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grade-ai-labs

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• 9 skills
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• 17 hours ago last updated

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   Write A Skill · grade-ai-labs

   Create new agent skills with proper structure, progressive disclosure, and bundled resources. Use when user wants to create, write, or build a new skill.
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   Write A Prd · grade-ai-labs

   Create a PRD through user interview, codebase exploration, and module design, then write it to a local markdown file. Use when user wants to write a PRD, create a product requirements document, or plan a new feature.
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   Find Features · grade-ai-labs bundle

   Discover feature areas in the current repository that are not yet documented under the agent docs `features/` tree (scaffolded by `setup-agentic-repository` — `agents-docs/features/` by default, or wherever `--docs-dir` put it), then create populated feature docs from the canonical template. Use whenever the user wants to find undocumented features, fill out `features/`, catch up on missing feature documentation, document feature X/Y/Z, or mentions "find features". This is the natural follow-up to `setup-agentic-repository`, which scaffolds the empty `features/` tree this skill populates.
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   Agentic Review · grade-ai-labs bundle

   Orchestrate repository-aware code review after `setup-agentic-repository` by deriving module specialists from `AGENTS.md` + `CONTEXT.md`/`CONTEXT-MAP.md`, generating canonical review artifacts, running deterministic policy automation checks, delegating scoped review tasks to subagents, recording specialist attestations, and returning deduplicated severity-ordered findings with fail-closed completion status. Use when the user wants PR review, local-diff review, orchestrated multi-agent review, module-wise code review, policy-aware convention checks, auditability of reviewer coverage, or asks to invoke `agentic-review` with agentic repository setup.
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   Document Features · grade-ai-labs bundle

   Populate `<docs-dir>/features/<slug>.md` for one, several, or every undocumented feature area by dispatching up to 10 parallel subagents — one per feature. The agent docs directory is discovered from `AGENTS.md` — typically `agents-docs/` (the `setup-agentic-repository` default) but may be elsewhere if `--docs-dir` was used. Use whenever the user wants to document features, fill out feature docs, write up specific features (e.g. "document auth and billing"), document all undocumented features, or follow up on `find-features` discovery. This is the natural sequel to `find-features` — that skill identifies what is missing, this skill writes the docs in parallel.
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   Ubiquitous Language · grade-ai-labs bundle

   Interview the user about the project's domain vocabulary one term at a time, resolving ambiguities and picking canonical terms, then append the resulting glossary into the appropriate `CONTEXT.md` (the bounded-context vocabulary file scaffolded by `setup-agentic-repository`). Use whenever the user wants to define domain terms, harden vocabulary, build a glossary, capture a ubiquitous language, fill in `CONTEXT.md`, or mentions "domain model", "DDD", "bounded context", or "vocabulary".
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   Review Policy Builder · grade-ai-labs bundle

   Build and maintain project-specific review policy for `agentic-review` by combining repository docs (`AGENTS.md`, `ENGINEERING.md`, `CONTEXT.md`/`CONTEXT-MAP.md`, ADRs), repository-mined conventions, and structured user input, then writing machine-usable policy files under `<docs-dir>/review/policies/`, including audit-governance metadata consumed by `agentic-review`. Use when the user wants architecture integrity checks (onion/clean/hexagonal), module-specific review rules, dependency-direction policy, naming/inheritance convention enforcement, stricter project/domain review standards, or explicit auditability requirements for specialist review coverage.
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   Setup Agentic Repository · grade-ai-labs bundle

   Scaffold the Mimas agent instruction file tree for any repository — AGENTS.md at root, subdomain CONTEXT.md files, and the full agents-docs/ hierarchy (a sibling of any existing docs/, kept separate so human-maintained project docs stay untouched). Every file is tailored to the repo's actual tech stack, git platform, and conventions. Use this skill whenever someone wants to set up agent instructions, onboard a repo for AI-assisted development, add AGENTS.md / CONTEXT.md files, create engineering docs for agents, or mentions "set up agentic repository" or "mimas template". Even if they just say "set up this repo for agents" or "add agent docs", this is the skill to use.
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   Review Policy Rule Builder · grade-ai-labs bundle

   Creates or updates `agentic-review` policy rules in `<docs-dir>/review/policies/global-policy.md` and `module-<slug>.md` using a structured interview and contract-safe merge behavior. Use when the user asks to add, modify, refine, deprecate, or clarify global or module review policy rules, or mentions rule severity, intent, evidence, automation metadata, or policy IDs like `RP-GLOBAL-###` and `RP-<MODULE>-###`.
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