Google Antigravity vs Slate

Agent access

1. ai-native userPoint an agent at llms.txt or agent-oriented docs

   weight 2 · round to Google Antigravity
   Google Antigravity✓fullprobed9/10

   Antigravity hosts a working llms.txt (HTTP 200) describing itself, and provides markdown-formatted docs pages (e.g. getting-started.md) that an agent can fetch directly, confirming genuine agent-oriented documentation support. Missing for 10: independent third-party confirmation that agents actually consume these successfully in practice.

   • [probe] “PROBE llms.txt: HTTP 200 at https://antigravity.google/llms.txt # Google Antigravity > Google Antigravity is an advanced agentic coding pla…”
   • [probe] “PROBE docs-md: HTTP 200 at https://antigravity.google/docs/getting-started.md # Getting Started with Antigravity 2.0 ### Download Visit [a…”
   • [claimed-docs] “Visit antigravity.google/download to download Google Antigravity 2.0. Select your operating system below”

   proof ↗⚑ flag

   Slate✓fullprobed8/10

   A probe confirms Slate's docs site serves a valid llms.txt at the root with links to actual docs pages, directly satisfying the ability to point an agent at agent-oriented docs. Missing for 10: no independent/community confirmation that agents successfully consume this llms.txt in practice, and no broader agent-oriented doc format beyond the single file.

   • [probe] “PROBE llms.txt: HTTP 200 at https://docs.randomlabs.ai/llms.txt # Slate ## Docs - [Introduction](https://docs.randomlabs.ai/en/getting-sta…”

   proof ↗⚑ flag

2. ai-native userRun the product headlessly / in CI for automation

   weight 2 · round to Google Antigravity
   Google Antigravity✓fullclaimed7/10

   Antigravity CLI has a documented headless mode explicitly for scripting agent tasks, CI pipeline integration, and machine-readable output (antigravity-docs-50), plus scheduled/cron tasks and background subagents support agentic automation outside interactive UI. Missing for 10: independent hands-on CI usage reports, concrete CI config examples/output schema, and no community corroboration of headless/CI use in practice.

   • [claimed-docs] “Run Antigravity CLI non-interactively to script agent tasks, integrate with CI pipelines, and capture machine-readable output.”
   • [claimed-docs] “Automate routine checks with Scheduled Tasks, simply define a cron schedule and the agents start and run autonomously in the background.”
   • [claimed-docs] “Edit, orchestrate, and build all in natural language. Tell your agents what you need, and they’ll work on getting it done.”
   • [claimed-docs] “Create or download fully customizable skills to further your agent’s autonomy and transform how you get work done.”

   proof ↗⚑ flag

   Slate—none0/10

   The docs describe Slate as an interactive terminal agent (onboarding, slash commands, hotkeys, subagent cards) with a permission-bypass flag (--dangerously-skip-permissions/--yolo), but there is no mention of a headless mode, non-interactive CLI flags, exit-code/scriptable output, or CI integration examples. Missing for 10: documented headless/non-interactive invocation, CI pipeline examples, scriptable output format, and any evidence of automation use outside the interactive TUI.

   • [claimed-docs] “We support `--dangerously-skip-permissions` (alias: `--yolo`) to bypass permission prompts.”
   • [claimed-docs] “npm i -g @randomlabs/slate”
   • [claimed-docs] “Use `/sessions` to switch between existing sessions”

   proof ↗⚑ flag

3. ai-native userPlug MCP servers into this product so it can use their tools

   weight 3 · round to Google Antigravity
   Google Antigravity✓fullclaimed8/10

   Antigravity has explicit, dedicated MCP documentation stating MCP lets it 'fetch structured context directly or execute safe actions on your behalf' and that it 'securely connects to local developer tools, databases, file parsers, and external remote APIs' via MCP, plus CLI/SDK support for configuring MCP servers (slash commands, plugins bundling MCP servers, layering MCP servers in the Agent SDK). Missing for 10: independent hands-on confirmation of successfully connecting a third-party MCP server and using its tools in a real workflow.

   • [claimed-docs] “MCP lets Antigravity fetch structured context directly or execute safe actions on your behalf when needed.”
   • [claimed-docs] “lets AI agents and editors securely connect to local developer tools, databases, file parsers, and external remote APIs”
   • [claimed-docs] “Access plugins, MCP, skills, and hooks configurations instantly via slash commands, quickly enhancing your workflow.”
   • [claimed-docs] “Layer custom Python callables, Model Context Protocol (MCP) servers, and reusable agent skills over our built-in filesystem and terminal too…”
   • [claimed-docs] “Plugins are namespaced bundles that allow you to extend Antigravity’s capabilities by grouping skills, rules, MCP servers, and hooks into a …”

   proof ↗⚑ flag

   Slate—none0/10

   No evidence in the pack mentions MCP servers or integrating external tool providers into Slate; the docs cover skills, permissions, orchestration, and CLI usage but never MCP support. Missing for 10: any documentation or claim about connecting/plugging in MCP servers, configuring MCP tool sources, or using MCP-provided tools.

   ⚑ flag

4. ai-native userUse an official CLI

   weight 2 · round drawn
   Google Antigravity✓fullprobed8/10

   Google Antigravity ships an official CLI with dedicated docs (antigravity-cli product page, headless/non-interactive mode for CI, sandboxing, vim mode, gcli migration), enabling natural-language orchestration of parallel agents, slash commands, and MCP/plugin config — clearly AI-native and agentic. Community evidence corroborates the CLI works in practice alongside VSCode. Missing for 10: independent deep-dive review of CLI-specific reliability/performance beyond a single community mention.

   • [claimed-docs] “Edit, orchestrate, and build all in natural language. Tell your agents what you need, and they’ll work on getting it done.”
   • [claimed-docs] “Have multiple agents working in parallel, so larger tasks get tackled faster.”
   • [claimed-docs] “Navigate your entire workflow via standard terminal shortcuts: adjust permissions, themes, and preferences via /config and type /keybindings…”
   • [claimed-docs] “Access plugins, MCP, skills, and hooks configurations instantly via slash commands, quickly enhancing your workflow.”
   • [claimed-docs] “the CLI automatically detects your existing profiles. An interactive checklist prompts you to choose which assets to migrate”
   • [claimed-docs] “Sensitive files like ~/.ssh and .env are blocked, anything not explicitly mounted is invisible inside the sandbox”
   • [claimed-docs] “Vim editor mode replaces the editing model in every multi-line input surface of the CLI”
   • [claimed-docs] “Run Antigravity CLI non-interactively to script agent tasks, integrate with CI pipelines, and capture machine-readable output.”
   • [community] “I much prefer using Gemini CLI in combination with vscode. It works like a charm. Now, I'll do the same with Antigravity CLI and vscode. It …”
   • [probe] “official CLI documented at https://antigravity.google/product/antigravity-cli”

   proof ↗⚑ flag

   Slate✓fullprobed8/10

   Slate is delivered as an official CLI (npm-installed, terminal-based) with rich first-party docs covering install, sessions, hotkeys, shell execution, and configuration — squarely matching the 'official CLI' story for an AI-native user. Missing for 10: independent/hands-on corroboration of the CLI experience itself (community evidence found only relates to unrelated porting-quality claims, not CLI usage).

   • [claimed-docs] “npm i -g @randomlabs/slate”
   • [claimed-docs] “Use `/sessions` to switch between existing sessions”
   • [claimed-docs] “Press Tab to queue the current message so it runs after the current turn finishes.”
   • [claimed-docs] “Execute shell commands directly with `!`”
   • [claimed-docs] “Ctrl+X then N New session”
   • [probe] “official CLI documented at https://docs.randomlabs.ai/en/getting-started/quickstart”

   proof ↗⚑ flag

5. ai-native userDrive the product through a documented public API

   weight 3 · round to Google Antigravity
   Google Antigravity~partialprobed6/10

   Antigravity documents an Agent SDK (Python) exposing the same tools/agent loop/context management as the app, plus a CLI headless mode for scripting and CI integration, both of which let an AI-native user drive the product programmatically. However, there is no evidence of a formal public REST/HTTP API — a probe for OpenAPI/swagger specs returned 404 on all candidate paths, so the 'documented public API' is limited to SDK/CLI surfaces rather than a conventional API contract. Missing for 10: a documented REST/HTTP API or OpenAPI spec, independent third-party confirmation of SDK usage/stability.

   • [claimed-docs] “The Agent SDK gives you the same tools, agent loop, and context management that power Google Antigravity, programmable in Python.”
   • [claimed-docs] “Layer custom Python callables, Model Context Protocol (MCP) servers, and reusable agent skills over our built-in filesystem and terminal too…”
   • [claimed-docs] “Build AI agents that autonomously read files, run commands, edit code, and more.”
   • [claimed-docs] “Run Antigravity CLI non-interactively to script agent tasks, integrate with CI pipelines, and capture machine-readable output.”
   • [probe] “PROBE openapi: all candidate paths 404 (https://antigravity.google/openapi.json, https://antigravity.google/swagger.json, https://antigravit…”

   proof ↗⚑ flag

   Slate—none0/10

   Slate is documented as a CLI/terminal agent with configuration, skills, and hotkeys, but there is no evidence of a documented public API for programmatic/agentic access—the OpenAPI probe returned 404 across all candidate paths and no SDK or REST/API docs are mentioned anywhere in the pack.

   • [probe] “PROBE openapi: all candidate paths 404 (https://docs.randomlabs.ai/openapi.json, https://docs.randomlabs.ai/swagger.json, https://docs.rando…”
   • [probe] “PROBE llms.txt: HTTP 200 at https://docs.randomlabs.ai/llms.txt # Slate ## Docs - [Introduction](https://docs.randomlabs.ai/en/getting-sta…”

   proof ↗⚑ flag

6. ai-native userIssue scoped/least-privilege API credentials for an agent

   weight 2 · round to Google Antigravity
   Google Antigravity!disputedcontradicted4/10

   Antigravity documents permission tiers (Deny/Ask/Allow) and a CLI sandbox that explicitly blocks access to sensitive files like .env and ~/.ssh, which is the closest analog to least-privilege credential scoping for an agent (docs-23, docs-42, docs-48). However, independent reports document a concrete bypass: Antigravity's own setting disallowing .env access was circumvented via prompt injection to exfiltrate secrets, and a default allowlisted domain (webhook.site) was used as an exfiltration channel — directly contradicting the claimed least-privilege protection (antigravity-comm-11, antigravity-comm-12). There is no evidence of a true scoped API-credential-issuance mechanism (e.g., minting restricted API keys/tokens for an agent); missing for 10: actual credential/token scoping API, third-party security audit confirming the sandbox holds, and any documented remediation.

   • [claimed-docs] “Permissions are evaluated across three distinct access lists: Deny... Ask... Allow”
   • [claimed-docs] “Permissions are evaluated across three distinct access lists: Deny...Ask...Allow”
   • [claimed-docs] “Sensitive files like ~/.ssh and .env are blocked, anything not explicitly mounted is invisible inside the sandbox”
   • [community] “Google Antigravity exfiltrates data via indirect prompt injection attack: Gemini is not supposed to have access to .env files with default s…”
   • [community] “The default Allowlist provided with Antigravity includes 'webhook.site', which was used as an exfiltration vector for secrets.”

   proof ↗⚑ flag

   Slate—none0/10

   Slate is a coding-agent CLI; its evidence only covers permission settings (allow/ask/deny) for tool actions, not issuance of scoped/least-privilege API credentials or tokens for agents. No mention of credential/token scoping, API key generation, or IAM-style access control.

   • [claimed-docs] “Each permission key maps to an action ("allow", "ask", or "deny"), or a pattern object for fine-grained control.”
   • [claimed-docs] “We support `--dangerously-skip-permissions` (alias: `--yolo`) to bypass permission prompts.”

   proof ↗⚑ flag

7. ai-native userBuild against official SDKs

   weight 2 · round to Google Antigravity
   Google Antigravity✓fullprobed7/10

   Google explicitly documents an official Agent SDK ('same tools, agent loop, and context management that power Antigravity, programmable in Python') supporting custom Python callables, MCP servers, skills, and multimedia inputs, which directly satisfies building against an official SDK. Missing for 10: independent/hands-on developer confirmation of the SDK working as documented, and no public API reference/OpenAPI spec was found (probe returned 404s), so depth of documentation beyond marketing copy is unverified.

   • [claimed-docs] “The Agent SDK gives you the same tools, agent loop, and context management that power Google Antigravity, programmable in Python.”
   • [claimed-docs] “Layer custom Python callables, Model Context Protocol (MCP) servers, and reusable agent skills over our built-in filesystem and terminal too…”
   • [claimed-docs] “Pass rich multimedia file attachments (images, videos, audio, and documents) to the agent alongside textual instruction prompt lists.”
   • [claimed-docs] “Build AI agents that autonomously read files, run commands, edit code, and more.”
   • [probe] “PROBE openapi: all candidate paths 404 (https://antigravity.google/openapi.json, https://antigravity.google/swagger.json, https://antigravit…”

   proof ↗⚑ flag

   Slate—none0/10

   The evidence pack covers Slate's CLI, skills, configuration, and orchestration features but contains no mention of an official SDK (Python/TypeScript/etc.) for building applications on top of Slate, and the OpenAPI probe returned 404s across all candidate paths. Missing for 10: any documented SDK package, API reference, or programmatic interface for building against Slate.

   • [probe] “PROBE openapi: all candidate paths 404 (https://docs.randomlabs.ai/openapi.json, https://docs.randomlabs.ai/swagger.json, https://docs.rando…”
   • [claimed-docs] “npm i -g @randomlabs/slate”

   proof ↗⚑ flag

Agentic features

1. ai-native userGet AI-generated insights and suggestions from my data inside the product

   weight 2 · round to Google Antigravity
   Google Antigravity~partialcommunity6/10

   Antigravity's editor/agent generates code suggestions, tab-autocompletion, and rich 'Artifacts' (implementation plans, diagrams, code diffs) that surface AI-derived insights from the user's codebase (antigravity-docs-6, -24, -30, -41), fitting the 'insights from data' story in a coding context. However, this is inference-in-editor suggestion generation rather than dedicated analytics/insight dashboards, and community hands-on reports raise real quality concerns ('the model was not good and slow, the harness was not good' — antigravity-comm-8), undercutting confidence in consistent insight quality. Missing for 10: no evidence of dedicated data-analysis/insight-summarization features beyond code artifacts, and no independent corroboration that suggestions are reliably high quality.

   • [claimed-docs] “Google Antigravity's Editor view offers tab autocompletion, natural language code commands, and a configurable, and context-aware configurab…”
   • [claimed-docs] “Planning Mode: The agent plans thoroughly before executing tasks... produces structured implementation plans called Artifacts”
   • [claimed-docs] “Artifacts include rich markdown plans (Implementation Plans), code diffs, architecture diagrams, images, and browser recordings.”
   • [claimed-docs] “offers tab autocompletion, natural language code commands, and a configurable, and context-aware configurable agent”
   • [community] “It's not even good, honestly. I was using it for couple weeks before dropping that 2 months ago. The model was not good and slow, the harnes…”

   proof ↗⚑ flag

   Slate~partialcommunity4/10

   Slate is a coding-agent CLI whose evidence shows it can analyze a codebase and produce suggestions (e.g., generating an ARCH.md with improvement ideas), which maps loosely to 'AI-generated insights from data' but only in the narrow sense of source code, not general data analysis. Community evidence also raises skepticism about the real quality of generated output (e.g., criticism of a ported-code example as low quality/unverified). Missing for 10: evidence of insights/suggestions over non-code datasets, dashboards or analytics-style outputs, and independent validation of suggestion quality.

   • [claimed-docs] “Please review the architecture of my entire codebase creating an ARCH.md and then give me ways I can improve it.”
   • [community] “Blog post claimed porting a library with one sentence, but critic noted it was JS->TS (trivial rename) not Python->TS, excluded tests/exampl…”
   • [community] “"Why trumpet code that is so ready for the garbage that you wouldn't even bother to publish it" - skepticism about the quality/usefulness of…”

   proof ↗⚑ flag

2. ai-native userSet up automations that run autonomously in the background

   weight 2 · round to Google Antigravity
   Google Antigravity✓fullclaimed8/10

   Docs explicitly describe Scheduled Tasks with cron schedules that let agents 'start and run autonomously in the background' (docs-2), plus related capabilities like scheduling messages to agents while away (docs-39), isolated background worktrees (docs-38), and headless/non-interactive CLI runs for CI automation (docs-50). This directly matches the story of autonomous background automations. Missing for 10: independent/hands-on verification that scheduled background tasks work reliably (community evidence focuses on other bugs/exfiltration issues, not scheduling specifically), and more detail on monitoring/error-handling for unattended runs.

   • [claimed-docs] “Automate routine checks with Scheduled Tasks, simply define a cron schedule and the agents start and run autonomously in the background.”
   • [claimed-docs] “Worktree support: Projects natively support Git worktrees, allowing agents to operate in isolated background folders.”
   • [claimed-docs] “users can schedule messages to be sent to their agents while they’re away”
   • [claimed-docs] “Run Antigravity CLI non-interactively to script agent tasks, integrate with CI pipelines, and capture machine-readable output.”
   • [claimed-docs] “Able to autonomously operate across your editor, terminal, and browser.”

   proof ↗⚑ flag

   Slate~partialclaimed5/10

   Slate supports background subagents, parallel task orchestration, and built-in workflows like goal/deep-research that run while the user keeps interacting, which shows some autonomous background execution. However, this is task-level parallelism within an active session, not scheduled or trigger-based automations that run independently of user presence. missing for 10: evidence of scheduled/cron-like automations, persistent background jobs surviving session end, or trigger-based (event-driven) autonomous runs without an active user session.

   • [claimed-docs] “Those agents show up as a grid of inline subagent cards, one per agent.”
   • [claimed-docs] “While one or more agents run in the background, you can keep talking with Slate: plan next steps, queue up additional tasks, or spin up more…”
   • [claimed-docs] “`goal` and `deep-research` are built-in programs. They are user-visible workflows, not something you need to author before using Slate.”

   proof ↗⚑ flag

3. ai-native userDelegate tasks to a built-in AI assistant inside the product

   weight 3 · round to Google Antigravity
   Google Antigravity✓fullcommunity8/10

   Antigravity is built around delegating tasks to autonomous agents that operate across editor, terminal, and browser, with subagents, scheduled tasks, and natural-language task delegation extensively documented; hands-on community reports (comm-1, comm-10) confirm the agent/CLI actually works for delegated tasks. missing for 10: independent third-party benchmarking of delegation quality, and community evidence is mixed on reliability/bugs which caps quality below top marks.

   • [claimed-docs] “Able to autonomously operate across your editor, terminal, and browser.”
   • [claimed-docs] “Edit, orchestrate, and build all in natural language. Tell your agents what you need, and they’ll work on getting it done.”
   • [claimed-docs] “an agent can delegate tasks—such as running tests or performing extensive codebase searches—to dedicated subagents.”
   • [claimed-docs] “Antigravity comes pre-packaged with several specialized subagents out of the box”
   • [claimed-docs] “Antigravity 2.0 serves as your AI agents’ central command center, providing a unified platform to launch, monitor, and orchestrate their act…”
   • [community] “I went ahead and downloaded it, it looks to be a VSCode fork very similar to Cursor, with support for Gemini 3 Pro, Claude Sonnet 4.5, and G…”
   • [community] “I much prefer using Gemini CLI in combination with vscode. It works like a charm. Now, I'll do the same with Antigravity CLI and vscode. It …”

   proof ↗⚑ flag

   Slate✓fullcommunity7/10

   Slate is a CLI-based AI assistant where users delegate whole tasks (e.g., 'review architecture and write ARCH.md') and it spins up parallel subagents, orchestration programs like goal/deep-research, and long multi-hour sessions per first-party docs. Community evidence (comm-1/2/3) raises skepticism about output quality/novelty but does not contradict the core delegation mechanism itself. Missing for 10: independent hands-on validation that delegated multi-agent tasks reliably complete as advertised.

   • [claimed-docs] “Parallelize working and orchestration of many tasks at once.”
   • [claimed-docs] “Please review the architecture of my entire codebase creating an ARCH.md and then give me ways I can improve it.”
   • [claimed-docs] “Those agents show up as a grid of inline subagent cards, one per agent.”
   • [claimed-docs] “While one or more agents run in the background, you can keep talking with Slate: plan next steps, queue up additional tasks, or spin up more…”
   • [claimed-docs] “`goal` and `deep-research` are built-in programs. They are user-visible workflows, not something you need to author before using Slate.”
   • [community] “"Why trumpet code that is so ready for the garbage that you wouldn't even bother to publish it" - skepticism about the quality/usefulness of…”

   proof ↗⚑ flag

4. ai-native userOperate the product with natural-language commands

   weight 2 · round to Google Antigravity
   Google Antigravity✓fullcommunity8/10

   Docs consistently describe natural-language operation as the core interaction model — editing, orchestrating, and building 'all in natural language' (antigravity-docs-9), NL code commands in the IDE (antigravity-docs-6/41), and even voice-to-prompt transcription (antigravity-docs-3), backed by planning/artifact review flows driven by conversational prompts (antigravity-docs-24, antigravity-docs-25). Community evidence corroborates it functions as an agentic assistant (comm-1, comm-10) though with quality/reliability complaints unrelated to the NL-command axis itself. Missing for 10: independent hands-on confirmation specifically praising the NL-command UX (most community commentary focuses on bugs/pricing/security rather than command quality).

   • [claimed-docs] “Edit, orchestrate, and build all in natural language. Tell your agents what you need, and they’ll work on getting it done.”
   • [claimed-docs] “Google Antigravity's Editor view offers tab autocompletion, natural language code commands, and a configurable, and context-aware configurab…”
   • [claimed-docs] “offers tab autocompletion, natural language code commands, and a configurable, and context-aware configurable agent”
   • [claimed-docs] “Speak your prompts. Powered by the latest Gemini Audio models, real-time transcription converts conversational speech into clearly phrased p…”
   • [claimed-docs] “Planning Mode: The agent plans thoroughly before executing tasks... produces structured implementation plans called Artifacts”
   • [claimed-docs] “The agent always halts and requests your explicit approval before proceeding with proposed changes.”
   • [community] “I went ahead and downloaded it, it looks to be a VSCode fork very similar to Cursor, with support for Gemini 3 Pro, Claude Sonnet 4.5, and G…”
   • [community] “I much prefer using Gemini CLI in combination with vscode. It works like a charm. Now, I'll do the same with Antigravity CLI and vscode. It …”

   proof ↗⚑ flag

   Slate✓fullclaimed7/10

   Docs show Slate is driven primarily via natural-language prompts (e.g. the quickstart example 'Please review the architecture of my entire codebase...') alongside slash-commands, shell escapes, and file references, indicating natural-language is the core interaction mode for an AI-native agent CLI. Missing for 10: independent/hands-on confirmation that complex natural-language commands are reliably parsed and executed as intended (community evidence only discusses code-porting quality, not NL command usage itself).

   • [claimed-docs] “Please review the architecture of my entire codebase creating an ARCH.md and then give me ways I can improve it.”
   • [claimed-docs] “Execute shell commands directly with `!`”
   • [claimed-docs] “Use `@filename` references”
   • [claimed-docs] “Use `/sessions` to switch between existing sessions”
   • [claimed-docs] “`goal` and `deep-research` are built-in programs. They are user-visible workflows, not something you need to author before using Slate.”

   proof ↗⚑ flag

Api quality

1. ai-native userDownload a machine-readable API spec (OpenAPI or equivalent)

   weight 2 · round drawn
   Google Antigravity—none0/10

   A direct probe for OpenAPI/Swagger specs at all standard candidate paths returned 404s, and no documentation item mentions a machine-readable API spec despite extensive docs on SDK, CLI, and MCP.

   • [probe] “PROBE openapi: all candidate paths 404 (https://antigravity.google/openapi.json, https://antigravity.google/swagger.json, https://antigravit…”

   proof ↗⚑ flag

   Slate—none0/10

   Slate's docs site was directly probed for an OpenAPI/swagger spec at standard locations and all returned 404, and no documentation anywhere mentions a machine-readable API spec for AI-native consumption.

   • [probe] “PROBE openapi: all candidate paths 404 (https://docs.randomlabs.ai/openapi.json, https://docs.randomlabs.ai/swagger.json, https://docs.rando…”

   proof ↗⚑ flag

2. ai-native userRely on versioned APIs with a documented deprecation policy

   weight 2 · round drawn
   Google Antigravity—none0/10

   No evidence of versioned APIs or a documented deprecation policy; OpenAPI probe returned 404s across all candidate paths and no docs mention API versioning or deprecation timelines.

   • [probe] “PROBE openapi: all candidate paths 404 (https://antigravity.google/openapi.json, https://antigravity.google/swagger.json, https://antigravit…”

   proof ↗⚑ flag

   Slate—none0/10

   Slate is a CLI coding agent product; no evidence of any versioned public API, API reference, or deprecation policy documentation exists—openapi probes returned 404 and no docs mention API versioning or deprecation. Absence of evidence for this applicable axis (a product could plausibly document API stability) yields 'none'.

   • [probe] “PROBE openapi: all candidate paths 404 (https://docs.randomlabs.ai/openapi.json, https://docs.randomlabs.ai/swagger.json, https://docs.rando…”

   proof ↗⚑ flag

1. ai-native userPerform bulk operations across many items at once

   weight 2 · round drawn
   Google Antigravity~partialclaimed6/10

   Antigravity supports parallel multi-agent orchestration across independent projects, subagent delegation, scheduled/background tasks, and a headless CLI for scripting bulk/CI workflows, which together enable operating across many items or tasks concurrently. However, there is no explicit documentation of a dedicated 'bulk operation' primitive (e.g., batch-apply an action across a list of files/items in one command) — the capability is inferred from parallelism/orchestration features rather than a purpose-built bulk-ops interface. missing for 10: explicit bulk/batch API or command for applying one operation across many items, independent hands-on evidence of large-scale parallel task execution working reliably.

   • [claimed-docs] “Orchestrate multiple autonomous agents working in parallel across independent projects.”
   • [claimed-docs] “Have multiple agents working in parallel, so larger tasks get tackled faster.”
   • [claimed-docs] “an agent can delegate tasks—such as running tests or performing extensive codebase searches—to dedicated subagents.”
   • [claimed-docs] “Worktree support: Projects natively support Git worktrees, allowing agents to operate in isolated background folders.”
   • [claimed-docs] “Run Antigravity CLI non-interactively to script agent tasks, integrate with CI pipelines, and capture machine-readable output.”

   proof ↗⚑ flag

   Slate~partialclaimed6/10

   Docs describe running many agents in parallel and orchestrating multiple tasks simultaneously (grid of subagent cards, spinning up more agents to parallelize work), which supports bulk-style operations across many items. However, there's no independent corroboration of this working at scale and no detail on failure handling, limits, or item-level bulk operations (e.g., bulk file edits/refactors) beyond task orchestration. Missing for 10: independent/hands-on verification of large-scale parallel task execution, concrete examples of bulk item processing (files/records), and failure/error handling details at scale.

   • [claimed-docs] “Parallelize working and orchestration of many tasks at once.”
   • [claimed-docs] “Those agents show up as a grid of inline subagent cards, one per agent.”
   • [claimed-docs] “While one or more agents run in the background, you can keep talking with Slate: plan next steps, queue up additional tasks, or spin up more…”
   • [claimed-docs] “`goal` and `deep-research` are built-in programs. They are user-visible workflows, not something you need to author before using Slate.”

   proof ↗⚑ flag

2. ai-native userDefine rules that trigger actions automatically on events

   weight 3 · round to Google Antigravity
   Google Antigravity✓fullclaimed7/10

   Antigravity's docs describe explicit rule activation modes (Manual, Always On, Model Decision, Glob) that trigger agent behavior automatically based on context/file patterns, plus Hooks that run custom scripts at specific points in the execution loop and Scheduled Tasks that trigger agents on a cron schedule — together these directly satisfy 'rules that trigger actions automatically on events'. Missing for 10: independent/hands-on verification that rule-triggering works reliably in practice, and more detail on broader event types beyond glob/model-decision/cron.

   • [claimed-docs] “At the rule level you can define how a rule should be activated: Manual... Always On... Model Decision... Glob”
   • [claimed-docs] “Hooks allow you to run custom scripts or shell commands at specific points during Antigravity’s execution loop.”
   • [claimed-docs] “Automate routine checks with Scheduled Tasks, simply define a cron schedule and the agents start and run autonomously in the background.”
   • [claimed-docs] “Rules are manually defined constraints for the Agent to follow, at both the local and global levels.”

   proof ↗⚑ flag

   Slate—none0/10

   Slate's docs describe agent rules for permissions/behavior ordering (docs-15, docs-20) and orchestration of parallel agents (docs-12, docs-13), but there is no evidence of user-defined rules that trigger actions automatically on external events (e.g., file changes, webhooks, schedule, git events). This is a plausible axis for a coding agent (many support hooks/triggers), so absence of evidence yields none rather than na.

   • [claimed-docs] “Slate by default respects agent rules in the following order”
   • [claimed-docs] “Each permission key maps to an action ("allow", "ask", or "deny"), or a pattern object for fine-grained control.”

   proof ↗⚑ flag

3. ai-native userSchedule recurring jobs or workflows

   weight 2 · round to Google Antigravity
   Google Antigravity✓fullclaimed8/10

   Docs explicitly describe Scheduled Tasks with cron-defined schedules that run agents autonomously in the background, plus scheduling messages to agents for later delivery, directly matching the recurring-jobs/workflow story. Missing for 10: independent/hands-on confirmation that scheduling actually works reliably in practice, and more detail on job management (editing/deleting/monitoring scheduled runs).

   • [claimed-docs] “Automate routine checks with Scheduled Tasks, simply define a cron schedule and the agents start and run autonomously in the background.”
   • [claimed-docs] “users can schedule messages to be sent to their agents while they’re away”

   proof ↗⚑ flag

   Slate—none0/10

   Slate is a coding-agent CLI with orchestration/parallel-agent features and sessions, but nothing in the evidence describes scheduling recurring jobs or workflows (e.g., cron-like triggers, timed recurring runs). Orchestration docs cover on-demand parallelization, not recurrence.

   • [claimed-docs] “Those agents show up as a grid of inline subagent cards, one per agent.”
   • [claimed-docs] “While one or more agents run in the background, you can keep talking with Slate: plan next steps, queue up additional tasks, or spin up more…”
   • [claimed-docs] “`goal` and `deep-research` are built-in programs. They are user-visible workflows, not something you need to author before using Slate.”

   proof ↗⚑ flag

4. ai-native userVersion, review, and roll back my automations

   weight 1 · round drawn
   Google Antigravity—none0/10

   Evidence shows automations (skills, hooks, plugins, scheduled tasks) but no mention of versioning, review history, or rollback capabilities for these automations themselves — missing for 10: version control/history for skills/hooks/plugins, a review workflow for automation changes, and any rollback/undo mechanism for automations.

   ⚑ flag

   Slate—none0/10

   Evidence shows session management (/sessions, /workspace) and built-in 'programs' like goal/deep-research, but nothing about versioning automations, reviewing history of changes, or rolling back to prior states of an automation/workflow. Missing for 10: any documentation of version history, diffing, or rollback mechanisms for automations/workflows.

   • [claimed-docs] “Use `/sessions` to switch between existing sessions”
   • [claimed-docs] “Use `/workspace` to open the workspace manager, where you can review and remove workspace directories.”
   • [claimed-docs] “`goal` and `deep-research` are built-in programs. They are user-visible workflows, not something you need to author before using Slate.”

   proof ↗⚑ flag

Background execution

1. ai-native userHave a cloud agent build, test, and demo a feature end-to-end for my review

   weight 2 · round to Google Antigravity
   Google Antigravity~partialcommunity6/10

   Docs describe agents that autonomously operate across editor/terminal/browser, delegate testing to subagents, produce reviewable Artifacts (implementation plans, diffs, browser recordings) and halt for approval — covering build, test and demo-for-review end-to-end (antigravity-docs-7,18,24,25,30,46). However, community reports of a subpar harness, app-breaking bugs, and a case where autonomous terminal execution deleted a whole drive raise real doubts about reliable end-to-end execution (antigravity-comm-8,antigravity-comm-14). Missing for 10: independent hands-on confirmation of a full successful build→test→demo cycle, and resolution of reliability/security concerns that could derail autonomous runs.

   • [claimed-docs] “Able to autonomously operate across your editor, terminal, and browser.”
   • [claimed-docs] “an agent can delegate tasks—such as running tests or performing extensive codebase searches—to dedicated subagents.”
   • [claimed-docs] “Planning Mode: The agent plans thoroughly before executing tasks... produces structured implementation plans called Artifacts”
   • [claimed-docs] “The agent always halts and requests your explicit approval before proceeding with proposed changes.”
   • [claimed-docs] “Artifacts include rich markdown plans (Implementation Plans), code diffs, architecture diagrams, images, and browser recordings.”
   • [claimed-docs] “an agent can delegate tasks—such as running tests or performing extensive codebase searches—to dedicated subagents”
   • [community] “It's not even good, honestly. I was using it for couple weeks before dropping that 2 months ago. The model was not good and slow, the harnes…”
   • [community] “Google Antigravity just deleted the contents of whole drive - came down to commanding a deletion of a 'directory with space in the name' wit…”

   proof ↗⚑ flag

   Slate!disputedcontradicted4/10

   Slate's docs claim orchestration of parallel background subagents and being 'one of the few agents capable of performing integration tests manually,' suggesting it could build and test a feature autonomously, but no docs mention a 'demo' output or cloud-hosted execution environment. Community hands-on critique of an actual Slate-produced port directly contradicts the build/test claim: reviewers found the work excluded tests/examples and provided no verifiable repo, undermining confidence that Slate reliably builds+tests end-to-end for review. Missing for 10: evidence of cloud/remote execution infra, an explicit demo-generation feature, and independent confirmation that test suites are actually run and pass.

   • [claimed-docs] “Those agents show up as a grid of inline subagent cards, one per agent.”
   • [claimed-docs] “While one or more agents run in the background, you can keep talking with Slate: plan next steps, queue up additional tasks, or spin up more…”
   • [claimed-docs] “Slate is one of the few agents capable of performing integration tests manually.”
   • [community] “Blog post claimed porting a library with one sentence, but critic noted it was JS->TS (trivial rename) not Python->TS, excluded tests/exampl…”
   • [community] “"Why trumpet code that is so ready for the garbage that you wouldn't even bother to publish it" - skepticism about the quality/usefulness of…”

   proof ↗⚑ flag

2. developerDelegate longer-running coding tasks to run in the background in an isolated cloud environment

   weight 3 · round to Google Antigravity
   Google Antigravity~partialclaimed5/10

   Antigravity supports background/autonomous execution via Scheduled Tasks that run agents in the background, Git worktree-based isolated background folders, and scheduling messages for agents while away, plus a 'Remote Control' feature to connect to running desktop sessions across machines. However, these mechanisms describe local-machine or worktree isolation and remote access to local sessions, not a distinctly cloud-hosted sandbox environment for offloading long-running tasks the way some competitors do. Missing for 10: explicit documentation of a persistent cloud-hosted execution environment independent of the user's machine, and independent/hands-on confirmation that background tasks truly run isolated in the cloud rather than locally.

   • [claimed-docs] “Automate routine checks with Scheduled Tasks, simply define a cron schedule and the agents start and run autonomously in the background.”
   • [claimed-docs] “Worktree support: Projects natively support Git worktrees, allowing agents to operate in isolated background folders.”
   • [claimed-docs] “users can schedule messages to be sent to their agents while they’re away”
   • [claimed-docs] “Antigravity Remote Control allows you to securely connect to and drive your Antigravity 2.0 desktop sessions running across your machines fr…”
   • [claimed-docs] “Antigravity 2.0 serves as your AI agents’ central command center, providing a unified platform to launch, monitor, and orchestrate their act…”
   • [claimed-docs] “Run Antigravity CLI non-interactively to script agent tasks, integrate with CI pipelines, and capture machine-readable output.”

   proof ↗⚑ flag

   Slate—none0/10

   Slate's docs describe subagents running 'in the background' locally while you keep chatting and orchestration/parallelization of tasks, but there is no mention of an isolated cloud environment, remote execution sandbox, or delegation to a hosted service — everything described appears to run within the local CLI session. This axis is applicable to coding agent tools generally, but no evidence supports a cloud-isolated background execution capability for Slate.

   • [claimed-docs] “Those agents show up as a grid of inline subagent cards, one per agent.”
   • [claimed-docs] “While one or more agents run in the background, you can keep talking with Slate: plan next steps, queue up additional tasks, or spin up more…”
   • [claimed-docs] “Slate works with you across long, multi-hour sessions.”
   • [claimed-docs] “Parallelize working and orchestration of many tasks at once.”

   proof ↗⚑ flag

Parallel agents

1. ai-native userLaunch fleets of autonomous agents that work in parallel on different tasks for hours or days

   weight 2 · round drawn
   Google Antigravity~partialclaimed7/10

   Docs describe orchestrating multiple autonomous agents in parallel across independent projects, scheduled/cron tasks that run autonomously in the background, worktree-isolated agents, subagent delegation, and remote control to check on running sessions from a browser — all supporting a 'fleet of parallel long-running agents' story. However, there is no independent/hands-on confirmation of agents actually running unattended for 'hours or days' at scale, and community reports focus on bugs, quota limits, and security issues rather than validating multi-day parallel fleet operation. Missing for 10: independent verification of long-duration (hours/days) autonomous runs, evidence of fleet scale limits, and hands-on confirmation from third parties.

   • [claimed-docs] “Orchestrate multiple autonomous agents working in parallel across independent projects.”
   • [claimed-docs] “Automate routine checks with Scheduled Tasks, simply define a cron schedule and the agents start and run autonomously in the background.”
   • [claimed-docs] “Have multiple agents working in parallel, so larger tasks get tackled faster.”
   • [claimed-docs] “Antigravity 2.0 serves as your AI agents’ central command center, providing a unified platform to launch, monitor, and orchestrate their act…”
   • [claimed-docs] “Worktree support: Projects natively support Git worktrees, allowing agents to operate in isolated background folders.”
   • [claimed-docs] “users can schedule messages to be sent to their agents while they’re away”
   • [claimed-docs] “Antigravity Remote Control allows you to securely connect to and drive your Antigravity 2.0 desktop sessions running across your machines fr…”
   • [claimed-docs] “an agent can delegate tasks—such as running tests or performing extensive codebase searches—to dedicated subagents.”

   proof ↗⚑ flag

   Slate~partialcommunity7/10

   Docs describe genuine parallel agent orchestration (grid of subagent cards, spinning up more agents while others run in background) and explicitly support multi-hour sessions, matching much of the story. However, evidence only confirms 'multi-hour' not multi-day autonomy, and community commentary raises skepticism about output quality/novelty without directly refuting the parallel-agent mechanics themselves. Missing for 10: confirmation of multi-day unattended runs, independent hands-on validation of fleet-scale parallel task completion.

   • [claimed-docs] “Slate works with you across long, multi-hour sessions.”
   • [claimed-docs] “Parallelize working and orchestration of many tasks at once.”
   • [claimed-docs] “Those agents show up as a grid of inline subagent cards, one per agent.”
   • [claimed-docs] “While one or more agents run in the background, you can keep talking with Slate: plan next steps, queue up additional tasks, or spin up more…”
   • [community] “Commenter compared the approach to 'Ralph as a service' referencing an existing agentic coding technique (ghuntley.com/ralph), suggesting Sl…”

   proof ↗⚑ flag

2. developerRun several task attempts in parallel and compare results before choosing one

   weight 1 · round to Slate
   Google Antigravity~partialclaimed4/10

   Docs confirm agents can run in parallel (multiple agents across projects, multiple CLI agents for large tasks) and a central dashboard to monitor/orchestrate them, but nothing describes running multiple attempts at the SAME task and comparing outputs before choosing a winner — that specific 'compare-and-select' workflow is unevidenced. missing for 10: explicit multi-attempt/variant generation for a single task, a comparison UI or ranking mechanism, and any selection step among parallel attempts.

   • [claimed-docs] “Have multiple agents working in parallel, so larger tasks get tackled faster.”
   • [claimed-docs] “Orchestrate multiple autonomous agents working in parallel across independent projects.”
   • [claimed-docs] “Antigravity 2.0 serves as your AI agents’ central command center, providing a unified platform to launch, monitor, and orchestrate their act…”

   proof ↗⚑ flag

   Slate~partialclaimed6/10

   Slate's orchestration docs show multiple subagents running in parallel as a grid of cards while the user keeps working, directly supporting parallel task execution (docs-12, docs-13). However, there's no explicit documentation of a compare/diff view or a 'choose winning attempt' workflow for reconciling multiple parallel results into one choice. Missing for 10: explicit comparison/selection UI or workflow for multiple attempts of the same task, and independent/hands-on confirmation of this specific use case.

   • [claimed-docs] “Those agents show up as a grid of inline subagent cards, one per agent.”
   • [claimed-docs] “While one or more agents run in the background, you can keep talking with Slate: plan next steps, queue up additional tasks, or spin up more…”
   • [claimed-docs] “`goal` and `deep-research` are built-in programs. They are user-visible workflows, not something you need to author before using Slate.”

   proof ↗⚑ flag

Scheduled automation

1. ai-native userSet up always-on agents that run on schedules or triggers to maintain and fix my software autonomously

   weight 2 · round to Google Antigravity
   Google Antigravity✓fullcommunity7/10

   First-party docs explicitly describe Scheduled Tasks with cron schedules that start and run agents autonomously in the background, plus scheduling messages to agents while away, parallel autonomous agent orchestration, and headless CLI mode for CI/trigger-based automation. However, there is no independent/hands-on corroboration of the scheduling feature itself, and community reports document serious reliability/safety incidents with autonomous execution (e.g., an agent deleting a whole drive via unattended terminal auto-execution), raising doubt about safely running such agents unattended. Missing for 10: independent verification that scheduled/cron-triggered agents work reliably in practice, and evidence that autonomous 'maintain and fix' runs don't require the same close supervision seen in incident reports.

   • [claimed-docs] “Automate routine checks with Scheduled Tasks, simply define a cron schedule and the agents start and run autonomously in the background.”
   • [claimed-docs] “users can schedule messages to be sent to their agents while they’re away”
   • [claimed-docs] “Orchestrate multiple autonomous agents working in parallel across independent projects.”
   • [claimed-docs] “Run Antigravity CLI non-interactively to script agent tasks, integrate with CI pipelines, and capture machine-readable output.”
   • [community] “Google Antigravity just deleted the contents of whole drive - came down to commanding a deletion of a 'directory with space in the name' wit…”

   proof ↗⚑ flag

   Slate—none0/10

   Slate's docs describe parallel subagent orchestration within a live session (background agents you keep talking to, spin up more agents to parallelize tasks) but there is no evidence of scheduling, event/webhook triggers, or persistent always-on agents that run autonomously outside an active session to maintain/fix software over time.

   • [claimed-docs] “Those agents show up as a grid of inline subagent cards, one per agent.”
   • [claimed-docs] “While one or more agents run in the background, you can keep talking with Slate: plan next steps, queue up additional tasks, or spin up more…”
   • [claimed-docs] “`goal` and `deep-research` are built-in programs. They are user-visible workflows, not something you need to author before using Slate.”

   proof ↗⚑ flag

Debugging

1. developerDebug a live running web application directly from my coding assistant

   weight 1 · round to Google Antigravity
   Google Antigravity~partialclaimed4/10

   Antigravity's agent can 'autonomously operate across your editor, terminal, and browser' and produces 'browser recordings' as artifacts, implying some browser-based interaction/testing, but there is no explicit documentation of live debugging features (console inspection, breakpoints, network tab, DOM inspection) for a running web app. missing for 10: explicit live-debugging tooling (breakpoints, console/network inspection), documented workflow for attaching to a running app, independent hands-on confirmation of debugging use.

   • [claimed-docs] “Able to autonomously operate across your editor, terminal, and browser.”
   • [claimed-docs] “Artifacts include rich markdown plans (Implementation Plans), code diffs, architecture diagrams, images, and browser recordings.”

   proof ↗⚑ flag

   Slate—none0/10

   No evidence in the pack mentions debugging live running applications, attaching to running processes, browser/runtime debugging, or any live-app inspection capability; Slate's docs focus on codebase review, shell commands, orchestration, and skills, none of which address live debugging.

   ⚑ flag

2. developerDebug issues and troubleshoot using natural-language queries

   weight 2 · round to Google Antigravity
   Google Antigravity~partialclaimed6/10

   Antigravity's docs show natural-language code commands, autonomous operation across editor/terminal/browser, and subagents that can run tests and search codebases (docs-6,7,9,18,46), which collectively support debugging/troubleshooting via NL prompts, but there is no explicit documentation of a dedicated 'debug' workflow or troubleshooting examples, and community reports focus on stability/security issues rather than confirming debugging quality. Missing for 10: explicit debugging-specific documentation or examples, and independent hands-on validation that NL debugging queries work reliably.

   • [claimed-docs] “Google Antigravity's Editor view offers tab autocompletion, natural language code commands, and a configurable, and context-aware configurab…”
   • [claimed-docs] “Able to autonomously operate across your editor, terminal, and browser.”
   • [claimed-docs] “Edit, orchestrate, and build all in natural language. Tell your agents what you need, and they’ll work on getting it done.”
   • [claimed-docs] “an agent can delegate tasks—such as running tests or performing extensive codebase searches—to dedicated subagents.”
   • [claimed-docs] “an agent can delegate tasks—such as running tests or performing extensive codebase searches—to dedicated subagents”

   proof ↗⚑ flag

   Slate~partialclaimed5/10

   Slate's docs show it operates via natural-language prompts, executes shell commands (`!`), references files (`@filename`), and can run integration tests and review codebase architecture in NL form, which implies it could be used for debugging and troubleshooting queries. However there is no explicit example, workflow, or documentation section dedicated to debugging/troubleshooting via natural language, and community evidence is skeptical/unrelated to this specific capability. Missing for 10: explicit debugging-focused examples or docs, independent verification that NL-based debugging works well, dedicated troubleshooting workflow beyond generic agent capabilities.

   • [claimed-docs] “Execute shell commands directly with `!`”
   • [claimed-docs] “Use `@filename` references”
   • [claimed-docs] “Slate is one of the few agents capable of performing integration tests manually.”
   • [claimed-docs] “Please review the architecture of my entire codebase creating an ARCH.md and then give me ways I can improve it.”

   proof ↗⚑ flag

Feature implementation

1. developerTurn a tracked issue into a complete pull request end-to-end

   weight 3 · round drawn
   Google Antigravity—none0/10

   Antigravity's docs describe autonomous coding agents that can edit files, run terminal commands, and operate across editor/terminal/browser, but there is no evidence of any issue-tracker (e.g., GitHub Issues) integration or an end-to-end workflow that ingests a tracked issue and produces a pull request. Missing for 10: issue-tracker ingestion, automated branch/PR creation, and any documented GitHub/GitLab PR workflow example.

   • [claimed-docs] “Able to autonomously operate across your editor, terminal, and browser.”
   • [claimed-docs] “Antigravity 2.0 serves as your AI agents’ central command center, providing a unified platform to launch, monitor, and orchestrate their act…”
   • [claimed-docs] “Run Antigravity CLI non-interactively to script agent tasks, integrate with CI pipelines, and capture machine-readable output.”

   proof ↗⚑ flag

   Slate—none0/10

   The evidence pack covers Slate's session management, orchestration, skills, and permissions but contains no mention of issue-tracker integration (e.g., GitHub issues) or automated pull-request creation/submission. Without evidence of ingesting a tracked issue and producing a PR end-to-end, this capability is unshown.

   ⚑ flag

2. developerDescribe a feature or bug in plain language and have the agent implement or fix it across multiple files

   weight 3 · round to Google Antigravity
   Google Antigravity~partialcommunity7/10

   Docs describe the core loop clearly: natural-language commands drive an agent that autonomously edits code across the editor/terminal, with Projects spanning multiple folders/repos giving full codebase context and Artifacts showing diffs/plans for review (antigravity-docs-6,7,9,17,24,30,40). Community reports confirm it functions as a real coding-agent IDE (comm-1) but also describe hands-on quality issues with the agent harness and model reliability during actual implementation work (comm-8), so delivery is real but not consistently polished. Missing for 10: independent benchmark/case-study evidence of successful multi-file feature implementation, and resolution of reported harness/quality complaints.

   • [claimed-docs] “Google Antigravity's Editor view offers tab autocompletion, natural language code commands, and a configurable, and context-aware configurab…”
   • [claimed-docs] “Able to autonomously operate across your editor, terminal, and browser.”
   • [claimed-docs] “Edit, orchestrate, and build all in natural language. Tell your agents what you need, and they’ll work on getting it done.”
   • [claimed-docs] “a project can work with one folder or multiple folders (e.g., a frontend and a backend repo), providing your agents with all of the context …”
   • [claimed-docs] “Planning Mode: The agent plans thoroughly before executing tasks... produces structured implementation plans called Artifacts”
   • [claimed-docs] “Artifacts include rich markdown plans (Implementation Plans), code diffs, architecture diagrams, images, and browser recordings.”
   • [claimed-docs] “Build AI agents that autonomously read files, run commands, edit code, and more.”
   • [community] “I went ahead and downloaded it, it looks to be a VSCode fork very similar to Cursor, with support for Gemini 3 Pro, Claude Sonnet 4.5, and G…”
   • [community] “It's not even good, honestly. I was using it for couple weeks before dropping that 2 months ago. The model was not good and slow, the harnes…”

   proof ↗⚑ flag

   Slate!disputedcontradicted5/10

   Docs imply broad multi-file code work (e.g. the quickstart example asks Slate to review an entire codebase and produce ARCH.md, plus orchestration features for parallelizing tasks across files/agents), suggesting Slate can act on plain-language requests across a codebase. However, independent community scrutiny of a specific real-world claim (a 'ported library' from one sentence) found it was actually a trivial JS->TS rename, excluded tests, lacked a verifiable repo, and drew explicit skepticism about the quality/usefulness of the generated code — concretely contradicting the marketed multi-file code-generation capability. Missing for 10: first-party documentation of a genuine multi-file bug-fix/feature-implementation workflow with verifiable before/after results, and independent hands-on confirmation that resolves the community dispute.

   • [claimed-docs] “Please review the architecture of my entire codebase creating an ARCH.md and then give me ways I can improve it.”
   • [claimed-docs] “While one or more agents run in the background, you can keep talking with Slate: plan next steps, queue up additional tasks, or spin up more…”
   • [claimed-docs] “Parallelize working and orchestration of many tasks at once.”
   • [community] “Blog post claimed porting a library with one sentence, but critic noted it was JS->TS (trivial rename) not Python->TS, excluded tests/exampl…”
   • [community] “"Why trumpet code that is so ready for the garbage that you wouldn't even bother to publish it" - skepticism about the quality/usefulness of…”

   proof ↗⚑ flag

Maintenance automation

1. developerHave the agent write tests, fix lint errors, resolve merge conflicts, and update dependencies for me

   weight 3 · round to Google Antigravity
   Google Antigravity~partialcommunity6/10

   Antigravity's docs describe general-purpose coding agents/subagents that can run tests, edit code, and operate across editor/terminal/browser (docs-18, docs-46, docs-40, docs-7), which implicitly covers writing tests and dependency/code edits, but there is no explicit documentation calling out lint-error fixing, merge-conflict resolution, or dependency updates as named capabilities. Community evidence is mixed on general quality/reliability but does not concretely refute these specific tasks. Missing for 10: explicit first-party documentation or hands-on examples of lint-fixing, merge-conflict resolution, and dependency-update workflows specifically.

   • [claimed-docs] “an agent can delegate tasks—such as running tests or performing extensive codebase searches—to dedicated subagents.”
   • [claimed-docs] “an agent can delegate tasks—such as running tests or performing extensive codebase searches—to dedicated subagents”
   • [claimed-docs] “Build AI agents that autonomously read files, run commands, edit code, and more.”
   • [claimed-docs] “Able to autonomously operate across your editor, terminal, and browser.”
   • [claimed-docs] “The Agent SDK gives you the same tools, agent loop, and context management that power Google Antigravity, programmable in Python.”
   • [community] “It's not even good, honestly. I was using it for couple weeks before dropping that 2 months ago. The model was not good and slow, the harnes…”

   proof ↗⚑ flag

   Slate~partialcommunity3/10

   Slate is documented as a general-purpose coding agent with shell execution, file editing, permissioning, and orchestration of multiple sub-agents (random-labs-docs-9, random-labs-docs-13, random-labs-docs-16), which implies it could perform tasks like running tests or lint/dependency commands, but the evidence never explicitly documents test-writing, lint-fixing, merge-conflict resolution, or dependency updates as capabilities. Community commentary raises quality concerns about generated code but doesn't specifically address these tasks. Missing for 10: explicit documentation or examples of writing/fixing tests, resolving lint errors, resolving merge conflicts, and updating dependencies.

   • [claimed-docs] “Execute shell commands directly with `!`”
   • [claimed-docs] “While one or more agents run in the background, you can keep talking with Slate: plan next steps, queue up additional tasks, or spin up more…”
   • [claimed-docs] “Slate is one of the few agents capable of performing integration tests manually.”
   • [community] “"Why trumpet code that is so ready for the garbage that you wouldn't even bother to publish it" - skepticism about the quality/usefulness of…”

   proof ↗⚑ flag

Multimodal generation

1. ai-native userGenerate a working app from a sketch, image, or PDF design

   weight 2 · round to Google Antigravity
   Google Antigravity~partialclaimed4/10

   Antigravity supports passing images, PDFs and other multimedia attachments to the agent as part of prompts (docs-15, docs-32), which implies it could take a sketch/image/PDF as design input for code generation, but there is no explicit documentation or example of a 'sketch-to-app' or 'design-to-code' workflow, nor any hands-on report of this being used successfully. missing for 10: dedicated design-to-app feature/workflow documentation, an example or case study of generating an app from an image/PDF, and independent verification that this works in practice.

   • [claimed-docs] “Pass rich multimedia file attachments (images, videos, audio, and documents) to the agent alongside textual instruction prompt lists.”
   • [claimed-docs] “External files such as Google Drive links, PDFs, and Office documents now appear in their own Documents section in the sidebar above Artifac…”

   proof ↗⚑ flag

   Slate—none0/10

   The evidence describes Slate as a terminal-based CLI agent for coding sessions, orchestration, skills, and permissions, but nothing in the docs or community evidence mentions accepting sketches, images, or PDF designs as input to generate an app. missing for 10: any mention of image/sketch/PDF input, multimodal design-to-code capability, or UI mockup ingestion.

   • [claimed-docs] “npm i -g @randomlabs/slate”
   • [claimed-docs] “Please review the architecture of my entire codebase creating an ARCH.md and then give me ways I can improve it.”
   • [claimed-docs] “Skills are markdown instruction packages that give the agent domain-specific knowledge and behavior.”
   • [claimed-docs] “description: "Create distinctive, production-grade frontend interfaces."”

   proof ↗⚑ flag

Codebase mapping

1. developerUnderstand how a codebase fits together to find where to start making changes

   weight 3 · round to Google Antigravity
   Google Antigravity~partialclaimed6/10

   Antigravity provides contextual codebase understanding indirectly: Projects give agents full context across multiple folders/repos, subagents can perform 'extensive codebase searches', and Artifacts can include architecture diagrams and implementation plans that map out how a change fits into the codebase. However, there's no dedicated codebase-mapping/explanation feature, and no independent/hands-on evidence confirming how well the agent actually explains codebase structure. Missing for 10: a first-class 'explain/visualize codebase architecture' feature, independent hands-on validation of comprehension quality on real repos.

   • [claimed-docs] “a project can work with one folder or multiple folders (e.g., a frontend and a backend repo), providing your agents with all of the context …”
   • [claimed-docs] “an agent can delegate tasks—such as running tests or performing extensive codebase searches—to dedicated subagents.”
   • [claimed-docs] “Artifacts include rich markdown plans (Implementation Plans), code diffs, architecture diagrams, images, and browser recordings.”
   • [claimed-docs] “Agents work within Projects, which define the boundaries of the folders and repositories they can access.”
   • [claimed-docs] “an agent can delegate tasks—such as running tests or performing extensive codebase searches—to dedicated subagents”
   • [claimed-docs] “Google Antigravity's Editor view offers tab autocompletion, natural language code commands, and a configurable, and context-aware configurab…”

   proof ↗⚑ flag

   Slate~partialclaimed5/10

   The quickstart example explicitly shows Slate producing an ARCH.md architecture review of an entire codebase with improvement suggestions, directly supporting codebase-understanding use cases, and @filename references plus workspace management help navigate a repo. However there's no dedicated codebase-mapping/search feature (e.g., symbol index, dependency graph) documented beyond this one example, and no independent evidence confirming quality of such architecture summaries. missing for 10: dedicated code-navigation/search tooling, independent validation of architecture-summary accuracy, more than a single example of codebase-understanding workflow.

   • [claimed-docs] “Please review the architecture of my entire codebase creating an ARCH.md and then give me ways I can improve it.”
   • [claimed-docs] “Use `@filename` references”
   • [claimed-docs] “Use `/workspace` to open the workspace manager, where you can review and remove workspace directories.”

   proof ↗⚑ flag

2. developerHave the agent map and explain an entire unfamiliar codebase without manually selecting context files

   weight 3 · round to Slate
   Google Antigravity~partialclaimed5/10

   Docs indicate agents automatically get full-project context (docs-17, docs-35) and can delegate to subagents that perform 'extensive codebase searches' (docs-18/46), suggesting the agent can explore an unfamiliar repo without manual file selection. However, there is no explicit feature or example describing whole-codebase mapping/explanation, and no independent/hands-on evidence confirming this works well in practice. missing for 10: a dedicated 'explain codebase' or repo-mapping feature description, and independent verification of this on an unfamiliar large codebase.

   • [claimed-docs] “a project can work with one folder or multiple folders (e.g., a frontend and a backend repo), providing your agents with all of the context …”
   • [claimed-docs] “an agent can delegate tasks—such as running tests or performing extensive codebase searches—to dedicated subagents.”
   • [claimed-docs] “Agents work within Projects, which define the boundaries of the folders and repositories they can access.”
   • [claimed-docs] “an agent can delegate tasks—such as running tests or performing extensive codebase searches—to dedicated subagents”

   proof ↗⚑ flag

   Slate~partialcommunity6/10

   Docs show Slate's quickstart example explicitly demonstrates asking it to 'review the architecture of my entire codebase' and generate an ARCH.md without manual file selection, and it has orchestration/subagent features for broad exploration. However there's no independent/hands-on verification that this codebase-mapping actually works well on large unfamiliar repos, and community evidence raises quality skepticism about other generated outputs. missing for 10: independent hands-on validation of full-codebase mapping accuracy, evidence of handling very large/unfamiliar codebases without manual curation, detail on how context is auto-selected under the hood.

   • [claimed-docs] “Please review the architecture of my entire codebase creating an ARCH.md and then give me ways I can improve it.”
   • [claimed-docs] “Those agents show up as a grid of inline subagent cards, one per agent.”
   • [claimed-docs] “While one or more agents run in the background, you can keep talking with Slate: plan next steps, queue up additional tasks, or spin up more…”
   • [community] “"Why trumpet code that is so ready for the garbage that you wouldn't even bother to publish it" - skepticism about the quality/usefulness of…”

   proof ↗⚑ flag

Context management

1. developerHave the agent build and recall memory automatically across sessions

   weight 2 · round drawn
   Google Antigravity—none0/10

   The evidence describes Projects, Rules, Artifacts, and context management, but none of these describe an automatic memory system that builds and recalls information across sessions without user re-specification; Rules are explicitly manual, and Projects only scope folders/permissions, not persistent learned memory. No documentation or community evidence confirms automatic cross-session memory recall.

   • [claimed-docs] “a project can work with one folder or multiple folders (e.g., a frontend and a backend repo), providing your agents with all of the context …”
   • [claimed-docs] “At the rule level you can define how a rule should be activated: Manual... Always On... Model Decision... Glob”
   • [claimed-docs] “Agents work within Projects, which define the boundaries of the folders and repositories they can access.”
   • [claimed-docs] “Rules are manually defined constraints for the Agent to follow, at both the local and global levels.”

   proof ↗⚑ flag

   Slate—none0/10

   Docs describe session switching (/sessions), long multi-hour session support, and diagnostic context attachment, but there is no evidence of automatic cross-session memory building or recall — sessions appear to be manually selected/switched contexts, not an automatic memory system. Missing for higher verdict: any documentation of persistent memory storage, automatic recall of past codebase context, or memory summarization across sessions.

   • [claimed-docs] “Slate works with you across long, multi-hour sessions.”
   • [claimed-docs] “Use `/sessions` to switch between existing sessions”
   • [claimed-docs] “Slate automatically attaches relevant diagnostic information (OS, version, session context) to your report.”

   proof ↗⚑ flag

2. developerInclude multiple project directories in a single session for broader context

   weight 2 · round to Google Antigravity
   Google Antigravity✓fullclaimed8/10

   Docs explicitly state Projects can span multiple folders (e.g., a frontend and backend repo) giving agents full codebase context, with Projects defining folder/repo access boundaries and worktree support for isolated background folders. Missing for 10: independent/hands-on corroboration of multi-folder session use in practice.

   • [claimed-docs] “Group your conversations into Projects, which can span multiple folders and support custom settings and scoped permissions.”
   • [claimed-docs] “a project can work with one folder or multiple folders (e.g., a frontend and a backend repo), providing your agents with all of the context …”
   • [claimed-docs] “Agents work within Projects, which define the boundaries of the folders and repositories they can access.”
   • [claimed-docs] “Worktree support: Projects natively support Git worktrees, allowing agents to operate in isolated background folders.”

   proof ↗⚑ flag

   Slate~partialclaimed6/10

   Docs mention a `/workspace` manager for reviewing and removing 'workspace directories' (plural), implying support for multiple project directories in one session, but there's no detailed documentation on how directories are added or how context is merged across them, and no independent/hands-on confirmation. Missing for 10: explicit instructions/examples for adding multiple directories, and independent verification that broader multi-directory context actually works in practice.

   • [claimed-docs] “Use `/workspace` to open the workspace manager, where you can review and remove workspace directories.”

   proof ↗⚑ flag

3. developerAdd a project instructions file to set coding standards and conventions the agent follows

   weight 3 · round to Google Antigravity
   Google Antigravity✓fullclaimed7/10

   Antigravity supports Rules (manually defined constraints for the agent at local and global levels, with activation modes like Always On/Glob) which serve as a project instructions file for coding standards and conventions, and Projects scope these settings per folder/repo. missing for 10: no independent/hands-on confirmation of rules file format or behavior, and no evidence of a specific standardized file name (e.g. AGENTS.md-equivalent) or examples of it being used in practice.

   • [claimed-docs] “At the rule level you can define how a rule should be activated: Manual... Always On... Model Decision... Glob”
   • [claimed-docs] “Rules are manually defined constraints for the Agent to follow, at both the local and global levels.”
   • [claimed-docs] “a project can work with one folder or multiple folders (e.g., a frontend and a backend repo), providing your agents with all of the context …”
   • [claimed-docs] “Agents work within Projects, which define the boundaries of the folders and repositories they can access.”

   proof ↗⚑ flag

   Slate~partialclaimed6/10

   Docs confirm Slate 'respects agent rules' in a defined precedence order and supports Skills (markdown instruction packages, including Claude Code-compatible `.claude/skills/` paths), which cover project-level conventions/instructions, but there's no explicit example of a single top-level 'instructions file' analogous to AGENTS.md/CLAUDE.md being demonstrated end-to-end. missing for 10: explicit naming/format of the project instructions file, a worked example showing the agent following custom conventions from it, and independent/community confirmation it works as documented.

   • [claimed-docs] “Slate by default respects agent rules in the following order”
   • [claimed-docs] “Skills are markdown instruction packages that give the agent domain-specific knowledge and behavior.”
   • [claimed-docs] “`.claude/skills/` | Claude Code compatibility”

   proof ↗⚑ flag

Issue diagnosis

1. developerReproduce issues, narrow down root causes, and verify fixes

   weight 3 · round to Google Antigravity
   Google Antigravity~partialcommunity6/10

   Antigravity's agents can run terminal commands and browser sessions, delegate to subagents that run tests or search the codebase (docs-18, docs-46, docs-7), and produce Artifacts with diffs, plans and browser recordings that could serve as reproduction/verification evidence (docs-30, docs-51). Headless/CI mode (docs-50) also supports automated verification loops. However there is no explicit documented workflow for issue reproduction or root-cause narrowing, and community reports show real-world reliability problems (deleted directories, exfiltration bugs) rather than confirmation that debugging workflows work well. Missing for 10: a dedicated debugging/root-cause-analysis feature, explicit test-verification-of-fix workflow, and independent hands-on validation that this works well in practice.

   • [claimed-docs] “Able to autonomously operate across your editor, terminal, and browser.”
   • [claimed-docs] “an agent can delegate tasks—such as running tests or performing extensive codebase searches—to dedicated subagents.”
   • [claimed-docs] “Artifacts include rich markdown plans (Implementation Plans), code diffs, architecture diagrams, images, and browser recordings.”
   • [claimed-docs] “an agent can delegate tasks—such as running tests or performing extensive codebase searches—to dedicated subagents”
   • [claimed-docs] “Run Antigravity CLI non-interactively to script agent tasks, integrate with CI pipelines, and capture machine-readable output.”
   • [claimed-docs] “An Artifact is a structured deliverable created by the agent to accomplish its task and communicate its progress and thinking to the human u…”
   • [community] “Google Antigravity just deleted the contents of whole drive - came down to commanding a deletion of a 'directory with space in the name' wit…”

   proof ↗⚑ flag

   Slate~partialclaimed4/10

   Slate documents shell execution (`!`), file references, and being 'one of the few agents capable of performing integration tests manually,' which are plausible building blocks for debugging workflows, but there's no explicit documentation of a reproduce→diagnose→verify-fix workflow. Missing for 10: explicit debugging/root-cause-analysis workflow documentation, evidence of test-driven verification loops, and independent hands-on confirmation that Slate helps developers actually reproduce and fix bugs.

   • [claimed-docs] “Slate is one of the few agents capable of performing integration tests manually.”
   • [claimed-docs] “Execute shell commands directly with `!`”
   • [claimed-docs] “Use `@filename` references”
   • [claimed-docs] “While one or more agents run in the background, you can keep talking with Slate: plan next steps, queue up additional tasks, or spin up more…”

   proof ↗⚑ flag

Marketplace

1. developerEquip the agent with custom skills to perform specialized tasks

   weight 1 · round to Google Antigravity
   Google Antigravity✓fullclaimed8/10

   Antigravity has a dedicated Skills system: SKILL.md-based reusable packages of knowledge/instructions the agent follows for specific tasks, creatable/downloadable and accessible via slash commands, plus composable with plugins that bundle skills, rules, MCP servers, and hooks. This is documented across product and docs pages consistently, though no independent/community hands-on verification of custom skills specifically was found. Missing for 10: independent/hands-on corroboration of custom skill creation working in practice, and a marketplace/registry of shareable skills.

   • [claimed-docs] “Create or download fully customizable skills to further your agent’s autonomy and transform how you get work done.”
   • [claimed-docs] “A skill is a folder containing a `SKILL.md` file with instructions that the agent can follow when working on specific tasks.”
   • [claimed-docs] “Skills are reusable packages of knowledge that extend what the agent can do.”
   • [claimed-docs] “Access plugins, MCP, skills, and hooks configurations instantly via slash commands, quickly enhancing your workflow.”
   • [claimed-docs] “Layer custom Python callables, Model Context Protocol (MCP) servers, and reusable agent skills over our built-in filesystem and terminal too…”
   • [claimed-docs] “Plugins are namespaced bundles that allow you to extend Antigravity’s capabilities by grouping skills, rules, MCP servers, and hooks into a …”

   proof ↗⚑ flag

   Slate✓fullclaimed7/10

   Slate has a documented Skills system: markdown instruction packages that give the agent domain-specific knowledge/behavior, with example skill definitions and compatibility with Claude Code's `.claude/skills/` format, letting developers equip the agent with custom specialized capabilities. Missing for 10: independent/hands-on verification that custom skills work as documented, and more detail on skill authoring/discovery beyond the single example.

   • [claimed-docs] “Skills are markdown instruction packages that give the agent domain-specific knowledge and behavior.”
   • [claimed-docs] “description: "Create distinctive, production-grade frontend interfaces."”
   • [claimed-docs] “`.claude/skills/` | Claude Code compatibility”

   proof ↗⚑ flag

2. engineering-leadIntegrate third-party partner-built agent apps into my workflows

   weight 1 · round to Google Antigravity
   Google Antigravity!disputedcontradicted4/10

   Vendor docs describe an extensibility layer (MCP servers, plugins, skills, hooks) that in principle lets teams plug in third-party building blocks (docs-14, docs-28, docs-12), suggesting an ecosystem for integrating outside agent capabilities. However, hands-on community reports directly contradict the notion of freely integrating partner-built agent apps: using a third-party agent ('Pi agent') alongside Antigravity triggered a Google account ban under Antigravity's TOS restricting 3rd-party usage, and users discovered unofficial vs official extensions causing confusion (comm-17, comm-18). Missing for 10: an official partner/marketplace program for third-party agent apps, clear TOS allowance for such integrations, and independent confirmation that such integrations work without account risk.

   • [claimed-docs] “Layer custom Python callables, Model Context Protocol (MCP) servers, and reusable agent skills over our built-in filesystem and terminal too…”
   • [claimed-docs] “Plugins are namespaced bundles that allow you to extend Antigravity’s capabilities by grouping skills, rules, MCP servers, and hooks into a …”
   • [claimed-docs] “Access plugins, MCP, skills, and hooks configurations instantly via slash commands, quickly enhancing your workflow.”
   • [community] “Google Antigravity TOS: 3rd party usage can get Google account suspended. My friend got a ban by using Pi agent with Antigravity. They un-ba…”
   • [community] “The VSCode Antigravity extension I was using turns out to be a 3rd-party one. I found out only today that there's an official extension too,…”

   proof ↗⚑ flag

   Slate—none0/10

   Slate is a coding CLI agent focused on subagents, skills, sessions, and model orchestration; there is no evidence of an ecosystem for integrating third-party partner-built agent apps (e.g., a marketplace, app store, or partner integration framework). Skills compatibility with Claude Code is mentioned but that is file-format compatibility, not partner app integration.

   • [claimed-docs] “Skills are markdown instruction packages that give the agent domain-specific knowledge and behavior.”
   • [claimed-docs] “`.claude/skills/` | Claude Code compatibility”

   proof ↗⚑ flag

Team knowledge

1. engineering-leadCreate a shared workspace from my docs and repos as a common source of truth for the team

   weight 1 · round drawn
   Google Antigravity—none0/10

   Antigravity's 'Projects' concept groups folders/repos for a single agent session's context (docs-5, docs-17, docs-35, docs-38) and can surface Docs/Drive links (docs-32), but there is no evidence of a multi-user, team-shared workspace or collaborative source-of-truth that an engineering-lead could set up for a whole team — Projects appear to be individually scoped, local constructs rather than shared team assets.

   • [claimed-docs] “Group your conversations into Projects, which can span multiple folders and support custom settings and scoped permissions.”
   • [claimed-docs] “a project can work with one folder or multiple folders (e.g., a frontend and a backend repo), providing your agents with all of the context …”
   • [claimed-docs] “Agents work within Projects, which define the boundaries of the folders and repositories they can access.”
   • [claimed-docs] “Worktree support: Projects natively support Git worktrees, allowing agents to operate in isolated background folders.”
   • [claimed-docs] “External files such as Google Drive links, PDFs, and Office documents now appear in their own Documents section in the sidebar above Artifac…”

   proof ↗⚑ flag

   Slate—none0/10

   Slate is a CLI coding agent focused on individual sessions, workspaces (local directories), skills, and orchestration of subagents—there is no evidence of a shared team workspace or collaborative source-of-truth feature built from docs and repos. The 'workspace' concept here refers to local directory management (/workspace), not a shared team hub.

   • [claimed-docs] “Use `/workspace` to open the workspace manager, where you can review and remove workspace directories.”
   • [claimed-docs] “Slate by default respects agent rules in the following order”
   • [claimed-docs] “Skills are markdown instruction packages that give the agent domain-specific knowledge and behavior.”

   proof ↗⚑ flag

Tool integration

1. developerConnect the agent to workflow tools like Jira, Slack, and Google Drive to extend its context

   weight 3 · round to Google Antigravity
   Google Antigravity~partialclaimed5/10

   Antigravity documents generic MCP support for connecting to 'local developer tools, databases, file parsers, and external remote APIs' and explicitly shows Google Drive links/files appearing in its sidebar Documents section, giving a plausible path to hook in workflow tools. However, there is no explicit documentation of Jira or Slack connectors/integrations, and no first-party or community evidence of anyone actually wiring these specific tools in via MCP. Missing for 10: explicit Jira/Slack connector docs or MCP server examples, and independent confirmation of successful workflow-tool integrations beyond Drive.

   • [claimed-docs] “MCP lets Antigravity fetch structured context directly or execute safe actions on your behalf when needed.”
   • [claimed-docs] “lets AI agents and editors securely connect to local developer tools, databases, file parsers, and external remote APIs”
   • [claimed-docs] “External files such as Google Drive links, PDFs, and Office documents now appear in their own Documents section in the sidebar above Artifac…”
   • [claimed-docs] “Layer custom Python callables, Model Context Protocol (MCP) servers, and reusable agent skills over our built-in filesystem and terminal too…”

   proof ↗⚑ flag

   Slate—none0/10

   No evidence anywhere in the docs pack mentions integrations with Jira, Slack, Google Drive, or any workflow tools/MCP connectors; the docs focus on CLI usage, sessions, skills, and permissions with no mention of external tool connectivity.

   ⚑ flag

2. developerKick off agent tasks directly from GitHub, GitLab, Linear, or Slack

   weight 2 · round drawn
   Google Antigravity—none0/10

   No evidence that Antigravity supports kicking off agent tasks from GitHub, GitLab, Linear, or Slack; documentation covers IDE, CLI, SDK, scheduled tasks, and MCP but no mention of triggers from these external issue-tracker/chat platforms.

   ⚑ flag

   Slate—none0/10

   No evidence of any integration with GitHub, GitLab, Linear, or Slack for triggering agent tasks; Slate's documentation covers CLI usage, sessions, skills, and configuration but nothing about ecosystem/platform triggers.

   ⚑ flag

Cross device continuity

1. developerStart a task on one device and continue it later from another device or browser

   weight 2 · round to Google Antigravity
   Google Antigravity✓fullclaimed7/10

   Antigravity Remote Control explicitly lets users securely connect to and drive their desktop Antigravity sessions from any web browser, directly enabling continuing a task started on one device from another device/browser, and scheduled/background tasks further support async continuation across sessions. Missing for 10: independent hands-on verification of cross-device continuity, details on session/state sync fidelity, and any community confirmation of this specific feature working in practice.

   • [claimed-docs] “Antigravity Remote Control allows you to securely connect to and drive your Antigravity 2.0 desktop sessions running across your machines fr…”
   • [claimed-docs] “users can schedule messages to be sent to their agents while they’re away”
   • [claimed-docs] “Antigravity 2.0 serves as your AI agents’ central command center, providing a unified platform to launch, monitor, and orchestrate their act…”

   proof ↗⚑ flag

   Slate—none0/10

   Docs show session management within Slate (e.g. `/sessions` to switch sessions, `Ctrl+X N` for new session) but only describe local session switching, not any cloud sync or cross-device/browser continuation mechanism. Slate appears to be a terminal-only CLI tool with no mention of a browser interface or account-based sync for resuming tasks elsewhere.

   • [claimed-docs] “Use `/sessions` to switch between existing sessions”
   • [claimed-docs] “Ctrl+X then N New session”
   • [claimed-docs] “npm i -g @randomlabs/slate”

   proof ↗⚑ flag

Ide integration

1. developerChat with the coding assistant directly inside my IDE for contextual help

   weight 3 · round to Google Antigravity
   Google Antigravity✓fullcommunity7/10

   Antigravity is a VSCode-fork IDE with an editor view offering tab autocompletion, natural language code commands, and a context-aware conversational agent, confirmed by community hands-on reports of using it like Cursor. This directly supports in-IDE chat for contextual help. missing for 10: independent review specifically praising chat UX/quality (community notes mixed quality/performance complaints), and no detailed walkthrough of the chat interface itself beyond high-level docs.

   • [claimed-docs] “Google Antigravity's Editor view offers tab autocompletion, natural language code commands, and a configurable, and context-aware configurab…”
   • [claimed-docs] “offers tab autocompletion, natural language code commands, and a configurable, and context-aware configurable agent”
   • [community] “I went ahead and downloaded it, it looks to be a VSCode fork very similar to Cursor, with support for Gemini 3 Pro, Claude Sonnet 4.5, and G…”
   • [claimed-docs] “Users can select which reasoning model they want to use within the model selector drop-down under the conversation prompt box”

   proof ↗⚑ flag

   Slate~partialclaimed6/10

   Slate is documented as a terminal-based coding agent with session management, `@filename` references, shell execution, and workspace context — providing contextual chat help that developers can run alongside their editor in a terminal. However, there is no evidence of a native IDE extension/panel (e.g., VS Code/JetBrains plugin) that embeds Slate directly inside the IDE UI itself. missing for 10: dedicated IDE extension/panel integration, evidence of in-editor chat UI beyond terminal, independent corroboration of IDE workflow usage.

   • [claimed-docs] “Use `/sessions` to switch between existing sessions”
   • [claimed-docs] “Execute shell commands directly with `!`”
   • [claimed-docs] “Use `@filename` references”
   • [claimed-docs] “Use `/workspace` to open the workspace manager, where you can review and remove workspace directories.”
   • [claimed-docs] “Ctrl+X then N New session”

   proof ↗⚑ flag

Session management

1. developerReview diffs visually and run multiple sessions side by side in a desktop app

   weight 2 · round to Google Antigravity
   Google Antigravity✓fullcommunity8/10

   Antigravity's desktop app (confirmed as a VSCode-style editor) ships a dedicated 'Review Changes' diff viewer with whitespace filtering and syntax-highlighted artifacts (antigravity-docs-33, -34, -30), plus explicit support for running multiple agents/sessions in parallel across independent projects and worktrees from one command center (antigravity-docs-1, -37, -38, -10). Missing for 10: independent hands-on confirmation of the side-by-side multi-session UI specifically (community evidence mostly discusses general bugs/instability rather than this feature directly).

   • [claimed-docs] “Artifacts include rich markdown plans (Implementation Plans), code diffs, architecture diagrams, images, and browser recordings.”
   • [claimed-docs] “Added a "Hide Whitespace Changes" option to the Review Changes overflow menu and file diff viewers to filter out whitespace-only edits.”
   • [claimed-docs] “Code and data artifacts like SQL and JSONL files now open in a virtualized viewer with syntax highlighting and line numbers”
   • [claimed-docs] “Planning Mode: The agent plans thoroughly before executing tasks... produces structured implementation plans called Artifacts”
   • [claimed-docs] “The agent always halts and requests your explicit approval before proceeding with proposed changes.”
   • [claimed-docs] “Orchestrate multiple autonomous agents working in parallel across independent projects.”
   • [claimed-docs] “Antigravity 2.0 serves as your AI agents’ central command center, providing a unified platform to launch, monitor, and orchestrate their act…”
   • [claimed-docs] “Worktree support: Projects natively support Git worktrees, allowing agents to operate in isolated background folders.”
   • [community] “I went ahead and downloaded it, it looks to be a VSCode fork very similar to Cursor, with support for Gemini 3 Pro, Claude Sonnet 4.5, and G…”

   proof ↗⚑ flag

   Slate—none0/10

   Slate is documented as a terminal/CLI tool (npm install, terminal-background onboarding, hotkeys, `/sessions` switching, subagent grid) with no mention of a desktop GUI or visual diff review; session switching is terminal-based, not side-by-side desktop windows. Missing for 10: any evidence of a desktop application, a visual diff viewer, or GUI-based side-by-side session comparison.

   • [claimed-docs] “npm i -g @randomlabs/slate”
   • [claimed-docs] “Onboarding asks for your terminal background, multiline input preference, and model source: your ChatGPT/Codex subscription, SuperGrok subsc…”
   • [claimed-docs] “Use `/sessions` to switch between existing sessions”
   • [claimed-docs] “Those agents show up as a grid of inline subagent cards, one per agent.”
   • [claimed-docs] “Ctrl+X then N New session”

   proof ↗⚑ flag

2. engineering-leadManage multiple agent-driven coding sessions from one unified workspace

   weight 2 · round drawn
   Google Antigravity✓fullcommunity8/10

   Antigravity's docs describe a unified 'command center' (antigravity-docs-37) that lets a lead orchestrate multiple autonomous agents in parallel across projects (antigravity-docs-1, antigravity-docs-10), grouped into Projects spanning folders/repos with scoped permissions (antigravity-docs-5, antigravity-docs-17, antigravity-docs-35), plus worktree isolation (antigravity-docs-38), scheduled/background tasks (antigravity-docs-2, antigravity-docs-39), subagent delegation (antigravity-docs-18/19), and even remote browser-based control of running sessions (antigravity-docs-26). This directly matches the engineering-lead's need to manage many concurrent agent sessions from one place. Missing for 10: independent verification of managing many simultaneous sessions at scale, and community reports note real stability/reliability issues (antigravity-comm-6, antigravity-comm-8, antigravity-comm-9) that temper confidence though they don't specifically contradict the multi-session orchestration claim.

   • [claimed-docs] “Antigravity 2.0 serves as your AI agents’ central command center, providing a unified platform to launch, monitor, and orchestrate their act…”
   • [claimed-docs] “Orchestrate multiple autonomous agents working in parallel across independent projects.”
   • [claimed-docs] “Have multiple agents working in parallel, so larger tasks get tackled faster.”
   • [claimed-docs] “Group your conversations into Projects, which can span multiple folders and support custom settings and scoped permissions.”
   • [claimed-docs] “a project can work with one folder or multiple folders (e.g., a frontend and a backend repo), providing your agents with all of the context …”
   • [claimed-docs] “Worktree support: Projects natively support Git worktrees, allowing agents to operate in isolated background folders.”
   • [claimed-docs] “Automate routine checks with Scheduled Tasks, simply define a cron schedule and the agents start and run autonomously in the background.”
   • [claimed-docs] “Antigravity Remote Control allows you to securely connect to and drive your Antigravity 2.0 desktop sessions running across your machines fr…”
   • [community] “Google made its lack of interest in Antigravity IDE obvious from very early. Updates were few and far between and app-breaking bugs stuck ar…”
   • [community] “It's not even good, honestly. I was using it for couple weeks before dropping that 2 months ago. The model was not good and slow, the harnes…”

   proof ↗⚑ flag

   Slate✓fullclaimed8/10

   Docs describe first-class multi-session/multi-agent workspace features: `/sessions` to switch sessions, `/workspace` manager, new-session hotkey, and orchestration showing a grid of inline subagent cards while continuing to chat, queue tasks, or spin up more parallel agents — directly matching the engineering-lead's need to manage multiple concurrent agent sessions from one place. Missing for 10: independent/hands-on verification of this workspace at scale and any lead-specific team-management features beyond individual session switching.

   • [claimed-docs] “Use `/sessions` to switch between existing sessions”
   • [claimed-docs] “Use `/workspace` to open the workspace manager, where you can review and remove workspace directories.”
   • [claimed-docs] “Those agents show up as a grid of inline subagent cards, one per agent.”
   • [claimed-docs] “While one or more agents run in the background, you can keep talking with Slate: plan next steps, queue up additional tasks, or spin up more…”
   • [claimed-docs] “Ctrl+X then N New session”

   proof ↗⚑ flag

Terminal workflow

1. developerRun a coding agent locally from my terminal

   weight 3 · round drawn
   Google Antigravity✓fullprobed8/10

   Google Antigravity ships an official CLI product (antigravity-cli) with terminal-native features like slash commands, headless/non-interactive mode for scripting, sandboxing, vim-mode editing, and config management, explicitly designed to run agents locally from the terminal, and a community comment confirms using 'Antigravity CLI with vscode' works fine. Missing for 10: deeper independent hands-on reviews specifically of the CLI (most community feedback focuses on the IDE, not the terminal tool) and no third-party benchmarks of terminal performance/reliability.

   • [claimed-docs] “Edit, orchestrate, and build all in natural language. Tell your agents what you need, and they’ll work on getting it done.”
   • [claimed-docs] “Have multiple agents working in parallel, so larger tasks get tackled faster.”
   • [claimed-docs] “Navigate your entire workflow via standard terminal shortcuts: adjust permissions, themes, and preferences via /config and type /keybindings…”
   • [claimed-docs] “Access plugins, MCP, skills, and hooks configurations instantly via slash commands, quickly enhancing your workflow.”
   • [claimed-docs] “Run Antigravity CLI non-interactively to script agent tasks, integrate with CI pipelines, and capture machine-readable output.”
   • [claimed-docs] “Sensitive files like ~/.ssh and .env are blocked, anything not explicitly mounted is invisible inside the sandbox”
   • [claimed-docs] “Vim editor mode replaces the editing model in every multi-line input surface of the CLI”
   • [probe] “official CLI documented at https://antigravity.google/product/antigravity-cli”
   • [community] “I much prefer using Gemini CLI in combination with vscode. It works like a charm. Now, I'll do the same with Antigravity CLI and vscode. It …”

   proof ↗⚑ flag

   Slate✓fullprobed8/10

   Slate ships as a global npm CLI (`npm i -g @randomlabs/slate`) that runs interactively in the terminal, with documented terminal-native features like hotkeys, shell command execution (`!`), file references (`@filename`), session management (`/sessions`), and configuration via `slate.json` — all consistent with a locally-run terminal coding agent. Missing for 10: independent hands-on confirmation of local terminal usage (community evidence only discusses porting-quality skepticism, not terminal operation itself) and no evidence of offline/non-terminal fallback limitations.

   • [claimed-docs] “npm i -g @randomlabs/slate”
   • [claimed-docs] “Onboarding asks for your terminal background, multiline input preference, and model source: your ChatGPT/Codex subscription, SuperGrok subsc…”
   • [claimed-docs] “Use `/sessions` to switch between existing sessions”
   • [claimed-docs] “Press Tab to queue the current message so it runs after the current turn finishes.”
   • [claimed-docs] “Execute shell commands directly with `!`”
   • [claimed-docs] “Use `@filename` references”
   • [claimed-docs] “Ctrl+X then N New session”
   • [probe] “official CLI documented at https://docs.randomlabs.ai/en/getting-started/quickstart”

   proof ↗⚑ flag

2. developerRun the agent non-interactively in scripts for workflow automation

   weight 2 · round to Google Antigravity
   Google Antigravity✓fullclaimed7/10

   Official docs explicitly describe a headless mode: 'Run Antigravity CLI non-interactively to script agent tasks, integrate with CI pipelines, and capture machine-readable output,' directly matching the workflow-automation story. Missing for 10: independent/hands-on confirmation of headless CI usage and details on machine-readable output format/exit codes.

   • [claimed-docs] “Run Antigravity CLI non-interactively to script agent tasks, integrate with CI pipelines, and capture machine-readable output.”
   • [claimed-docs] “Edit, orchestrate, and build all in natural language. Tell your agents what you need, and they’ll work on getting it done.”
   • [claimed-docs] “Create or download fully customizable skills to further your agent’s autonomy and transform how you get work done.”

   proof ↗⚑ flag

   Slate—none0/10

   The evidence shows Slate is a CLI-based interactive agent (npm install, onboarding, in-session commands like /sessions, !, @filename) but nowhere documents a non-interactive/headless mode, flags for scripted execution, or CI/automation usage; --dangerously-skip-permissions bypasses prompts but is not shown as enabling scripted/non-interactive invocation. Missing for 10: documentation of a non-interactive/print/exec mode, exit-code or piping behavior, or any CI/scripting examples.

   • [claimed-docs] “npm i -g @randomlabs/slate”
   • [claimed-docs] “We support `--dangerously-skip-permissions` (alias: `--yolo`) to bypass permission prompts.”
   • [probe] “official CLI documented at https://docs.randomlabs.ai/en/getting-started/quickstart”

   proof ↗⚑ flag

1. ai-native userDo everything through the API that I can do in the UI

   weight 2 · round to Google Antigravity
   Google Antigravity~partialprobed4/10

   Antigravity offers an Agent SDK (Python programmable) and a headless/non-interactive CLI mode for scripting agent tasks, plugins, MCP, and hooks, suggesting substantial programmatic access to agent capabilities. However, there is no documented public REST/HTTP API or OpenAPI spec (probe explicitly found all openapi.json candidate paths 404'd), and no evidence that UI-only features like Remote Control, Editor tab-autocompletion, artifact review UI, or scheduled task UI are fully exposed via API parity. missing for 10: a documented public API/OpenAPI spec, confirmation that all UI features (remote control, artifact review, scheduling UI) have API equivalents, and independent verification of API-UI parity.

   • [claimed-docs] “The Agent SDK gives you the same tools, agent loop, and context management that power Google Antigravity, programmable in Python.”
   • [claimed-docs] “Run Antigravity CLI non-interactively to script agent tasks, integrate with CI pipelines, and capture machine-readable output.”
   • [claimed-docs] “Layer custom Python callables, Model Context Protocol (MCP) servers, and reusable agent skills over our built-in filesystem and terminal too…”
   • [probe] “PROBE openapi: all candidate paths 404 (https://antigravity.google/openapi.json, https://antigravity.google/swagger.json, https://antigravit…”

   proof ↗⚑ flag

   Slate—none0/10

   No evidence of any public API for Slate — the openapi.json/swagger.json probes returned 404s and no docs reference programmatic endpoints; Slate is documented purely as a CLI/terminal agent with slash-commands, hotkeys, and config files, not an API-driven product with UI/API parity.

   • [probe] “PROBE openapi: all candidate paths 404 (https://docs.randomlabs.ai/openapi.json, https://docs.randomlabs.ai/swagger.json, https://docs.rando…”
   • [probe] “official CLI documented at https://docs.randomlabs.ai/en/getting-started/quickstart”
   • [claimed-docs] “npm i -g @randomlabs/slate”

   proof ↗⚑ flag

2. ai-native userExport all of my data in open formats and leave

   weight 3 · round drawn
   Google Antigravity—none0/10

   No evidence in the pack mentions data export, open-format portability, or account/data deletion features for Antigravity; docs cover projects, agents, artifacts, and CLI but never data portability or export-and-leave capability.

   ⚑ flag

   Slate—none0/10

   No evidence in the docs or elsewhere describes any data export functionality, open-format export, or data portability mechanism for Slate. Sessions, workspace history, and configurations appear stored locally but no documented export/leave path is mentioned. Missing for 10: any documentation of export commands, data format specifications, or account/data portability guarantees.

   ⚑ flag

3. ai-native userRead the product's source under an open license

   weight 2 · round drawn
   Google Antigravity—none0/10

   No evidence of any open-source license or public source repository for Antigravity; it appears closed-source (VSCode fork distributed as binary download, third-party unofficial extensions noted). Nothing in the docs or community reports references source availability or a license.

   ⚑ flag

   Slate—none0/10

   No evidence of an open-source license or public source repository for Slate; the CLI is distributed via npm install with no mention of source availability. missing for 10: open-source license declaration, public source repo link, license file/OSS registry evidence.

   ⚑ flag

4. ai-native userSelf-host the core product

   weight 3 · round drawn
   Google Antigravity—none0/10

   No evidence anywhere in the pack indicates Antigravity can be self-hosted; it is described only as a downloadable desktop app/IDE/CLI/SDK connecting to Google's cloud-hosted models, with account/entitlement gating and TOS restrictions mentioned in community reports, but no self-hosted server or on-prem deployment option is documented.

   • [claimed-docs] “Visit antigravity.google/download to download Google Antigravity 2.0. Select your operating system below”
   • [claimed-docs] “Antigravity 2.0 serves as your AI agents’ central command center, providing a unified platform to launch, monitor, and orchestrate their act…”
   • [community] “don't buy Google AI subscription before you confirm you have 'Antigravity entitlement'... You can have verified account, bank card added, ac…”
   • [community] “Google Antigravity TOS: 3rd party usage can get Google account suspended. My friend got a ban by using Pi agent with Antigravity. They un-ba…”

   proof ↗⚑ flag

   Slate—none0/10

   No evidence anywhere in the docs of Slate being open-source or offering a self-hosted deployment option; it's installed via npm as a CLI that connects to model subscriptions/credits, implying a hosted/service model rather than self-hostable core infrastructure. Missing for 10: any mention of self-hosting instructions, open-source repo, or on-prem deployment option.

   • [claimed-docs] “npm i -g @randomlabs/slate”
   • [claimed-docs] “Onboarding asks for your terminal background, multiline input preference, and model source: your ChatGPT/Codex subscription, SuperGrok subsc…”

   proof ↗⚑ flag

Authentication

1. developerAuthenticate with an API key instead of an account login

   weight 2 · round drawn
   Google Antigravity—none0/10

   Evidence shows Antigravity requires a Google account/login and even ties usage to 'Antigravity entitlement' on that account (comm-19), with account-level bans possible (comm-17, comm-20); no docs or CLI reference mention an API-key authentication mode as an alternative to account login.

   • [community] “Google Antigravity TOS: 3rd party usage can get Google account suspended. My friend got a ban by using Pi agent with Antigravity. They un-ba…”
   • [community] “don't buy Google AI subscription before you confirm you have 'Antigravity entitlement'... You can have verified account, bank card added, ac…”
   • [community] “Banning the entire account rather than AI access is wildly user hostile... And then you get to fight the support bots and eventually go to t…”

   proof ↗⚑ flag

   Slate—none0/10

   No evidence pack mentions API key authentication as an alternative to account login; onboarding docs only describe choosing a model source (ChatGPT/Codex, SuperGrok, or Slate credits subscription), not API-key auth. No mention of an API key mechanism anywhere, and the openapi probe returned 404s, giving no indication of an API-key based auth path.

   • [claimed-docs] “Onboarding asks for your terminal background, multiline input preference, and model source: your ChatGPT/Codex subscription, SuperGrok subsc…”
   • [probe] “PROBE openapi: all candidate paths 404 (https://docs.randomlabs.ai/openapi.json, https://docs.randomlabs.ai/swagger.json, https://docs.rando…”

   proof ↗⚑ flag

2. engineering-leadAuthenticate through an enterprise identity or cloud platform for compliance and scalability

   weight 2 · round drawn
   Google Antigravity—none0/10

   No evidence of SSO/SAML/OIDC, Google Workspace/Cloud IAM enterprise login, or any enterprise identity federation for Antigravity; docs mention only Google account sign-in and entitlement issues, with community reports of account suspensions rather than enterprise auth support. Missing for 10: SSO/SAML/OIDC support, Google Cloud IAM or Workspace admin console integration, enterprise provisioning/SCIM documentation.

   • [community] “don't buy Google AI subscription before you confirm you have 'Antigravity entitlement'... You can have verified account, bank card added, ac…”
   • [community] “Banning the entire account rather than AI access is wildly user hostile... And then you get to fight the support bots and eventually go to t…”

   proof ↗⚑ flag

   Slate—none0/10

   No evidence of SSO/SAML/OIDC enterprise identity integration or cloud-platform authentication for compliance; onboarding only mentions choosing a model source (ChatGPT/Codex, SuperGrok, or Slate credits), not enterprise identity federation.

   • [claimed-docs] “Onboarding asks for your terminal background, multiline input preference, and model source: your ChatGPT/Codex subscription, SuperGrok subsc…”

   proof ↗⚑ flag

3. developerSign in with my existing product subscription plan to use the coding agent

   weight 2 · round to Slate
   Google Antigravity—none0/10

   No documentation describes signing in with an existing Google AI/Gemini subscription plan to unlock Antigravity access, and community reports directly state that users with an active AI Pro subscription still could not use even the free tier without a separate 'Antigravity entitlement.' missing for 10: any first-party docs describing subscription-based sign-in, evidence of successful subscription-linked access, and confirmation that paid Google AI plans map directly to Antigravity usage.

   • [community] “don't buy Google AI subscription before you confirm you have 'Antigravity entitlement'... You can have verified account, bank card added, ac…”
   • [community] “Google Antigravity TOS: 3rd party usage can get Google account suspended. My friend got a ban by using Pi agent with Antigravity. They un-ba…”
   • [community] “On the pricing page it says free individual plan with 'generous rate limits'. I gave it an HTML file and 2 minutes later got: 'Model quota l…”

   proof ↗⚑ flag

   Slate✓fullclaimed8/10

   Docs explicitly state onboarding lets you choose your model source as your existing ChatGPT/Codex subscription or SuperGrok subscription (in addition to Slate credits), directly matching the story of signing in with an existing subscription plan to use the agent. Missing for 10: independent/hands-on confirmation that subscription sign-in actually works end-to-end and any detail on limitations of that mode vs credits.

   • [claimed-docs] “Onboarding asks for your terminal background, multiline input preference, and model source: your ChatGPT/Codex subscription, SuperGrok subsc…”

   proof ↗⚑ flag

4. developerSign in with a personal account to get free-tier access without managing API keys

   weight 1 · round to Slate
   Google Antigravity!disputedcontradicted3/10

   Vendor pages advertise a free individual plan with no mention of API key management, implying sign-in-with-personal-account access, but community hands-on reports directly contradict the free-tier promise — one user got a 'Model quota limit exceeded' error within minutes despite the 'generous rate limits' claim, and another describes being locked out of even the free tier due to an 'Antigravity entitlement' gate despite having an active subscription. missing for 10: first-party documentation explicitly describing the personal-account sign-in flow and free-tier terms, and independent confirmation that free-tier access works reliably without unexpected quota/entitlement blocks.

   • [community] “On the pricing page it says free individual plan with 'generous rate limits'. I gave it an HTML file and 2 minutes later got: 'Model quota l…”
   • [community] “don't buy Google AI subscription before you confirm you have 'Antigravity entitlement'... You can have verified account, bank card added, ac…”
   • [claimed-docs] “Visit antigravity.google/download to download Google Antigravity 2.0. Select your operating system below”

   proof ↗⚑ flag

   Slate~partialclaimed4/10

   Docs show onboarding lets users choose a model source including an existing ChatGPT/Codex or SuperGrok subscription instead of managing API keys, implying account-based auth is supported, but there's no explicit mention of a free tier or of signing in with a personal Slate account for free credits without a paid subscription. Missing for 10: explicit free-tier account sign-in flow, confirmation that 'Slate credits' option requires no payment, and any account-based (not subscription-based) login mechanism.

   • [claimed-docs] “Onboarding asks for your terminal background, multiline input preference, and model source: your ChatGPT/Codex subscription, SuperGrok subsc…”

   proof ↗⚑ flag

Model choice

1. developerLet the tool automatically pick the best model for each task

   weight 1 · round to Slate
   Google Antigravity—none0/10

   Docs describe a manual model selector dropdown where users choose the reasoning model themselves (antigravity-docs-16), not an automatic 'best model per task' selection mechanism; no evidence anywhere of automatic model routing or task-based model optimization.

   • [claimed-docs] “Users can select which reasoning model they want to use within the model selector drop-down under the conversation prompt box”

   proof ↗⚑ flag

   Slate~partialclaimed6/10

   Docs explicitly claim Slate 'automatically selects the right model for the job' and also allow developers to set preferred default models per slot via `/models` or `slate.json`, suggesting a hybrid automatic+manual approach relevant to pricing/limits tradeoffs. However, there's no detail on the selection logic, cost-awareness, or independent verification that auto-selection actually optimizes for task/price. Missing for 10: independent hands-on confirmation of auto-selection quality, explanation of selection criteria (cost vs capability), and evidence of pricing-limit awareness in model choice.

   • [claimed-docs] “Slate automatically selects the right model for the job.”
   • [claimed-docs] “Set preferred default models for each slot with the `/models` dialog or `slate.json` under `models`.”

   proof ↗⚑ flag

2. developerChoose which underlying AI model powers my session from multiple providers

   weight 2 · round to Google Antigravity
   Google Antigravity✓fullcommunity8/10

   Docs explicitly describe a model selector dropdown for choosing reasoning models, and community hands-on evidence confirms multiple providers (Gemini 3 Pro, Claude Sonnet 4.5, GPT-OSS 120B) are selectable, not locked to Gemini only. Missing for 10: pricing/tier restrictions per model and independent benchmarking of model-switching quality across providers.

   • [claimed-docs] “Users can select which reasoning model they want to use within the model selector drop-down under the conversation prompt box”
   • [community] “I went ahead and downloaded it, it looks to be a VSCode fork very similar to Cursor, with support for Gemini 3 Pro, Claude Sonnet 4.5, and G…”
   • [community] “Nice to see that it's not locked to just Gemini models.”

   proof ↗⚑ flag

   Slate~partialclaimed6/10

   Docs confirm model source can be chosen at onboarding (ChatGPT/Codex, SuperGrok, or Slate credits) and that default models per 'slot' can be set via `/models` or slate.json, showing multi-provider flexibility. However, this is framed around subscription/credit sources rather than a clear list of many independent model providers, and there's no independent/hands-on verification of switching providers mid-session. missing for 10: independent corroboration of provider switching, a full list of supported model providers, and confirmation this works reliably in practice.

   • [claimed-docs] “Onboarding asks for your terminal background, multiline input preference, and model source: your ChatGPT/Codex subscription, SuperGrok subsc…”
   • [claimed-docs] “Set preferred default models for each slot with the `/models` dialog or `slate.json` under `models`.”

   proof ↗⚑ flag

1. ai-native userChoose where my data is stored (region/residency)

   weight 2 · round drawn
   Google Antigravity—none0/10

   No evidence pack item mentions data residency, region selection, or storage location controls; only a telemetry on/off toggle is documented, which does not address data residency. Missing for 10: any documentation of regional data storage options, residency guarantees, or enterprise data-location controls.

   ⚑ flag

   Slate—none0/10

   Slate is a CLI coding agent tool; the evidence pack contains no mention of data residency, region selection, or storage location controls. Missing for 10: any documentation of data residency options, regional storage configuration, or compliance controls.

   ⚑ flag

2. ai-native userPrevent my data from being used to train AI models

   weight 3 · round to Google Antigravity
   Google Antigravity~partialclaimed5/10

   Antigravity's settings docs mention a Telemetry toggle to enable/disable sharing interaction logs 'to improve models,' which is the only evidence addressing training-data opt-out; there's no further detail on scope, default state, or enterprise data-processing guarantees. Missing for 10: independent verification the toggle actually excludes data from training, clarity on default setting, and any enterprise/DPA-level documentation of data usage.

   • [claimed-docs] “toggle Telemetry (enable/disable sharing interaction logs to improve models)”

   proof ↗⚑ flag

   Slate—none0/10

   No evidence in the pack addresses data-training opt-out, privacy policy, or any control over model training use; the documentation covers CLI usage, orchestration, and skills but nothing about data privacy posture. Missing for 10: any privacy policy statement, opt-out settings, or data usage terms regarding AI training.

   ⚑ flag

3. ai-native userControl data retention and deletion

   weight 2 · round to Google Antigravity
   Google Antigravity~partialclaimed3/10

   Docs mention a Telemetry toggle to enable/disable sharing interaction logs, which is a privacy-related control, but there is no documented mechanism for viewing, exporting, or deleting stored data/history, nor any stated retention policy. Missing for 10: explicit data deletion controls, data export/retention policy documentation, and independent confirmation these settings work as described.

   • [claimed-docs] “toggle Telemetry (enable/disable sharing interaction logs to improve models)”

   proof ↗⚑ flag

   Slate—none0/10

   No evidence pack items mention data retention policies, deletion controls, or privacy settings for user data/sessions; docs cover workspace management and permissions but not data retention/deletion. Missing for 10: any documentation of data retention periods, deletion mechanisms, or export/erase controls.

   ⚑ flag

4. ai-native userOpt out of telemetry and usage tracking

   weight 2 · round to Google Antigravity
   Google Antigravity✓fullclaimed7/10

   Docs confirm a settings toggle to enable/disable telemetry ('sharing interaction logs to improve models'), giving users a direct opt-out. Missing for 10: independent/hands-on confirmation that the toggle fully stops all data collection, and no detail on what telemetry remains even when disabled.

   • [claimed-docs] “toggle Telemetry (enable/disable sharing interaction logs to improve models)”

   proof ↗⚑ flag

   Slate—none0/10

   No evidence pack item mentions telemetry, usage tracking, analytics, or an opt-out setting anywhere in Slate's docs or community coverage; the closest item (diagnostic attachment on bug reports) doesn't address general telemetry opt-out. Missing for 10: any mention of telemetry collection, a privacy policy, or a documented opt-out flag/setting.

   • [claimed-docs] “Slate automatically attaches relevant diagnostic information (OS, version, session context) to your report.”

   proof ↗⚑ flag

Data governance

1. engineering-leadOpt out of having my code and prompts used for AI model training

   weight 1 · round to Google Antigravity
   Google Antigravity~partialclaimed5/10

   Docs mention a Telemetry toggle to 'enable/disable sharing interaction logs to improve models,' which functions as an opt-out from data being used for model improvement, but there is no explicit documentation framing this as a training opt-out for enterprise/engineering-lead governance needs (e.g., no data-processing agreement, no distinction between prompts/code vs telemetry, no enterprise admin-level control). Missing for 10: explicit statement that code/prompts are excluded from training, org-wide/admin-level enforcement of the opt-out, and independent confirmation the toggle actually stops training use.

   • [claimed-docs] “toggle Telemetry (enable/disable sharing interaction logs to improve models)”

   proof ↗⚑ flag

   Slate—none0/10

   No evidence in the pack addresses data usage, training opt-out, or privacy policy for prompts/code submitted to Slate or its model providers.

   ⚑ flag

Pr review

1. developerHave the agent stage changes, write commit messages, create branches, and open pull requests

   weight 3 · round to Google Antigravity
   Google Antigravity~partialclaimed3/10

   Antigravity's agents can operate the terminal and natively support Git worktrees, which implies they could run git commands like staging, committing, and branching, but no documentation explicitly describes agent-driven commit message generation, branch creation, or PR opening (e.g., GitHub integration). Missing for 10: explicit docs on commit-message authoring, branch creation workflow, and pull-request creation/integration with GitHub/GitLab.

   • [claimed-docs] “Worktree support: Projects natively support Git worktrees, allowing agents to operate in isolated background folders.”
   • [claimed-docs] “Able to autonomously operate across your editor, terminal, and browser.”
   • [claimed-docs] “Build AI agents that autonomously read files, run commands, edit code, and more.”

   proof ↗⚑ flag

   Slate—none0/10

   No evidence in the pack mentions git operations like staging, committing, branching, or opening pull requests; documentation covers sessions, orchestration, skills, permissions, and CLI setup but not any git/PR workflow. Absence of evidence for this applicable capability means the verdict is none.

   ⚑ flag

2. developerInspect diffs and run checks to catch problems before merging

   weight 3 · round to Google Antigravity
   Google Antigravity~partialcommunity6/10

   Docs describe a Review Changes/diff viewer (with whitespace filtering, syntax highlighting) and Artifacts containing code diffs, plus mandatory human approval before changes are applied, and subagents/CI headless mode that can run tests. This covers diff inspection and pre-merge gating, but there's no dedicated 'run checks' feature (e.g., integrated linting/test-run summary) beyond subagent test delegation, and no independent hands-on confirmation that this workflow reliably catches problems — community reports instead highlight safety failures (accidental deletion, data exfiltration) that occurred despite review/approval mechanisms. Missing for 10: independent verification that diff review + checks actually catch bugs pre-merge, and a dedicated automated check/test-report feature beyond ad-hoc subagent delegation.

   • [claimed-docs] “Planning Mode: The agent plans thoroughly before executing tasks... produces structured implementation plans called Artifacts”
   • [claimed-docs] “The agent always halts and requests your explicit approval before proceeding with proposed changes.”
   • [claimed-docs] “Artifacts include rich markdown plans (Implementation Plans), code diffs, architecture diagrams, images, and browser recordings.”
   • [claimed-docs] “Added a "Hide Whitespace Changes" option to the Review Changes overflow menu and file diff viewers to filter out whitespace-only edits.”
   • [claimed-docs] “Code and data artifacts like SQL and JSONL files now open in a virtualized viewer with syntax highlighting and line numbers”
   • [claimed-docs] “an agent can delegate tasks—such as running tests or performing extensive codebase searches—to dedicated subagents.”
   • [claimed-docs] “Run Antigravity CLI non-interactively to script agent tasks, integrate with CI pipelines, and capture machine-readable output.”
   • [community] “Google Antigravity just deleted the contents of whole drive - came down to commanding a deletion of a 'directory with space in the name' wit…”
   • [community] “absolutely no sympathy for someone running Antigravity in Turbo mode (this is not the default and it clearly states that Antigravity auto-ex…”

   proof ↗⚑ flag

   Slate~partialclaimed3/10

   Slate's docs mention it can perform integration tests manually (random-labs-docs-16), implying some check-running capability, but there is no evidence of diff inspection, git diff review, PR-style change summaries, or pre-merge validation workflows. missing for 10: diff/change inspection UI or command, explicit pre-merge check/test running workflow, and any corroborating hands-on evidence of catching problems before merge.

   • [claimed-docs] “Slate is one of the few agents capable of performing integration tests manually.”

   proof ↗⚑ flag

Safe execution

1. engineering-leadControl which external tools and integrations the agent is allowed to access

   weight 2 · round to Slate
   Google Antigravity!disputedcontradicted5/10

   Antigravity docs describe granular controls—Deny/Ask/Allow permission lists, MCP server configuration, plugins bundling MCP servers, and sandboxing that blocks sensitive files—giving engineering leads levers to restrict tool/integration access (antigravity-docs-23, antigravity-docs-42, antigravity-docs-12, antigravity-docs-28, antigravity-docs-48). However, independent security reports document that these controls were bypassed in practice: Gemini accessed .env files despite being configured not to, and the default Allowlist shipped with webhook.site, which was used as a live exfiltration vector—directly contradicting the claim that admins can reliably restrict external access (antigravity-comm-11, antigravity-comm-12, antigravity-comm-13). Missing for 10: evidence of a fix/patch to these bypasses, and no first-party acknowledgment/remediation documentation confirming the control now holds as designed.

   • [claimed-docs] “Permissions are evaluated across three distinct access lists: Deny... Ask... Allow”
   • [claimed-docs] “Permissions are evaluated across three distinct access lists: Deny...Ask...Allow”
   • [claimed-docs] “Access plugins, MCP, skills, and hooks configurations instantly via slash commands, quickly enhancing your workflow.”
   • [claimed-docs] “Plugins are namespaced bundles that allow you to extend Antigravity’s capabilities by grouping skills, rules, MCP servers, and hooks into a …”
   • [claimed-docs] “Sensitive files like ~/.ssh and .env are blocked, anything not explicitly mounted is invisible inside the sandbox”
   • [community] “Google Antigravity exfiltrates data via indirect prompt injection attack: Gemini is not supposed to have access to .env files with default s…”
   • [community] “The default Allowlist provided with Antigravity includes 'webhook.site', which was used as an exfiltration vector for secrets.”
   • [community] “Antigravity was also vulnerable to the classic Markdown image exfiltration bug, reported a few days prior and flagged as 'intended behavior'…”

   proof ↗⚑ flag

   Slate~partialclaimed6/10

   Slate's configuration docs describe a permission system where each permission key maps to allow/ask/deny actions or fine-grained pattern objects, which supports controlling what tools/actions the agent can perform, and a `--yolo` flag exists to bypass these prompts entirely. However there's no explicit documentation of controlling specific external integrations (e.g., MCP servers, API connectors) or org/team-level lockdown for an engineering lead specifically. Missing for 10: explicit external-integration/MCP allowlist docs, engineering-lead/team-level enforcement (vs individual config), and independent verification that permission enforcement can't be trivially bypassed.

   • [claimed-docs] “Each permission key maps to an action ("allow", "ask", or "deny"), or a pattern object for fine-grained control.”
   • [claimed-docs] “We support `--dangerously-skip-permissions` (alias: `--yolo`) to bypass permission prompts.”

   proof ↗⚑ flag

2. engineering-leadHave the agent operate inside a sandbox when interacting with code, tools, and network resources

   weight 2 · round to Google Antigravity
   Google Antigravity!disputedcontradicted4/10

   Antigravity CLI docs describe a real sandbox mechanism (sensitive files like ~/.ssh and .env blocked, unmounted paths invisible) plus a permission allow/ask/deny system, suggesting sandboxed tool/network access is a documented feature. However, independent reports concretely contradict this: Gemini bypassed its own .env protection to exfiltrate secrets via indirect prompt injection using an allow-listed exfiltration endpoint, a known markdown-image exfiltration bug was dismissed as 'intended behavior,' and unrestrained terminal auto-execution led to a user's entire drive being deleted — showing the sandbox/permission boundary is not reliably enforced in practice. Missing for 10: consistent enforcement of sandbox boundaries against prompt-injection/exfiltration, first-party acknowledgment/fix of these incidents, and independent verification that the CLI's stated sandbox extends to the IDE agent's file/network access.

   • [claimed-docs] “Sensitive files like ~/.ssh and .env are blocked, anything not explicitly mounted is invisible inside the sandbox”
   • [claimed-docs] “Permissions are evaluated across three distinct access lists: Deny... Ask... Allow”
   • [claimed-docs] “MCP lets Antigravity fetch structured context directly or execute safe actions on your behalf when needed.”
   • [community] “Google Antigravity exfiltrates data via indirect prompt injection attack: Gemini is not supposed to have access to .env files with default s…”
   • [community] “The default Allowlist provided with Antigravity includes 'webhook.site', which was used as an exfiltration vector for secrets.”
   • [community] “Antigravity was also vulnerable to the classic Markdown image exfiltration bug, reported a few days prior and flagged as 'intended behavior'…”
   • [community] “Google Antigravity just deleted the contents of whole drive - came down to commanding a deletion of a 'directory with space in the name' wit…”
   • [community] “The most useful suggestion from the Reddit thread: turn off 'Terminal Command Auto Execution' via File > Preferences > Antigravity Settings …”
   • [community] “absolutely no sympathy for someone running Antigravity in Turbo mode (this is not the default and it clearly states that Antigravity auto-ex…”

   proof ↗⚑ flag

   Slate—none0/10

   The evidence shows a permission system (allow/ask/deny actions) and a --yolo flag to bypass prompts, but there is no mention of sandboxed execution, containerization, or network isolation for the agent's code/tool interactions. missing for 10: any documentation of sandbox/container execution, network isolation controls, or filesystem confinement mechanisms.

   • [claimed-docs] “Each permission key maps to an action ("allow", "ask", or "deny"), or a pattern object for fine-grained control.”
   • [claimed-docs] “We support `--dangerously-skip-permissions` (alias: `--yolo`) to bypass permission prompts.”

   proof ↗⚑ flag

Security checks

1. engineering-leadSee license and public-code matching references for AI-suggested code

   weight 1 · round drawn
   Google Antigravity—none0/10

   No evidence in the pack mentions license compliance checks, public-code/OSS matching, provenance detection, or any similar review-safety feature for AI-suggested code; the docs focus on agents, artifacts, permissions, and workflow tooling with no mention of license scanning.

   ⚑ flag

   Slate—none0/10

   No evidence anywhere in the pack mentions license compliance checks, public-code/OSS matching, or provenance references for AI-suggested code; the docs cover orchestration, skills, permissions, and CLI usage but nothing about code provenance/license scanning.

   ⚑ flag

2. developerGet contextual explanations and automatic fixes for security vulnerabilities

   weight 2 · round drawn
   Google Antigravity—none0/10

   No evidence that Antigravity provides security-vulnerability-specific explanations or automatic fixes; the docs describe general agentic coding, planning, and review features but never mention vulnerability scanning or security remediation. Community evidence instead highlights security *problems* in Antigravity itself (prompt injection exfiltration), not a vulnerability-fixing capability for users' code.

   ⚑ flag

   Slate—none0/10

   No evidence in the pack mentions security vulnerability detection, explanations, or automatic fixes; documentation covers session management, orchestration, skills, and configuration but nothing about security review or vulnerability remediation. Missing for 10: any mention of vulnerability scanning, security explanations, or auto-fix capability.

   ⚑ flag