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Found 87 Skills
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).
Optional skill. Reconstruct a human-review-preparation file from an existing pull request, merge request, branch diff, or commit range in a repository the user trusts. Use when the user wants retrospective understanding of already-implemented changes, AI-side assessment and recommendations, and an optional provider-specific sharing variant written to a local file when needed.
Performs AI-powered code review on Git changes using the `ocr` CLI from alibaba/open-code-review. Use when the user asks to review code, review a pull request, review staged/unstaged changes, review a commit, or compare branches for code quality issues. Produces line-level review comments and can automatically apply fixes when requested. With appropriate review rules, can detect various types of issues including bugs, security vulnerabilities, performance problems, and code quality concerns.
Generate conventional commit messages automatically. Use when user runs git commit, stages changes, or asks for commit message help. Analyzes git diff to create clear, descriptive conventional commit messages. Triggers on git commit, staged changes, commit message requests.
This skill should be used when the user asks to "review my changes", "review this code", "check my work", "what's wrong with my changes", "review before I push", "security review", "do a code review", or mentions reviewing, auditing, or analyzing local code changes before committing or opening a PR.
Validate changesets in openai-agents-js using LLM judgment against git diffs (including uncommitted local changes). Use when packages/ or .changeset/ are modified, or when verifying PR changeset compliance and bump level.
Refresh AI's understanding of code. Use this skill when the user mentions terms like "refresh", "re-understand", "refresh cache", "reload", etc. This skill compares all modified files, re-reads and understands the code that may have been modified by humans, ensuring that the AI's context is synchronized with the latest code state.
Remove AI-generated code slop from a branch. Use when cleaning up AI-generated code, removing unnecessary comments, defensive checks, or type casts. Checks diff against main and fixes style inconsistencies.
Generate appropriate commit messages based on Git diffs
Review git diffs, staged changes, and GitHub PRs. Change-focused analysis across seven pillars (Security, Performance, Architecture, Error Handling, Testing, Maintainability, Paranoia) with numeric scoring 1-10. Supports GitHub PR review, staged changes, and arbitrary diffs. Use when: reviewing a PR, reviewing staged changes, reviewing a diff, pre-commit review. Triggers: review PR, review my changes, review the diff, review staged, review-pr, check my changes.
Review a git diff or explicit file scope for reuse, code quality, efficiency, clarity, and standards issues, then optionally apply safe Codex-driven fixes. Use when the user asks to "simplify code", "review changed code", "check for code reuse", "review code quality", "review efficiency", "simplify changes", "clean up code", "refactor changes", or "run simplify".
Diff-aware AI browser testing — analyzes git changes, generates targeted test plans, and executes them via agent-browser. Reads git diff to determine what changed, maps changes to affected pages via route map, generates a test plan scoped to the diff, and runs it with pass/fail reporting. Use when testing UI changes, verifying PRs before merge, running regression checks on changed components, or validating that recent code changes don't break the user-facing experience.