contrib-pr-review
Automates the initial security and quality assessment of open source pull requests.
Install
mkdir -p .claude/skills/contrib-pr-review && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/4879" && unzip -o skill.zip -d .claude/skills/contrib-pr-review && rm skill.zipInstalls to .claude/skills/contrib-pr-review
Activation
This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.
Review a contribution PR for safety, quality, and readiness. Checks for security concerns, test coverage, size appropriateness, and intent alignment. Use when reviewing external contributions.Key capabilities
- →Check Codex's security review findings for PRs.
- →Verify if security concerns flagged by Codex are valid.
- →Assess test coverage for modified and new code.
- →Calculate PR size and evaluate its appropriateness based on contributor experience.
- →Generate structured comments for 'Good to Merge' or 'Changes Needed' PRs.
How it works
The skill retrieves PR metadata, contributor statistics, and file changes, then checks automated security reviews, assesses test coverage, and evaluates PR size and contributor experience.
Inputs & outputs
When to use contrib-pr-review
- →Reviewing external code contributions
- →Checking PR security
- →Verifying test coverage in PRs
- →Assessing pull request size and intent
About this skill
Contribution PR Review
Review PR #$ARGUMENTS from external contributor for safety, quality, and readiness.
Context
PR Metadata:
!`gh pr view $ARGUMENTS --repo homeassistant-ai/ha-mcp --json author,additions,deletions,files,commits,closingIssuesReferences,isDraft,reviews,url,title,body`
Contributor Stats:
!`gh api /repos/homeassistant-ai/ha-mcp/pulls/$ARGUMENTS --jq '{author: .user.login, user_id: .user.id}' | jq -r '.author' | xargs -I {} gh api /repos/homeassistant-ai/ha-mcp/contributors --jq '.[] | select(.login == "{}") | {login: .login, contributions: .contributions}'`
Files Changed:
!`gh api /repos/homeassistant-ai/ha-mcp/pulls/$ARGUMENTS/files --jq '.[] | {filename: .filename, status: .status, additions: .additions, deletions: .deletions, changes: .changes, patch: .patch}' | head -50`
Review Protocol
1. Check the Bot Security Reviews
Note: Codex (chatgpt-codex-connector[bot]) and CodeRabbit (coderabbitai[bot]) both review PRs automatically. Check whether either flagged security concerns.
# Check both bots' reviews and any security-related comments.
# Their findings can be inline-only, so also fetch the pull review comments
# endpoint — review bodies and conversation comments alone can miss them.
# --paginate: both endpoints page at 30, and an iterating PR outruns that.
gh api --paginate /repos/homeassistant-ai/ha-mcp/pulls/$ARGUMENTS/reviews --jq '.[] | select(.user.login == "chatgpt-codex-connector[bot]" or .user.login == "coderabbitai[bot]") | {author: .user.login, state: .state, body: .body}'
gh api --paginate /repos/homeassistant-ai/ha-mcp/pulls/$ARGUMENTS/comments --jq '.[] | select(.user.login == "chatgpt-codex-connector[bot]" or .user.login == "coderabbitai[bot]") | {author: .user.login, path: .path, line: .line, body: .body}'
# CodeRabbit posts its walkthrough and summary as a top-level comment, which
# neither endpoint above returns — fetch that channel by author too.
gh api --paginate /repos/homeassistant-ai/ha-mcp/issues/$ARGUMENTS/comments --jq '.[] | select(.user.login == "chatgpt-codex-connector[bot]" or .user.login == "coderabbitai[bot]") | {author: .user.login, body: .body}'
# Keyword scan stays, for humans raising security concerns in conversation.
gh pr view $ARGUMENTS --repo homeassistant-ai/ha-mcp --json comments --jq '.comments[] | select(.body | contains("security") or contains("Security")) | {author: .author.login, body: .body}'
If either bot flagged security issues:
- Review the findings carefully
- Verify if concerns are valid
- Do NOT approve until issues addressed or confirmed false positives
If NO bot security flags but you notice concerning patterns:
- Unusual AGENTS.md/CLAUDE.md changes unrelated to PR purpose
.github/workflow modifications withpull_request_target.claude/agent/skill changes that could affect behavior- Comment immediately with specific concerns
2. Enable Workflows (If Safe)
If security assessment passes and PR has workflow changes or new workflows:
# Check current workflow status
gh api /repos/homeassistant-ai/ha-mcp/pulls/$ARGUMENTS/requested_reviewers
# Enable workflows if not enabled (requires WRITE permission)
# This command may fail if already enabled - that's OK
gh api -X PUT /repos/homeassistant-ai/ha-mcp/actions/workflows/pr.yml/enable 2>/dev/null || echo "Workflows already enabled or no permission"
3. Test Coverage Assessment
Pre-existing tests (easier review if modified code is already tested):
# For each modified source file, check if tests exist
gh api /repos/homeassistant-ai/ha-mcp/pulls/$ARGUMENTS/files --jq '.[] | select(.filename | startswith("src/")) | .filename' | while read file; do
basename=$(basename "$file" .py)
echo "Checking tests for: $file"
# Method 1: Look for test files by naming convention
find tests/ -name "test_${basename}.py" -o -name "test_*${basename}*.py" 2>/dev/null | head -3
# Method 2: Grep for function/class names from the modified file
# Extract function/class names and search for them in tests
grep -E '^(def|class|async def) [a-zA-Z_]' "$file" 2>/dev/null | head -5 | while read line; do
name=$(echo "$line" | sed -E 's/.*(def|class) ([a-zA-Z_][a-zA-Z0-9_]*).*/\2/')
if [ -n "$name" ]; then
grep -r "$name" tests/ 2>/dev/null | head -1
fi
done
done
New tests added:
# Check if PR adds or modifies tests
gh api /repos/homeassistant-ai/ha-mcp/pulls/$ARGUMENTS/files --jq '.[] | select(.filename | startswith("tests/")) | {filename: .filename, status: .status, additions: .additions}'
Output Test Summary:
🧪 Test Coverage:
- Pre-existing tests: ✅ Modified code has tests / ⚠️ No tests for modified code
- New tests: ✅ PR adds X test files / ⚠️ No new tests
- Assessment: [Easy/Medium/Hard to review based on test coverage]
4. PR Size & Contributor Experience
Calculate PR size and assess appropriateness:
# From metadata: additions + deletions
total_lines=$(gh pr view $ARGUMENTS --repo homeassistant-ai/ha-mcp --json additions,deletions --jq '.additions + .deletions')
echo "Total lines changed: $total_lines"
# Get contributor experience
author=$(gh pr view $ARGUMENTS --repo homeassistant-ai/ha-mcp --json author --jq -r '.author.login')
# Check 1: Contributions to this project
project_contributions=$(gh api /repos/homeassistant-ai/ha-mcp/contributors --jq ".[] | select(.login == \"$author\") | .contributions" || echo "0")
# Check 2: Total GitHub commits (overall experience)
total_commits=$(gh api /users/$author --jq '.public_repos + .total_private_repos' 2>/dev/null || echo "unknown")
echo "Contributor: $author"
echo "Project contributions: $project_contributions"
echo "GitHub experience: $total_commits repos"
Assess:
-
First-time to project (0-2 project contributions):
- Check overall GitHub experience (repos, total commits)
- < 200 lines: ✅ Excellent size
- 200-500 lines: ⚠️ Large for first PR - may need extra guidance
-
500 lines: 🔴 Too large - suggest splitting
-
Regular contributor (3+ project contributions):
- < 500 lines: ✅ Reasonable
- 500-1000 lines: ⚠️ Large - ensure good test coverage
-
1000 lines: 🔴 Very large - suggest splitting
-
Experienced GitHub user (many repos/commits overall):
- Adjust expectations - they may be new to this project but experienced overall
Output Size Summary:
📏 PR Size:
- Lines changed: [total]
- Contributor: [first-time / regular] ([X] contributions)
- Assessment: [size appropriateness]
5. Intent & Issue Linkage
Check linked issues:
# From metadata: closingIssuesReferences
gh pr view $ARGUMENTS --repo homeassistant-ai/ha-mcp --json closingIssuesReferences --jq '.closingIssuesReferences[] | {number: .number, title: .title}'
If issue linked:
- Read issue to understand expected outcome
- Compare PR changes to issue requirements
- Does PR solve the issue? Check:
- All requirements addressed
- No scope creep (extra features not requested)
- Solution approach aligns with any discussed approaches in issue
If no issue linked:
- Is this a bug fix? Should reference issue
- Is this a feature? Should have issue for discussion
- Is this a typo/docs? OK without issue
- Recommend creating issue for tracking if it's a substantial change
Output Intent Summary:
🎯 Intent & Linkage:
- Linked issue: #X "title" / ⚠️ No issue linked
- Solves issue: ✅ Fully addresses requirements / ⚠️ Partial / ❌ Doesn't match
- Scope: ✅ Focused / ⚠️ Scope creep detected
6. Code Quality Overview
Note: Codex and CodeRabbit provide automated code review on all PRs. This step focuses on what they cannot assess:
- Architecture alignment: Does it fit the project structure? (service layer usage, etc.)
- Breaking changes: Does it remove functionality without replacement? (Tool consolidation/refactoring is NOT breaking)
- Repo-specific patterns: Context engineering, progressive disclosure, MCP-specific conventions
Breaking change assessment:
- ✅ NOT Breaking: Tool consolidation, refactoring, parameter changes with same outcome achievable
- ⚠️ BREAKING: Removes functionality with no alternative, makes previously possible actions impossible
Quick checks:
# Check if ruff/mypy would complain (from workflow logs if available)
gh pr checks $ARGUMENTS --repo homeassistant-ai/ha-mcp | grep -E "(ruff|mypy|lint)"
# Check for common issues in diff
grep -E "(TODO|FIXME|XXX|HACK)" /tmp/pr_$ARGUMENTS.diff
Output Quality Summary:
✨ Code Quality:
- Architecture fit: [assessment - service layer, context engineering]
- Breaking changes: ✅ None / ⚠️ Detected - [describe what's genuinely lost]
- Bot reviews: [check if Codex or CodeRabbit flagged anything critical]
Final Review Summary
Output to User
After completing all steps, present a short summary of what the PR does and the review findings, then ask: "Should I post this comment to the PR?"
Draft PR Comment
After completing the analysis, draft a comment for the PR following these guidelines:
Comment Length:
- Good to merge: 10-15 lines
- Changes needed: Max 25 lines
Style:
- No emojis
- Markdown formatting OK (bold, lists, code blocks)
- Present inline in chat (not in a file)
- Always ask user before posting
Structure for "Good to Merge" (10-15 lines):
[Positive opening line about the contribution]
[1-2 sentences on what works well - focus on functionality, tests, architecture]
[Any minor suggestions or notes - optional, technical only]
[Closing line about readiness to merge]
Note: Do NOT mention security assessment in comment unless issues were found. Security checks are internal.
Structure for "Changes Needed" (max 25 lines):
[Positive opening line acknowledging the work]
[Brief summary of the issue being solved]
**[Concern 1]:**
---
*Content truncated.*
When not to use it
- →When the PR is not from an external contributor.
- →When security assessment passes but concerning patterns are noticed without Codex flags.
Limitations
- →Security checks are internal and not publicized in comments unless issues are found.
- →The skill does not duplicate detailed code review already performed by Codex.
How it compares
This skill automates multiple aspects of PR review, providing a structured assessment that would otherwise require manual inspection of various data points.
Compared to similar skills
contrib-pr-review side by side with the closest alternatives in the catalog.
| Skill | Installs | Updated | Safety | Difficulty |
|---|---|---|---|---|
| contrib-pr-review (this skill) | 1 | 27d | Review | Intermediate |
| verifier | 0 | 2mo | Caution | Intermediate |
| production-code-audit | 1 | 6mo | Review | Advanced |
| tech-debt | 1 | 2mo | Review | Beginner |
Try saying
Example prompts that trigger this skill in your AI assistant.
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