PR Reviewer Skill

Conduct comprehensive, professional code reviews for GitHub Pull Requests using industry-standard criteria and automated tooling.

Table of Contents

Purpose

When to Use

Review Process Workflow

Reference Documentation

Scripts Reference

Best Practices

Quick Reference Commands

Tips for Effective Reviews

Resources

Purpose

This skill performs code reviews by:

Automating data collection

- Fetching all PR-related information (metadata, diff, comments, commits, issues)

Organizing review workspace

- Creating structured directory with all artifacts

Applying systematic criteria

- Reviewing against comprehensive quality checklist

Facilitating inline feedback

- Optionally adding comments directly to PR code

Ensuring completeness

- Checking functionality, security, testing, maintainability

When to Use

Activate this skill when:

A GitHub PR URL is provided with a review request

Receiving "review this PR" or "code review" requests

Checking PR quality before merging

Providing systematic feedback on proposed changes

GitHub PR review is mentioned in any context

Review Process Workflow

IMPORTANT

This skill uses a two-stage approval process . Nothing is posted to GitHub until explicit approval with /send or /send-decline . Overview Fetch PR data - Collect all information Generate review files - Create detailed, human, and inline comment files Review and edit - Examine files, make changes as needed (use /show ) Approve and post - Use /send (approve) or /send-decline (request changes) Step 1: Fetch PR Data Use fetch_pr_data.py to automatically collect all PR information: python scripts/fetch_pr_data.py < pr_url

[ --output-dir < dir

] [ --no-clone ] Actions performed: Parse PR URL to extract owner, repo, and PR number Create directory structure: /PRs/// Fetch PR metadata (title, author, state, branches, labels) Download PR diff and commit history Retrieve all PR comments and reviews Extract ticket references (JIRA, GitHub issues) Optionally clone source branch and generate git diff Example: python scripts/fetch_pr_data.py https://github.com/facebook/react/pull/28476

Custom output directory

python scripts/fetch_pr_data.py https://github.com/owner/repo/pull/123 --output-dir /tmp/reviews

Skip cloning (faster, no git diff)

python scripts/fetch_pr_data.py https://github.com/owner/repo/pull/123 --no-clone

Output structure:

/tmp/PRs///

├── metadata.json # PR metadata (title, author, branches)

├── diff.patch # PR diff from gh CLI

├── git_diff.patch # Git diff (if cloned)

├── comments.json # Review comments on code

├── commits.json # Commit history

├── related_issues.json # Linked GitHub issues

├── ticket_numbers.json # Extracted ticket references

├── SUMMARY.txt # Human-readable summary

└── source/ # Cloned repository (if not --no-clone)

Step 2: Analyze PR Data

After fetching, analyze collected data against review criteria:

Read

SUMMARY.txt

- High-level overview

Review

metadata.json

- PR context, labels, assignees

Examine

diff.patch

- Code changes

Check

comments.json

- Existing feedback

Review

commits.json

- Commit quality and messages

Check

related_issues.json

- Linked tickets/issues

Apply review criteria - Evaluate against comprehensive checklist

Use the Read tool to examine files:

Read /tmp/PRs///SUMMARY.txt

Read /tmp/PRs///metadata.json

Read /tmp/PRs///diff.patch

Step 3: Generate Review Files

CRITICAL

After analysis, use generate_review_files.py to create structured review documents: python scripts/generate_review_files.py < pr_review_dir

--findings < findings_json

[ --metadata < metadata_json

] Creates three files in pr_review_dir/pr/ : pr/review.md - Detailed internal review with emojis and line numbers pr/human.md - Clean review for posting (no emojis, em-dashes, line numbers) pr/inline.md - Proposed inline comments with code snippets Also creates slash commands in .claude/commands/ : /send - Post human.md and approve PR /send-decline - Post human.md and request changes /show - Open review directory in VS Code Findings JSON structure : { "summary" : "Overall assessment of the PR..." , "metadata" : { "repository" : "owner/repo" , "number" : 123 , "title" : "PR title" , "author" : "username" , "head_branch" : "feature" , "base_branch" : "main" } , "blockers" : [ { "category" : "Security" , "issue" : "SQL injection vulnerability" , "file" : "src/db/queries.py" , "line" : 45 , "details" : "Using string concatenation for SQL query" , "fix" : "Use parameterized queries" , "code_snippet" : "result = db.execute('SELECT * FROM users WHERE id = ' + user_id)" } ] , "important" : [ ... ] , "nits" : [ ... ] , "suggestions" : [ "Consider adding..." , "Future enhancement..." ] , "questions" : [ "Is this intended to..." , "Should we..." ] , "praise" : [ "Excellent test coverage" , "Clear documentation" ] , "inline_comments" : [ { "file" : "src/app.py" , "line" : 42 , "comment" : "Consider edge case handling for empty input" , "code_snippet" : "def process(data):\n return data.strip()" , "start_line" : 41 , "end_line" : 43 , "owner" : "owner" , "repo" : "repo" , "pr_number" : 123 } ] } Step 4: Review and Edit Files Use /show to open the review directory in VS Code. Actions available: Read pr/review.md - Detailed analysis Edit pr/human.md - Modify before posting Review pr/inline.md - Check proposed comments Adjust any content as needed NOTHING is posted until explicit approval in Step 5. Step 5: Approve and Post Post the review when ready: Option A: Approve the PR /send Posts pr/human.md as comment Approves the PR Confirms action Option B: Request Changes /send-decline Posts pr/human.md as comment Requests changes on the PR Confirms action Posting inline comments (optional, after /send or /send-decline): Review pr/inline.md and run the provided commands for specific code comments. Step 6: Apply Review Criteria Reference references/review_criteria.md for comprehensive checklist. Review against these categories: Category Key Questions Functionality Does code solve the problem? Bugs? Edge cases? Readability Clear code? Meaningful names? DRY? Style Follows linter rules? Consistent with codebase? Performance Efficient algorithms? Scalable? Security Vulnerabilities addressed? Secrets protected? Testing Tests exist? Cover happy paths and edge cases? PR Quality Focused scope? Clean commits? Clear description? Priority markers for findings: Blocker: Must be fixed before merge Important: Should be addressed Nit: Nice to have, optional Suggestion: Consider for future Question: Clarification needed Praise: Good work For detailed criteria: Read references/review_criteria.md Reference Documentation This skill includes comprehensive reference guides: Reference Purpose references/review_criteria.md Complete checklist covering functionality, security, testing, and more references/gh_cli_guide.md Quick reference for GitHub CLI commands references/scenarios.md Detailed workflows for common review scenarios references/troubleshooting.md Common issues and solutions Scripts Reference scripts/fetch_pr_data.py Automated PR data fetching and organization. python scripts/fetch_pr_data.py < pr_url

[ options ] Options: --output-dir DIR Base output directory ( default: /tmp ) --no-clone Skip cloning repository scripts/generate_review_files.py Generate structured review files from analysis findings. python scripts/generate_review_files.py < pr_review_dir

--findings < findings_json

[ --metadata < metadata_json

] Creates: pr/review.md - Detailed internal review pr/human.md - Clean review for posting pr/inline.md - Proposed inline comments with commands .claude/commands/send.md - Slash command to approve and post .claude/commands/send-decline.md - Slash command to request changes .claude/commands/show.md - Slash command to open in VS Code REVIEW_READY.txt - Summary of next steps scripts/add_inline_comment.py Add inline code review comments to specific lines in PR. python scripts/add_inline_comment.py < owner

< repo

< pr_number

< commit_id

< file_path

< line

"" [ options ] Options: --side RIGHT | LEFT Side of diff ( default: RIGHT ) --start-line N Starting line for multi-line comment --start-side RIGHT | LEFT Starting side for multi-line comment Best Practices Communication Frame feedback as suggestions, not criticism Explain why an issue matters, not just what is wrong Acknowledge excellent practices Prioritize blockers first, style issues last Review Efficiency Use scripts to automate data fetching and comment posting Reference review_criteria.md as checklist Focus: Critical issues > Important > Nice-to-have Review promptly (within 24 hours if possible) Inline Comments Reference exact lines and files Provide better alternatives Test inline comments on test PRs first Use sparingly to avoid overwhelming PR Size Handling Large PRs (>400 lines): Suggest splitting Review in logical chunks Focus on architecture for large changes For detailed scenarios: Read references/scenarios.md Quick Reference Commands

Fetch PR data

python scripts/fetch_pr_data.py https://github.com/owner/repo/pull/123

Add inline comment

python scripts/add_inline_comment.py owner repo 123 latest "src/app.py" 42 "Comment"

View PR in browser

gh pr view 123 --repo owner/repo --web

Check PR status

gh pr checks 123 --repo owner/repo

View existing comments

gh api /repos/owner/repo/pulls/123/comments --jq '.[] | {path, line, body}' Tips for Effective Reviews Start with context: Read PR description, linked issues, commit messages Understand intent: Identify the problem being solved Check tests first: Verify tests demonstrate the fix/feature Look for patterns: Repeated issues suggest architecture problems Consider alternatives: Evaluate simpler approaches Think about maintenance: Assess future modification ease Remember humans: Maintain kindness, respect, and constructive tone For troubleshooting: Read references/troubleshooting.md Resources Review Criteria : references/review_criteria.md gh CLI Guide : references/gh_cli_guide.md Scenarios : references/scenarios.md Troubleshooting : references/troubleshooting.md GitHub PR Review Docs : https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/reviewing-changes-in-pull-requests Google Engineering Practices : https://google.github.io/eng-practices/review/ OWASP Top 10 : https://owasp.org/www-project-top-ten/