Total 43,570 skills, Code Quality has 2034 skills
Showing 12 of 2034 skills
Review and refactor code in your project according to defined instructions
Plan a multi-file refactor with proper sequencing and rollback steps
Add educational comments to the file specified, or prompt asking for file to comment if one is not provided.
Refactoring using Extract Methods in Java Language
Refactor Django/Python code to improve maintainability, readability, and adherence to best practices. Transforms fat views into Clean Architecture with Use Cases and Services. Applies SOLID principles, Clean Code patterns, Python 3.12+ features like type parameter syntax and @override decorator, Django 5+ patterns like GeneratedField and async views. Fixes N+1 queries, extracts business logic from views, separates Read/Write serializers, and converts exception-based error handling to explicit return values. Use when refactoring Django code, applying Clean Architecture, or modernizing legacy Django projects.
Kotlin language guardrails, patterns, and best practices for AI-assisted development. Use when working with Kotlin files (.kt, .kts), build.gradle.kts, or when the user mentions Kotlin. Provides null safety patterns, coroutine guidelines, data class conventions, and testing standards specific to this project's coding standards.
17 principles of Unix software design, from Eric Raymond's *The Art of Unix Programming*. You can refer to these principles when carrying out software design.
Modern TypeScript patterns your AI agent should use. Strict mode, discriminated unions, satisfies operator, const assertions, and type-safe patterns for TypeScript 5.x.
TypeScript best practices and patterns for writing type-safe, maintainable code. Use when working with TypeScript files, configuring tsconfig, defining interfaces/types, implementing error handling, writing generics, or setting up type-safe communication patterns. Includes patterns for discriminated unions, type guards, utility types, and more.
Perform exhaustive code reviews using multi-agent analysis, ultra-thinking, and worktrees
Use this skill for mathematical code verification. Use when reviewing math-heavy code, verifying algorithm correctness, checking numerical stability, aligning with mathematical standards. Do not use when general algorithm review - use architecture-review. DO NOT use when: performance optimization - use parseltongue:python-performance.
Read open review comments and resolve them by making code fixes