code-review-playbook
Use this skill when conducting or improving code reviews. Provides structured review processes, conventional comments patterns, language-specific checklists, and feedback templates. Use when reviewing PRs or standardizing review practices.
From orknpx claudepluginhub yonatangross/orchestkit --plugin orkThis skill is limited to using the following tools:
assets/pr-template.mdassets/review-feedback-template.mdchecklists/code-review-checklist.mdexamples/conventional-comments.mdexamples/pr-review-walkthrough.mdreferences/conventional-comments.mdreferences/review-patterns.mdrules/_sections.mdrules/_template.mdrules/linting-biome-rules.mdrules/linting-biome-setup.mdrules/python-quality.mdrules/security-baseline.mdrules/typescript-quality.mdscripts/fetch-pr-data.shscripts/review-pr.mdscripts/run-lint-check.shscripts/run-pr-checks.pytest-cases.jsonCode Review Playbook
This skill provides a comprehensive framework for effective code reviews that improve code quality, share knowledge, and foster collaboration. Whether you're a reviewer giving feedback or an author preparing code for review, this playbook ensures reviews are thorough, consistent, and constructive.
Overview
- Reviewing pull requests or merge requests
- Preparing code for review (self-review)
- Establishing code review standards for teams
- Training new developers on review best practices
- Resolving disagreements about code quality
- Improving review processes and efficiency
Code Review Philosophy
Purpose of Code Reviews
Code reviews serve multiple purposes:
- Quality Assurance: Catch bugs, logic errors, and edge cases
- Knowledge Sharing: Spread domain knowledge across the team
- Consistency: Ensure codebase follows conventions and patterns
- Mentorship: Help developers improve their skills
- Collective Ownership: Build shared responsibility for code
- Documentation: Create discussion history for future reference
Principles
Be Kind and Respectful:
- Review the code, not the person
- Assume positive intent
- Praise good solutions
- Frame feedback constructively
Be Specific and Actionable:
- Point to specific lines of code
- Explain why something should change
- Suggest concrete improvements
- Provide examples when helpful
Balance Speed with Thoroughness:
- Aim for timely feedback (< 24 hours)
- Don't rush critical reviews
- Use automation for routine checks
- Focus human review on logic and design
Distinguish Must-Fix from Nice-to-Have:
- Use conventional comments to indicate severity
- Block merges only for critical issues
- Allow authors to defer minor improvements
- Capture deferred work in follow-up tickets
Conventional Comments
issue [blocking]: Missing error handling for API call
If the API returns a 500 error, this will crash. Add try/catch.
security [blocking]: API endpoint is not authenticated
The /api/admin/users endpoint is missing auth middleware.
Load Read("${CLAUDE_SKILL_DIR}/references/conventional-comments.md") for the full format, labels (praise, nitpick, suggestion, issue, question, security, bug, breaking), decorations ([blocking], [non-blocking], [if-minor]), and examples.
Review Process
1. Before Reviewing
Check Context:
- Read the PR/MR description
- Understand the purpose and scope
- Review linked tickets or issues
- Check CI/CD pipeline status
Verify Automated Checks:
- Tests are passing
- Linting has no errors
- Type checking passes
- Code coverage meets targets
- No merge conflicts
Set Aside Time:
- Small PR (< 200 lines): 15-30 minutes
- Medium PR (200-500 lines): 30-60 minutes
- Large PR (> 500 lines): 1-2 hours (or ask to split)
2. During Review
Follow a Pattern:
-
High-Level Review (5-10 minutes)
- Read PR description and understand intent
- Skim all changed files to get overview
- Verify approach makes sense architecturally
- Check that changes align with stated purpose
-
Detailed Review (20-45 minutes)
- Line-by-line code review
- Check logic, edge cases, error handling
- Verify tests cover new code
- Look for security vulnerabilities
- Ensure code follows team conventions
-
Testing Considerations (5-10 minutes)
- Are tests comprehensive?
- Do tests test the right things?
- Are edge cases covered?
- Is test data realistic?
-
Documentation Check (5 minutes)
- Are complex sections commented?
- Is public API documented?
- Are breaking changes noted?
- Is README updated if needed?
3. After Reviewing
Provide Clear Decision:
- ✅ Approve: Code is ready to merge
- 💬 Comment: Feedback provided, no action required
- 🔄 Request Changes: Issues must be addressed before merge
Respond to Author:
- Answer questions promptly
- Re-review after changes made
- Approve when issues resolved
- Thank author for addressing feedback
Review Checklists
General Code Quality
- Readability: Code is easy to understand
- Naming: Variables and functions have clear, descriptive names
- Comments: Complex logic is explained
- Formatting: Code follows team style guide
- DRY: No unnecessary duplication
- SOLID Principles: Code follows SOLID where applicable
- Function Size: Functions are focused and < 50 lines
- Cyclomatic Complexity: Functions have complexity < 10
Security
- Authentication: Protected endpoints require auth
- Authorization: Users can only access their own data
- Input Sanitization: SQL injection, XSS prevented
- Secrets Management: No hardcoded credentials or API keys
- Encryption: Sensitive data encrypted at rest and in transit
- Rate Limiting: Endpoints protected from abuse
Quick Start Guide
For Reviewers:
- Read PR description and understand intent
- Check that automated checks pass
- Do high-level review (architecture, approach)
- Do detailed review (logic, edge cases, tests)
- Use conventional comments for clear communication
- Provide decision: Approve, Comment, or Request Changes
For Authors:
- Write clear PR description
- Perform self-review before requesting review
- Ensure all automated checks pass
- Keep PR focused and reasonably sized (< 400 lines)
- Respond to feedback promptly and respectfully
- Make requested changes or explain reasoning
Skill Version: 2.0.0 Last Updated: 2026-01-08 Maintained by: AI Agent Hub Team
Related Skills
ork:architecture-patterns- Enforce testing and architectural best practices during code reviewsecurity-scanning- Automated security checks to complement manual reviework:testing-unit- Unit testing patterns to verify during review
Rules
Each category has individual rule files in rules/ loaded on-demand:
| Category | Rule | Impact | Key Pattern |
|---|---|---|---|
| TypeScript Quality | rules/typescript-quality.md | HIGH | No any, Zod validation, exhaustive switches, React 19 |
| Python Quality | rules/python-quality.md | HIGH | Pydantic v2, ruff, mypy strict, async timeouts |
| Security Baseline | rules/security-baseline.md | CRITICAL | No secrets, auth on endpoints, input validation |
| Linting | rules/linting-biome-setup.md | HIGH | Biome setup, ESLint migration, gradual adoption |
| Linting | rules/linting-biome-rules.md | HIGH | Biome config, type-aware rules, CI integration |
Total: 5 rules across 4 categories
Available Scripts
-
scripts/review-pr.md- Dynamic PR review with auto-fetched GitHub data- Auto-fetches: PR title, author, state, changed files, diff stats, comments count
- Usage:
/ork:review-pr [PR-number] - Requires: GitHub CLI (
gh) - Uses
$ARGUMENTSand!commandfor live PR data
-
assets/review-feedback-template.md- Static review feedback template -
assets/pr-template.md- PR description template