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