Total 54,172 skills, Code Quality has 2444 skills
Showing 12 of 2444 skills
Create diagnostic logs with clear purpose, filterable identifiers, and stringified objects for easy browser console copying. Use when adding logging statements, debugging code, or creating diagnostic output.
Review a pull request or contribution deeply, explain it tutorial-style for a maintainer, and produce a polished report artifact such as HTML or Markdown. Use when asked to analyze a PR, explain a contributor's design decisions, compare it with similar systems, or prepare a merge recommendation.
Use when the user asks to review code, review changes, review a commit, review a PR, audit code quality, check for security issues, or generate a code review report. Trigger on phrases like "review my changes", "코드 리뷰", "check my code", "review the last commit", "what do you think of this diff", "compare branches", "code audit" — even if they don't say "code review" explicitly. For persistent file output use `code-review-md` (markdown) or `code-review-html` (markdown + HTML).
Stage 1 spec compliance review. Triggers: /review stage 1. Verifies implementation matches design specification — functional completeness, TDD compliance, and test coverage. Do NOT use for code quality checks — use quality-review instead. Do NOT use for debugging.
PR triage: audit open PRs, deep review selected ones, draft and post review comments. Args: "all" to review all, PR numbers to focus (e.g. "42 57"), "en"/"fr" for language, no arg = audit only in French.
Use when completing tasks, implementing major features, or before merging to verify work meets requirements
Use when reviewing code, pull requests, branches, diffs, or changed files for quality, correctness, security, performance, and style issues.
Adversarial code review that breaks the self-review monoculture. Use when you want a genuinely critical review of recent changes, before merging a PR, or when you suspect Claude is being too agreeable about code quality. Forces perspective shifts through hostile reviewer personas that catch blind spots the author's mental model shares with the reviewer.
Designs and refactors software codebases to be AI-friendly by aligning the filesystem with domain/feature boundaries, creating deep (greybox) modules with small public interfaces, enforcing import boundaries, and tightening tests/feedback loops. Use when the user asks to "make the codebase AI-ready", "reduce coupling", "introduce deep modules", "create module boundaries", "restructure folders by feature", "define service interfaces", or "plan a refactor + tests so AI agents can work safely".
Scans the project and configures checks and reviews for Agent Validator for requests such as "set up validator", "configure checks and reviews", or "initialize validator for this repo".
Handles commit flows by detecting changes, optionally running validator validation, and completing commits for requests such as "commit with validator", "run checks before commit", "run validator then commit", or "skip validator and commit".
Generate a single-file interactive HTML code-review artifact for a GitHub PR. Fetches the diff via the gh CLI, performs an honest severity-coded self-review, and renders an artifact with: collapsible per-file diffs with colored inline annotations, severity filter chips, per-finding checkboxes, and a "Create feedback prompt" modal that aggregates the checked items into a paste-ready follow-up prompt ending with "Please address this feedback. Address each individual item in its own conventional commit." Use this skill whenever the user wants to review a pull request visually, asks for an HTML or static review artifact, says "review PR", "review this PR", "build a PR review", wants color-coded findings, feedback aggregation, or a review file they can share — even if they don't explicitly say "HTML". Also trigger on "code review artifact", "interactive review", "feedback prompt for a PR", or when the user mentions reviewing a specific PR number.