code-reviewer

Code Reviewer

Complete toolkit for code reviewer with modern tools and best practices.

Quick Start

# 1. 정적 분석 (ktlint, eslint, flake8)
python script/code_review_analyzer.py --output .claude/review-report.json

# 2. PR 분석 (파일별 리스크, 리뷰 우선순위)
python script/pr_analyzer.py --pr 123 --output .claude/pr-analysis.json

# 3. 마크다운 리포트 생성
python script/review_report_generator.py --input .claude/review-report.json

Core Capabilities

1. Code Review Analyzer

정적 분석 도구(ktlint, eslint, flake8)를 통합하여 JSON 리포트를 생성합니다.

Features:

• 언어별 린터 자동 실행 (Kotlin: ktlint, TS/JS: eslint, Python: flake8)
• 심각도 분류 (error/warning/info)
• 자동 수정 가능 여부 표시 (auto_fixable)
• 파일별 이슈 집계 및 상위 문제 파일 식별

Usage:

python script/code_review_analyzer.py --output .claude/review-report.json

2. PR Analyzer

GitHub PR의 변경사항을 분석하여 리스크 수준과 리뷰 우선순위를 산정합니다.

Features:

• 파일별 리스크 평가 (critical/high/medium/low)
• 고위험 패턴 자동 식별 (보안, 설정, 인증 관련)
• 변경량 기반 리스크 가중치
• 리뷰 우선순위 자동 정렬

Usage:

# GitHub PR 분석
python script/pr_analyzer.py --pr 123

# 로컬 변경사항 분석 (git diff)
python script/pr_analyzer.py --base main

3. Review Report Generator

JSON 분석 결과를 마크다운 리뷰 리포트로 변환합니다.

Features:

• Critical/Warning/Suggestion 분류
• 파일:라인 형식으로 위치 표시
• 파일별 이슈 그룹핑
• GitHub PR 코멘트 형식 지원

Usage:

# JSON → 마크다운 변환
python script/review_report_generator.py -i .claude/review-report.json -o .claude/review-report.md

# stdout 출력
python script/review_report_generator.py --print

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 .env

2. Run Quality Checks

# 코드 리뷰 분석 실행
python script/code_review_analyzer.py --output .claude/review-report.json

# 리포트 확인 후 수정

3. Implement Best Practices

Follow the patterns and practices documented in:

• references/code_review_checklist.md
• references/coding_standards.md
• references/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 script/code_review_analyzer.py --output .claude/review-report.json

# 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: script/ directory