review-pull-request
について
このClaudeスキルは、GitHub CLIを使用して包括的なGitHubプルリクエストレビューを実行します。差分の分析、コミット履歴の確認、CI/CDステータスの検証を行い、重大度レベル別のフィードバック(ブロック/提案/細かな指摘/称賛)を提供します。PRのレビュー担当としてアサインされた時、他者へのレビュー依頼前の自己レビュー時、またはマージ後の品質監査時にご利用ください。
クイックインストール
Claude Code
推奨npx skills add pjt222/agent-almanac -a claude-code/plugin add https://github.com/pjt222/agent-almanacgit clone https://github.com/pjt222/agent-almanac.git ~/.claude/skills/review-pull-requestこのコマンドをClaude Codeにコピー&ペーストしてスキルをインストールします
ドキュメント
Review Pull Request
Review GitHub pull request end-to-end — from understand the change through submit structured feedback. Uses gh CLI for all GitHub interactions and produces severity-leveled review comments.
When Use
- Pull request ready for review and assigned to you
- Performing second review after author addresses feedback
- Reviewing your own PR before requesting others' review (self-review)
- Auditing merged PR for post-merge quality assessment
- When you want structured review process rather than ad-hoc scanning
Inputs
- Required: PR identifier (number, URL, or
owner/repo#number) - Optional: Review focus (security, performance, correctness, style)
- Optional: Codebase familiarity level (familiar, somewhat, unfamiliar)
- Optional: Time budget for review (quick scan, standard, thorough)
Steps
Step 1: Understand Context
Read PR description and understand what change is trying to accomplish.
- Fetch PR metadata:
gh pr view <number> --json title,body,author,baseRefName,headRefName,labels,additions,deletions,changedFiles,reviewDecision - Read PR title and description:
- What problem does this PR solve?
- What approach did author take?
- Are there any specific areas author wants reviewed?
- Check PR size and assess time required:
PR Size Guide:
+--------+-----------+---------+-------------------------------------+
| Size | Files | Lines | Review Approach |
+--------+-----------+---------+-------------------------------------+
| Small | 1-5 | <100 | Read every line, quick review |
| Medium | 5-15 | 100-500 | Focus on logic changes, skim config |
| Large | 15-30 | 500- | Review by commit, focus on critical |
| | | 1000 | files, flag if should be split |
| XL | 30+ | 1000+ | Flag for splitting. Review only the |
| | | | most critical files. |
+--------+-----------+---------+-------------------------------------+
- Review commit history:
gh pr view <number> --json commits --jq '.commits[].messageHeadline'- Are commits logical and well-structured?
- Does history tell a story (each commit a coherent step)?
- Check CI/CD status:
gh pr checks <number>- All checks passing?
- Checks failing? Note which ones — affects review
Got: Clear understanding of what PR does, why exists, how big, whether CI green. This context shapes review approach.
If fail: PR description empty or unclear? Note this as first piece of feedback. PR without context = review antipattern. gh commands fail? Verify you authenticated (gh auth status) and have access to repository.
Step 2: Analyze the Diff
Read actual code changes systematically.
- Fetch full diff:
gh pr diff <number> - For small/medium PRs, read entire diff sequentially
- For large PRs, review by commit:
gh pr diff <number> --patch # full patch format - For each changed file, evaluate:
- Correctness: Does code do what PR says it does?
- Edge cases: Boundary conditions handled?
- Error handling: Errors caught and handled appropriately?
- Security: Any injection, auth, or data exposure risks?
- Performance: Any obvious O(n^2) loops, missing indexes, or memory issues?
- Naming: New variables/functions/classes named clearly?
- Tests: New behaviors covered by tests?
- Take notes as you read, classifying each observation by severity
Got: Set of observations covering correctness, security, performance, quality for every meaningful change in diff. Each observation has severity level.
If fail: Diff too large to review effectively? Flag it: "This PR changes {N} files and {M} lines. I recommend splitting it into smaller PRs for more effective review." Still review highest-risk files.
Step 3: Classify Feedback
Organize observations into severity levels.
- Classify each observation:
Feedback Severity Levels:
+-----------+------+----------------------------------------------------+
| Level | Icon | Description |
+-----------+------+----------------------------------------------------+
| Blocking | [B] | Must fix before merge. Bugs, security issues, |
| | | data loss risks, broken functionality. |
| Suggest | [S] | Should fix, but won't block merge. Better |
| | | approaches, missing edge cases, style issues that |
| | | affect maintainability. |
| Nit | [N] | Optional improvement. Style preferences, minor |
| | | naming suggestions, formatting. |
| Praise | [P] | Good work worth calling out. Clever solutions, |
| | | thorough testing, clean abstractions. |
+-----------+------+----------------------------------------------------+
- For each Blocking item, explain:
- What's wrong (specific issue)
- Why matters (the impact)
- How to fix (concrete suggestion)
- For each Suggest item, explain alternative and why it's better
- Keep Nits brief — one sentence is enough
- Include at least one Praise if anything positive stands out
Got: Sorted list of feedback items with clear severity levels. Blocking items have fix suggestions. Ratio should generally be: few Blocking, some Suggest, minimal Nit, at least one Praise.
If fail: Everything seems blocking? PR may need to be reworked rather than patched. Consider requesting changes at PR level rather than line-by-line comments. Nothing seems wrong? Say so — "LGTM" is valid feedback when code good.
Step 4: Write Review Comments
Compose review with structured, actionable feedback.
- Write review summary (top-level comment):
- One sentence: what PR does (confirm understanding)
- Overall assessment: approve, request changes, or comment
- Key items: list Blocking issues (if any) and top Suggest items
- Praise: call out good work
- Write inline comments for specific code locations:
# Post inline comments via gh API gh api repos/{owner}/{repo}/pulls/{number}/comments \ -f body="[B] This SQL query is vulnerable to injection. Use parameterized queries instead.\n\n\`\`\`suggestion\ndb.query('SELECT * FROM users WHERE id = $1', [userId])\n\`\`\`" \ -f commit_id="<sha>" \ -f path="src/users.js" \ -F line=42 \ -f side="RIGHT" - Format feedback consistently:
- Start each comment with severity tag:
[B],[S],[N], or[P] - Use GitHub suggestion blocks for concrete fixes
- Link to documentation for style/pattern suggestions
- Start each comment with severity tag:
- Submit review:
# Approve gh pr review <number> --approve --body "Review summary here" # Request changes (when blocking issues exist) gh pr review <number> --request-changes --body "Review summary here" # Comment only (when unsure or providing FYI feedback) gh pr review <number> --comment --body "Review summary here"
Got: Submitted review with clear, actionable feedback. Author knows exactly what to fix (Blocking), what to consider (Suggest), what went well (Praise).
If fail: gh pr review fails? Check permissions. You need write access to repo or to be requested reviewer. Inline comments fail? Fall back to putting all feedback in review body with file:line references.
Step 5: Follow Up
Track review resolution.
- After author responds or pushes updates:
gh pr view <number> --json reviewDecision,reviews - Re-review only changes that address your feedback:
gh pr diff <number> # check new commits - Verify Blocking items resolved before approving
- Resolve comment threads as issues are addressed
- Approve when all Blocking items fixed:
gh pr review <number> --approve --body "All blocking issues resolved. LGTM."
Got: Blocking issues verified as fixed. Review conversation resolved. PR approved or further changes requested with specific remaining items.
If fail: Author disagrees with feedback? Discuss in PR thread. Focus on impact (why matters) rather than authority. Disagreement persists on non-blocking items? Yield gracefully — author owns code.
Checks
- PR context understood (purpose, size, CI status)
- All changed files reviewed (or highest-risk files for XL PRs)
- Feedback classified by severity (Blocking/Suggest/Nit/Praise)
- Blocking items have specific fix suggestions
- At least one Praise included for positive aspects
- Review decision matches feedback (approve only if no Blocking items)
- Inline comments reference specific lines with severity tags
- CI/CD checks verified (green before approval)
- Follow-up completed after author revisions
Pitfalls
- Rubber-stamp: Approve without actually reading diff. Every approval = assertion of quality
- Nit avalanche: Drown author in style preferences. Save nits for mentoring situations; skip them in time-sensitive reviews
- Miss the forest: Review line-by-line without understand overall design. Read PR description and commit history first
- Block on style: Formatting and naming almost never blocking. Reserve Blocking for bugs, security, data integrity
- No praise: Only pointing out problems is demoralizing. Good code deserves recognition
- Review scope creep: Comment on code not changed in PR. Pre-existing issues bother you? File separate issue
See Also
review-software-architecture— System-level architecture review (complementary to PR-level review)security-audit-codebase— Deep security analysis for PRs with security-sensitive changescreate-pull-request— Other side of process: creating PRs easy to reviewcommit-changes— Clean commit history makes PR review significantly easier
GitHub リポジトリ
関連スキル
llamaguard
その他LlamaGuardは、暴力やヘイトスピーチなど6つの安全性カテゴリーにおいて、LLMの入力と出力をモデレートするMetaの70-80億パラメータモデルです。94〜95%の精度を提供し、vLLM、Hugging Face、Amazon SageMakerを使用してデプロイ可能です。このスキルを使用して、AIアプリケーションにコンテンツフィルタリングと安全策を簡単に統合できます。
cost-optimization
その他このClaudeスキルは、リソースの適正サイジング、タグ付け戦略、支出分析を通じて、開発者がクラウドコストを最適化することを支援します。AWS、Azure、GCPにわたるクラウド支出の削減とコストガバナンスの実施のためのフレームワークを提供します。インフラコストの分析、リソースの適正サイジング、または予算制約への対応が必要な際にご利用ください。
quantizing-models-bitsandbytes
その他このスキルは、bitsandbytesを使用してLLMを8ビットまたは4ビット精度に量子化し、精度の低下を最小限に抑えつつ50〜75%のメモリ削減を実現します。限られたGPUメモリでより大規模なモデルを実行したり、推論を高速化するのに理想的で、INT8、NF4、FP4などのフォーマットをサポートしています。HuggingFace Transformersと統合され、QLoRAトレーニングや8ビットオプティマイザーを可能にします。
dispatching-parallel-agents
その他このClaudeスキルは、複数のエージェントを配備し、3つ以上の独立した問題を並行して調査・修正します。共有状態や依存関係がなく解決可能な、無関係な障害が発生するシナリオ向けに設計されています。中核となる機能は並列問題解決であり、効率を最大化するために独立した問題領域ごとに1つのエージェントを割り当てます。
