Total 30,085 skills, Code Quality has 1594 skills
Showing 12 of 1594 skills
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes
Use when completing tasks, implementing major features, or before merging to verify work meets requirements
Use when about to claim work is complete, fixed, or passing, before committing or creating PRs - requires running verification commands and confirming output before making any success claims; evidence before assertions always
Use when receiving code review feedback, before implementing suggestions, especially if feedback seems unclear or technically questionable - requires technical rigor and verification, not performative agreement or blind implementation
Conduct thorough, constructive code reviews for quality and security. Use when reviewing pull requests, checking code quality, identifying bugs, or auditing security. Handles best practices, SOLID principles, security vulnerabilities, performance analysis, and testing coverage.
Profile and optimize Python code using cProfile, memory profilers, and performance best practices. Use when debugging slow Python code, optimizing bottlenecks, or improving application performance.
Simplify and refactor code while preserving behavior, improving clarity, and reducing complexity. Use when simplifying complex code, removing duplication, or applying design patterns. Handles Extract Method, DRY principle, SOLID principles, behavior validation, and refactoring patterns.
Optimize application performance for speed, efficiency, and scalability. Use when improving page load times, reducing bundle size, optimizing database queries, or fixing performance bottlenecks. Handles React optimization, lazy loading, caching, code splitting, and profiling.
Run vet immediately after ANY logical unit of code changes. Do not batch your changes, do not wait to be asked to run vet, make sure you are proactive.
Organize project files and folders for maintainability and scalability. Use when structuring new projects, refactoring folder structure, or establishing conventions. Handles project structure, naming conventions, and file organization best practices.
Systematically debug code issues using proven methodologies. Use when encountering errors, unexpected behavior, or performance problems. Handles error analysis, root cause identification, debugging strategies, and fix verification.
Detect patterns, anomalies, and trends in code and data. Use when identifying code smells, finding security vulnerabilities, or discovering recurring patterns. Handles regex patterns, AST analysis, and statistical anomaly detection.