developerConfigure an agent to automatically open a pull request when its task completes
weight 2 · round to CodegenCodegen'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.
- [claimed-docs] “Review PRs, suggest changes, comment on issues, create branches, commit code, and manage repositories.”
- [claimed-docs] “Install the GitHub App to grant the agent access to your repositories. No complex setup required.”
- [claimed-docs] “Create and manage AI agents that can write code, fix bugs, and handle development tasks across your repositories with full programmatic cont…”
- [claimed-docs] “Trigger agents from CI/CD pipelines when builds fail”
YYLOnone0/10YYLO'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.
- [github] “`merge land` selects one immutable task source, composes in a private detached candidate, and uses Git expected-old ref protection. A moved …”
- [github] “Tests and semantic reviews are explicit project checks outside merge. Merge launches no models, chooses no reviewers, schedules no suites, a…”
- [claimed-docs] “Bring Slack messages or GitHub issues into kanban and return completed responses to their source thread.”
- [claimed-docs] “Start hydrates an exact-base task worktree. Preflight is read-only, finish queues the committed candidate”
developerReview a diff of an agent's changes and approve it before it becomes a pull request
weight 3 · round to CodegenCodegen 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.
- [claimed-docs] “Review PRs, suggest changes, comment on issues, create branches, commit code, and manage repositories.”
- [claimed-docs] “Use it to view agents, pull their work, create new agents, and run Claude Code with full telemetry and monitoring.”
- [claimed-docs] “Pull agent work - Download branches and code changes created by agents directly to your local environment”
- [claimed-docs] “Reads the PR diff Summarizes changes across files Runs security and secrets scan Checks test coverage Posts a review comment on failure”
YYLOnone0/10Docs 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.
- [github] “`task start` freezes the protected target SHA, creates a dedicated branch/worktree, and completes configured dependency hydration before rep…”
- [github] “`merge land` selects one immutable task source, composes in a private detached candidate, and uses Git expected-old ref protection. A moved …”
- [github] “Tests and semantic reviews are explicit project checks outside merge. Merge launches no models, chooses no reviewers, schedules no suites, a…”
- [claimed-docs] “Start hydrates an exact-base task worktree. Preflight is read-only, finish queues the committed candidate”
Pr review automation
ai-native userHave incoming issues automatically triaged with severity suggested and routed to the right owner
weight 2 · round drawnCodegennone0/10Evidence 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.
- [claimed-docs] “Update statuses, add comments, link PRs to issues, and create new tasks. Support for Jira, Linear, Clickup and Monday.com.”
- [claimed-docs] “Connect your Linear workspace to enable agent interactions with your issues.”
YYLOnone0/10YYLO'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.
- [claimed-docs] “Bring Slack messages or GitHub issues into kanban and return completed responses to their source thread.”
engineering-leadHave every pull request automatically reviewed with AI-generated inline comments
weight 3 · round to CodegenDocs 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.
- [claimed-docs] “Review PRs, suggest changes, comment on issues, create branches, commit code, and manage repositories.”
- [claimed-docs] “Reads the PR diff Summarizes changes across files Runs security and secrets scan Checks test coverage Posts a review comment on failure”
- [claimed-docs] “Install the GitHub App to grant the agent access to your repositories. No complex setup required.”
- [claimed-docs] “Trigger agents from CI/CD pipelines when builds fail”
YYLOnone0/10YYLO'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.
- [github] “Tests and semantic reviews are explicit project checks outside merge. Merge launches no models, chooses no reviewers, schedules no suites, a…”
- [github] “`merge land` selects one immutable task source, composes in a private detached candidate, and uses Git expected-old ref protection. A moved …”
- [github] “YYLO is a command-line orchestrator for coding agents, repeatable workflows, and receipt-backed repository changes. It is for developers who…”
Readiness checks
engineering-leadAutomatically fix failing agent-readiness criteria in my repository
weight 1 · round to CodegenCodegen 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.
- [claimed-docs] “Analyze requirements, implement features, fix bugs, write tests, and improve documentation based on your prompts.”
- [claimed-docs] “Trigger agents from CI/CD pipelines when builds fail”
- [claimed-docs] “Reads the PR diff Summarizes changes across files Runs security and secrets scan Checks test coverage Posts a review comment on failure”
YYLOnone0/10YYLO'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.
engineering-leadRun a readiness report that evaluates how ready my repository is for autonomous agents
weight 2 · round to YYLOCodegennone0/10The 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.
- [claimed-docs] “Reads the PR diff Summarizes changes across files Runs security and secrets scan Checks test coverage Posts a review comment on failure”
- [claimed-docs] “Review PRs, suggest changes, comment on issues, create branches, commit code, and manage repositories.”
- [claimed-docs] “Safely run code, install dependencies, and test changes in robust isolated environments”
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.
Scale parallelism — running many jobs at once — concurrency, fleets, queueingScale parallelism
Running many jobs at once — concurrency, fleets, queueing
Concurrent execution
engineering-leadRun many agent tasks concurrently to scale delivery throughput
weight 3 · round drawnCodegen'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.
- [claimed-docs] “agent = Agent(org_id="...", token="...") # Run an agent with a prompt task = agent.run(prompt="Leave a review on PR #123")”
- [claimed-docs] “Create and manage AI agents that can write code, fix bugs, and handle development tasks across your repositories with full programmatic cont…”
- [claimed-docs] “Use it to view agents, pull their work, create new agents, and run Claude Code with full telemetry and monitoring.”
- [claimed-docs] “Trigger agents from CI/CD pipelines when builds fail”
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.
- [claimed-docs] “Bounded concurrent fan-out for independent kanban tasks, data items, or complete commands—with structured evidence for every item.”
- [claimed-docs] “Repeat bounded YYLO iterations until no open kanban work remains.”
- [claimed-docs] “Continue a captured session with `yy continue SESSION_ID`; do not reconstruct work from tmux scrollback.”
- [github] “YYLO is a command-line orchestrator for coding agents, repeatable workflows, and receipt-backed repository changes. It is for developers who…”
Deployment flexibility
engineering-leadSelf-host agent infrastructure locally, in containers, or on my own VMs
weight 2 · round to YYLOCodegennone0/10Codegen 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.
- [claimed-docs] “Safely run code, install dependencies, and test changes in robust isolated environments”
- [claimed-docs] “Log local Claude Code instances to the cloud and provision MCP servers across your org.”
- [claimed-docs] “Install the GitHub App to grant the agent access to your repositories. No complex setup required.”
- [claimed-docs] “Use it to view agents, pull their work, create new agents, and run Claude Code with full telemetry and monitoring.”
- [claimed-docs] “Create and manage AI agents that can write code, fix bugs, and handle development tasks across your repositories with full programmatic cont…”
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.
- [github] “YYLO is a command-line orchestrator for coding agents, repeatable workflows, and receipt-backed repository changes. It is for developers who…”
- [probe] “PROBE runtime (recorded 2026-09-14): `npm view @yylo/cli name version bin` — the official CLI is live on the public npm registry (@yylo/cli …”
- [probe] “PROBE runtime (recorded 2026-09-14): `curl -s https://raw.githubusercontent.com/yylo-dev/yylo/HEAD/LICENSE | head -3` returns 'MIT License /…”
- [github] “A successful run prints the installed YYLO version, initializes `.juno_task/`, then emits a watch receipt with `"state":"COMPLETED"`, `"exit…”
Headless automation
developerRun an agent headlessly inside CI/CD pipelines and shell scripts
weight 2 · round drawnCodegen 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.
- [claimed-docs] “Trigger agents from CI/CD pipelines when builds fail”
- [claimed-docs] “Programmatically interact with agents using the Python SDK for advanced automation.”
- [claimed-docs] “agent = Agent(org_id="...", token="...") # Run an agent with a prompt task = agent.run(prompt="Leave a review on PR #123")”
- [claimed-docs] “Use it to view agents, pull their work, create new agents, and run Claude Code with full telemetry and monitoring.”
- [probe] “official CLI documented at https://docs.codegen.com/introduction/cli”
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.
- [claimed-docs] “npm install -g @yylo/cli@0.2.2 yylo init --task "Describe one verifiable outcome" --subagent claude”
- [claimed-docs] “`yy loop` repeats arbitrary shell commands sequentially.”
- [claimed-docs] “Bounded concurrent fan-out for independent kanban tasks, data items, or complete commands—with structured evidence for every item.”
- [github] “`doctor workspace` is intentionally nonzero when it finds an actionable topology problem; it never fetches or changes the workspace.”
- [github] “YYLO is a command-line orchestrator for coding agents, repeatable workflows, and receipt-backed repository changes. It is for developers who…”
- [probe] “PROBE runtime (recorded 2026-09-14): `npm view @yylo/cli name version bin` — the official CLI is live on the public npm registry (@yylo/cli …”
Not comparable on these axes
ai-native userConnect an agent via an official MCP server
weight 3 · not comparableCodegenn/aCodegen 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.
- [claimed-docs] “Connect with Slack, Linear, Figma, databases, and extend capabilities with custom MCP tools.”
- [claimed-docs] “Log local Claude Code instances to the cloud and provision MCP servers across your org.”
YYLOnone0/10YYLO 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.
ai-native userIssue scoped/least-privilege API credentials for an agent
weight 2 · not comparableCodegennone0/10Codegen'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.
- [claimed-docs] “agent = Agent(org_id="...", token="...") # Run an agent with a prompt task = agent.run(prompt="Leave a review on PR #123")”
- [claimed-docs] “Create and manage AI agents that can write code, fix bugs, and handle development tasks across your repositories with full programmatic cont…”
developerAttach a marked-up screenshot or mockup to a task so the agent implements the correct visual change
weight 2 · not comparableCodegennone0/10No 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.
- [claimed-docs] “Connect with Slack, Linear, Figma, databases, and extend capabilities with custom MCP tools.”
YYLOn/aYYLO 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.
ai-native userDo everything through the API that I can do in the UI
weight 2 · not comparableCodegen 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.
- [claimed-docs] “Programmatically interact with agents using the Python SDK for advanced automation.”
- [claimed-docs] “agent = Agent(org_id="...", token="...") # Run an agent with a prompt task = agent.run(prompt="Leave a review on PR #123")”
- [claimed-docs] “Create and manage AI agents that can write code, fix bugs, and handle development tasks across your repositories with full programmatic cont…”
- [claimed-docs] “programmatically retrieve detailed agent traces for analysis and debugging”
- [claimed-docs] “Trigger agents from CI/CD pipelines when builds fail”
- [probe] “PROBE openapi: all candidate paths 404 (https://docs.codegen.com/openapi.json, https://docs.codegen.com/swagger.json, https://docs.codegen.c…”
YYLOn/aYYLO 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.
- [github] “YYLO is a command-line orchestrator for coding agents, repeatable workflows, and receipt-backed repository changes. It is for developers who…”
- [claimed-docs] “npm install -g @yylo/cli@0.2.2 yylo init --task "Describe one verifiable outcome" --subagent claude”
- [probe] “PROBE runtime (recorded 2026-09-14): `npm view @yylo/cli name version bin` — the official CLI is live on the public npm registry (@yylo/cli …”
engineering-leadSee and manage plan-based daily task and concurrency limits for agent workflows
weight 2 · not comparableCodegennone0/10No 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.
YYLOn/aYYLO 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.
- [github] “YYLO is a command-line orchestrator for coding agents, repeatable workflows, and receipt-backed repository changes. It is for developers who…”
- [claimed-docs] “Bounded concurrent fan-out for independent kanban tasks, data items, or complete commands—with structured evidence for every item.”
- [probe] “PROBE runtime (recorded 2026-09-14): `curl -s https://raw.githubusercontent.com/yylo-dev/yylo/HEAD/LICENSE | head -3` returns 'MIT License /…”
ai-native userChoose where my data is stored (region/residency)
weight 2 · not comparableCodegennone0/10No 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.
YYLOn/aYYLO 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.
- [github] “YYLO is a command-line orchestrator for coding agents, repeatable workflows, and receipt-backed repository changes. It is for developers who…”
- [claimed-docs] “npm install -g @yylo/cli@0.2.2 yylo init --task "Describe one verifiable outcome" --subagent claude”
ai-native userPrevent my data from being used to train AI models
weight 3 · not comparableCodegennone0/10No 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.
YYLOn/aYYLO 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.
developerTag an agent in a chat thread to discuss and delegate a bug or task
weight 2 · not comparableCodegen 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.
- [claimed-docs] “Send notifications, ask for clarification, report progress, and interact directly with your team in Slack channels.”
- [claimed-docs] “Add the Codegen Slack App to communicate with the agent directly in your workspace.”
- [claimed-docs] “Update statuses, add comments, link PRs to issues, and create new tasks. Support for Jira, Linear, Clickup and Monday.com.”
- [claimed-docs] “Connect your Linear workspace to enable agent interactions with your issues.”
YYLOn/aYYLO 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.
developerQuery generated documentation for any public or private repository
weight 1 · not comparableCodegennone0/10Evidence 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.
engineering-leadHave security alerts automatically validated and remediated with an opened pull request
weight 2 · not comparableCodegen 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. missing 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.
- [claimed-docs] “Reads the PR diff Summarizes changes across files Runs security and secrets scan Checks test coverage Posts a review comment on failure”
- [claimed-docs] “Trigger agents from CI/CD pipelines when builds fail”
- [claimed-docs] “Review PRs, suggest changes, comment on issues, create branches, commit code, and manage repositories.”
- [claimed-docs] “Analyze requirements, implement features, fix bugs, write tests, and improve documentation based on your prompts.”
YYLOn/aYYLO 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.
- [github] “YYLO is a command-line orchestrator for coding agents, repeatable workflows, and receipt-backed repository changes. It is for developers who…”
- [github] “Tests and semantic reviews are explicit project checks outside merge. Merge launches no models, chooses no reviewers, schedules no suites, a…”
engineering-leadCreate agent sessions on behalf of other users in my organization
weight 2 · not comparableCodegen'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.
- [claimed-docs] “Manage organizations - Switch between organizations and configure repositories”
- [claimed-docs] “agent = Agent(org_id="...", token="...") # Run an agent with a prompt task = agent.run(prompt="Leave a review on PR #123")”
- [claimed-docs] “Use it to view agents, pull their work, create new agents, and run Claude Code with full telemetry and monitoring.”
- [claimed-docs] “Create and manage AI agents that can write code, fix bugs, and handle development tasks across your repositories with full programmatic cont…”
YYLOn/aYYLO 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.
- [github] “YYLO is a command-line orchestrator for coding agents, repeatable workflows, and receipt-backed repository changes. It is for developers who…”
- [claimed-docs] “npm install -g @yylo/cli@0.2.2 yylo init --task "Describe one verifiable outcome" --subagent claude”
developerUse a managed cloud offering to run agents without operating my own backend infrastructure
weight 2 · not comparableCodegen 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.
- [claimed-docs] “Safely run code, install dependencies, and test changes in robust isolated environments”
- [claimed-docs] “Programmatically interact with agents using the Python SDK for advanced automation.”
- [claimed-docs] “agent = Agent(org_id="...", token="...") # Run an agent with a prompt task = agent.run(prompt="Leave a review on PR #123")”
- [claimed-docs] “Create and manage AI agents that can write code, fix bugs, and handle development tasks across your repositories with full programmatic cont…”
- [claimed-docs] “programmatically retrieve detailed agent traces for analysis and debugging”
- [claimed-docs] “Trigger agents from CI/CD pipelines when builds fail”
YYLOn/aYYLO 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.