code-reviewer
The code-reviewer skill provides automated code quality analysis for pull requests and fresh code reviews, evaluating correctness, maintainability, readability, regression risk, and test coverage gaps. Use it for general code review needs, but route security audits to security-reviewer, existing review comments to receiving-code-review, and test-first development to tdd-guide instead.
git clone --depth 1 https://github.com/foryourhealth111-pixel/Vibe-Skills /tmp/code-reviewer && cp -r /tmp/code-reviewer/bundled/skills/code-reviewer ~/.claude/skills/code-reviewerSKILL.md
# Code Reviewer Complete toolkit for code reviewer with modern tools and best practices. ## Routing Boundary Use `code-reviewer` when the user asks for a fresh review of code or a PR: - correctness and likely bugs - maintainability and readability - regression risk - test coverage gaps from a reviewer's point of view Do not let this skill own narrower problems: - `security-reviewer` owns OWASP, secret leak, auth bypass, injection, and dedicated security audit prompts. - `receiving-code-review` owns existing CodeRabbit/GitHub/human review comments. - `verification-before-completion` owns final evidence before claiming work is done. - `deslop` owns cleanup of AI-generated comments, redundant guards, and boilerplate. - `tdd-guide` owns test-first or RED -> GREEN -> REFACTOR development. ## Migrated Legacy Assets The legacy review wrapper has been absorbed into this direct route owner. - `references/python-style-guide.md` contains the retained Python naming, import, documentation, error-handling, and secret-handling guidance. - `scripts/check_style.py` is a lightweight stdin/string style checker for quick local review support. Use these assets as supporting material inside `code-reviewer`; do not route to the deleted wrapper skill. ## Quick Start ### Main Capabilities This skill provides three core capabilities through automated scripts: ```bash # 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:** ```bash 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:** ```bash 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:** ```bash 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 ```bash # Install dependencies npm install # or pip install -r requirements.txt # Configure environment cp .env.example .env ``` ### 2. Run Quality Checks ```bash # 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 - Flag obvious security risks during a general review. - Route dedicated OWASP, auth, secret, injection, or threat-model requests to `security-reviewer`. ### 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 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
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