Code Reviewer

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

Quick Start

bash

# 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:

bash

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:

bash

# 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:

bash

# 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

bash

# Install dependencies
npm install
# or
pip install -r requirements.txt

# Configure environment
cp .env.example .env

2. Run Quality Checks

bash

# 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

bash

# 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