Shapes turns action-first: action leads, steps numbered, state restated. Use for ADHD-friendly output. Do not use to trim tokens; use response-compression.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Evaluates API surface design, consistency, and exemplar alignment. Use when reviewing public API changes or before releasing a new API surface.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
pensive:code-refinement)pensive:architecture-review)Use this skill to review public API changes, design new surfaces, audit consistency, and validate documentation completeness. Run it before any API release to confirm alignment with project guidelines.
api-review:surface-inventoryapi-review:exemplar-researchapi-review:consistency-auditapi-review:docs-governanceapi-review:evidence-logapi-review:findings-verifiedCatalog all public APIs by language. Record stability levels, feature flags, and versioning metadata. Use tools like rg to find public symbols (e.g., pub in Rust or non-underscored def in Python). Confirm the working tree state with git status before starting.
Identify at least two high-quality API references for the relevant language, such as pandas, requests, or tokio. Document their patterns for namespacing, pagination, error handling, and structure to serve as a baseline for the audit.
Compare the project's API against the identified exemplar patterns. Analyze naming conventions, parameter ordering, return types, and error semantics. Identify duplication, leaky abstractions, missing feature gates, and documentation gaps.
Validate that documentation includes entry points, quickstarts, and a complete API reference. Verify that changelogs and migration notes are maintained. Check for SemVer compliance, stability promises, and clear deprecation timelines. Confirm that documentation is generated automatically using tools like rustdoc, Sphinx, or OpenAPI.
Record all executed commands and findings. Summarize the final recommendation as Approve, Approve with actions, or Block. Include specific action items with assigned owners and due dates.
Confirm consistent conventions and descriptive names that follow language-specific idioms.
Verify consistent ordering and ensure optional parameters have explicit defaults. Check that type annotations are complete.
Analyze return patterns for consistency. Confirm that error cases are documented and that pagination follows a uniform structure.
Verify that all public APIs include usage examples and that the changelog reflects current changes.
The final report must include a summary of the API surface, a numerical inventory of endpoints and public types, and an alignment analysis against researched exemplars. Document consistency issues and documentation gaps with precise file and line references. Conclude with a clear decision and a timed action plan.
Each issue must follow this structure:
[A1] Title
- Location: file.py:42
- Anchor: `verbatim source text at line 42`
- Issue: what is wrong | Fix: remediation | Evidence: [E1]
The Anchor is the exact source text at Location; it is what
citation_verifier.py re-reads to prove the finding is real.
Use imbue:proof-of-work for reproducible command capture and imbue:structured-output for formatting findings. Reference imbue:diff-analysis/modules/risk-assessment-framework when assessing breaking changes.
modules/surface-inventory.md for API cataloging patternsmodules/exemplar-research.md for researching API standardsmodules/consistency-audit.md for cross-API consistency checksapi-review:findings-verified)Write findings to .review/findings.json, run the citation verifier
(Skill(imbue:review-core) Step 5), and drop or label UNVERIFIED any
the verifier rejects.
Location + verbatim Anchor
confirmed by citation_verifier.py (exit 0), or unverified findings
were dropped or labeled UNVERIFIED.If the audit command is missing, verify that dependencies are installed and
accessible in the system PATH. Check file permissions if access errors
occur. Use the --verbose flag to inspect execution logs if the tool
behaves unexpectedly.
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Shapes turns action-first: action leads, steps numbered, state restated. Use for ADHD-friendly output. Do not use to trim tokens; use response-compression.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Inverts burden of proof for code additions. Use when reviewing PRs, planning refactors, or running unbloat to challenge every addition's necessity.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Tracks per-agent token usage and flags waste in parallel dispatch. Use when evaluating parallel agent efficiency or after a multi-agent run.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Coordinates Claude agent teams via filesystem protocol. Use when orchestrating parallel agents with task dependencies. Do not use for single-agent tasks.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Selects architecture paradigm via research before scaffolding. Use when architecture is undecided and the choice needs justification and documentation.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Generates a Mermaid architecture diagram showing high-level component relationships. Use when visualizing how plugins or modules fit together.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.