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 .env

2. Run Quality Checks

Use the analyzer script

python scripts/code_quality_checker.py .

Review recommendations

Apply fixes

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 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