Total 50,476 skills, Code Quality has 2287 skills
Showing 12 of 2287 skills
Remove AI-generated code slop from a branch. Use when cleaning up AI-generated code, removing unnecessary comments, defensive checks, or type casts. Checks diff against main and fixes style inconsistencies.
Apply appropriate design patterns (Singleton, Factory, Observer, Strategy, etc.) to solve architectural problems. Use when refactoring code architecture, implementing extensible systems, or following SOLID principles.
Code refactoring best practices based on Martin Fowler's catalog and Clean Code principles (formerly refactoring). This skill should be used when refactoring existing code, improving code structure, reducing complexity, eliminating code smells, or reviewing code for maintainability. Triggers on tasks involving extract method, rename, decompose conditional, reduce coupling, or improve readability.
Python 3.11+ performance optimization guidelines (formerly python-311). This skill should be used when writing, reviewing, or refactoring Python code to ensure optimal performance patterns. Triggers on tasks involving asyncio, data structures, memory management, concurrency, loops, strings, or Python idioms.
Set up Biome (default) or ESLint + Prettier, Vitest testing, and pre-commit hooks for any JavaScript/TypeScript project. Uses Bun as the package manager. Use this skill when initializing code quality tooling for a new project or adding linting to an existing one.
Canonical, cross-language clean code standard with stable rule IDs (CC-*). Use when writing/reviewing code, defining team standards, or mapping lint/CI findings to consistent CC-* rule citations.
Writes production-quality Pine Script v6 code following TradingView guidelines and best practices. Use when implementing indicators, strategies, or any Pine Script code. Triggers on requests to create, write, implement, or code Pine Script functionality.
Run a comprehensive code review
Use when Code implementation and refactoring, architecturing or designing systems, process and workflow improvements, error handling and validation. Provide tehniquest to avoid over-engineering and apply iterative improvements.
Use when reviewing code, pull requests, or diffs. Provides patterns, checklists, and templates for systematic code review with a focus on correctness, security, readability, performance, and maintainability.
Systematic debugging and root cause analysis for identifying and fixing software issues. Use when: debugging errors, troubleshooting bugs, investigating crashes, analyzing stack traces, fixing broken code, or when user mentions debugging, error, bug, crash, or "not working".
Error handling patterns for robust applications. Use when implementing try-catch blocks, error boundaries, custom errors, or logging. Covers async errors, React error boundaries, and API error responses.