Total 50,280 skills, Code Quality has 2282 skills
Showing 12 of 2282 skills
Analyze code coverage and CRAP (Change Risk Anti-Patterns) scores to identify high-risk code. Use OpenCover format with ReportGenerator for Risk Hotspots showing cyclomatic complexity and untested code paths.
Simplifies and refines code for clarity, consistency, and maintainability while preserving all functionality. Use when asked to "simplify code", "clean up code", "refactor for clarity", "improve readability", or review recently modified code for elegance. Focuses on project-specific best practices.
Review code for logging patterns and suggest evlog adoption. Detects console.log spam, unstructured errors, and missing context. Guides wide event design, structured error handling, request-scoped logging, and log draining with adapters (Axiom, OTLP).
Build complete call graphs with GrepAI trace. Use this skill for recursive dependency analysis.
CRITICAL: Use for type-driven design. Triggers: type state, PhantomData, newtype, marker trait, builder pattern, make invalid states unrepresentable, compile-time validation, sealed trait, ZST, 类型状态, 新类型模式, 类型驱动设计
CRITICAL: Use for performance optimization. Triggers: performance, optimization, benchmark, profiling, flamegraph, criterion, slow, fast, allocation, cache, SIMD, make it faster, 性能优化, 基准测试
Common Python anti-patterns to avoid. Use as a checklist when reviewing code, before finalizing implementations, or when debugging issues that might stem from known bad practices.
Execute `dart analyze` to identify warnings and errors, and use `dart fix --apply` to automatically resolve mechanical lint issues. Use during development to ensure code quality and before committing changes.
Debug experiment code with structured error analysis. Categorize errors, apply targeted fixes with retry logic, and use reflection to prevent recurring issues. Use when experiment code fails or produces incorrect results.
Linting and formatting for Megatron-LM. Covers running autoformat.sh, tools (ruff, black, isort, pylint, mypy), and code style rules.
PR quality checklist for ensuring comprehensive, well-documented pull requests. Use before submitting PRs to improve review efficiency and code quality.
Python error handling patterns including input validation, exception hierarchies, and partial failure handling. Use when implementing validation logic, designing exception strategies, handling batch processing failures, or building robust APIs.