- name
- code-reviewer
- description
- Comprehensive code review skill for TypeScript, JavaScript, Python, Swift, Kotlin, Go. Includes automated code analysis, best practice checking, security scanning, and review checklist generation. Use when reviewing pull requests, providing code feedback, identifying issues, or ensuring code quality standards.
Code Reviewer
Complete toolkit for code reviewer with modern tools and best practices.
Quick Start
Main Capabilities
This skill provides three core capabilities through automated scripts:
# Script 1: Pr Analyzer
python scripts/pr_analyzer.py [options]
# Script 2: Code Quality Checker
python scripts/code_quality_checker.py [options]
# Script 3: Review Report Generator
python scripts/review_report_generator.py [options]Core Capabilities
1. Pr Analyzer
Automated tool for pr analyzer tasks.
Features:
- Automated scaffolding
- Best practices built-in
- Configurable templates
- Quality checks
Usage:
python scripts/pr_analyzer.py <project-path> [options]2. Code Quality Checker
Comprehensive analysis and optimization tool.
Features:
- Deep analysis
- Performance metrics
- Recommendations
- Automated fixes
Usage:
python scripts/code_quality_checker.py <target-path> [--verbose]3. Review Report Generator
Advanced tooling for specialized tasks.
Features:
- Expert-level automation
- Custom configurations
- Integration ready
- Production-grade output
Usage:
python scripts/review_report_generator.py [arguments] [options]Reference Documentation
Code Review Checklist
Comprehensive guide available in references/code_review_checklist.md:
- Detailed patterns and practices
- Code examples
- Best practices
- Anti-patterns to avoid
- Real-world scenarios
Coding Standards
Complete workflow documentation in references/coding_standards.md:
- Step-by-step processes
- Optimization strategies
- Tool integrations
- Performance tuning
- Troubleshooting guide
Common Antipatterns
Technical reference guide in references/common_antipatterns.md:
- Technology stack details
- Configuration examples
- Integration patterns
- Security considerations
- Scalability guidelines
Tech Stack
Languages: TypeScript, JavaScript, Python, Go, Swift, Kotlin Frontend: React, Next.js, React Native, Flutter Backend: Node.js, Express, GraphQL, REST APIs Database: PostgreSQL, Prisma, NeonDB, Supabase DevOps: Docker, Kubernetes, Terraform, GitHub Actions, CircleCI Cloud: AWS, GCP, Azure
Development Workflow
1. Setup and Configuration
# Install dependencies
npm install
# or
pip install -r requirements.txt
# Configure environment
cp .env.example .env2. Run Quality Checks
# Use the analyzer script
python scripts/code_quality_checker.py .
# Review recommendations
# Apply fixes3. Implement Best Practices
Follow the patterns and practices documented in:
references/code_review_checklist.mdreferences/coding_standards.mdreferences/common_antipatterns.md
Best Practices Summary
Code Quality
- Follow established patterns
- Write comprehensive tests
- Document decisions
- Review regularly
Performance
- Measure before optimizing
- Use appropriate caching
- Optimize critical paths
- Monitor in production
Security
- Validate all inputs
- Use parameterized queries
- Implement proper authentication
- Keep dependencies updated
Maintainability
- Write clear code
- Use consistent naming
- Add helpful comments
- Keep it simple
Common Commands
# Development
npm run dev
npm run build
npm run test
npm run lint
# Analysis
python scripts/code_quality_checker.py .
python scripts/review_report_generator.py --analyze
# Deployment
docker build -t app:latest .
docker-compose up -d
kubectl apply -f k8s/Troubleshooting
Common Issues
Check the comprehensive troubleshooting section in references/common_antipatterns.md.
Getting Help
- Review reference documentation
- Check script output messages
- Consult tech stack documentation
- Review error logs
Resources
- Pattern Reference:
references/code_review_checklist.md - Workflow Guide:
references/coding_standards.md - Technical Guide:
references/common_antipatterns.md - Tool Scripts:
scripts/directory