Total 54,194 skills, Code Quality has 2444 skills
Showing 12 of 2444 skills
Simplifies and refines Python code for clarity, consistency, and maintainability while preserving all functionality. Applies dignified-python standards. Focuses on recently modified code unless instructed otherwise.
Comprehensive Python expertise covering language fundamentals, idiomatic patterns, software design principles, and production best practices. Use when writing, reviewing, debugging, or refactoring Python code. Triggers: Python, .py files, pip, uv, pytest, dataclasses, asyncio, type hints, or any Python library.
Pull request and code review with diff-based routing across five dimensions: code quality and guideline compliance, test coverage analysis, silent failure detection, type design and invariant analysis, and comment quality auditing. Classifies changed files and loads only relevant review methodologies. Produces severity-ranked findings (Critical, Important, Suggestion) with confidence scoring. Replaces pr-review-toolkit plugin. Trigger phrases: "review my PR", "review this code", "check my changes", "is this ready to merge", "audit this PR", "review before committing", "check code quality", "any issues with this code", "pre-merge review", "look over my changes", "code review". Use this skill when reviewing code before commit or merge, checking PR quality, or when the user asks for feedback on recent modifications.
Request peer review with proper context and preparation. Structures review requests with clear description of changes and testing status.
Use before claiming work is done, fixed, or passing — requires running verification commands and confirming output before any success claim. Prevents false completion claims, unverified assertions, and "should work" statements.
Run a structured, adversarial multi-agent bug review pipeline on a codebase. Use this skill whenever the user wants to find bugs, audit code quality, review a codebase for issues, or run any kind of bug-finding or code analysis workflow. Also trigger when the user asks to 'review my code for bugs', 'find all issues in this repo', 'audit this codebase', or any similar request. The pipeline uses three sequential phases: a Bug Finder that maximizes issue discovery, a Bug Adversary that challenges false positives, and an Arbiter that issues final verdicts — producing a clean, high-confidence bug report.
Comprehensive Python programming guidelines based on Google's Python Style Guide. Use when you needs to write Python code, review Python code for style issues, refactor Python code, or provide Python programming guidance. Covers language rules (imports, exceptions, type annotations), style rules (naming conventions, formatting, docstrings), and best practices for clean, maintainable Python code.
Use this skill for multi-model AI code review. Trigger whenever the user asks to review code changes, audit a diff, check code quality, review a PR, review commits, or review uncommitted changes before pushing or merging. Also trigger when they say 'code review', 'review my changes', 'check this before I merge', or want multiple perspectives on code. Runs Codex and Claude reviews in parallel, then synthesizes a unified report. Do NOT use for reviewing documentation, markdown, or non-code files, or for trivial single-line changes.
Automated code review for pull requests using multiple specialized agents
Biome linter and formatter for JavaScript/TypeScript. Covers configuration, rules, and integration patterns. Replaces ESLint + Prettier for faster development experience. USE WHEN: user mentions "biome", "linting", "formatting", "code style", "biome.json", asks about "setup linter", "format code", "migrate from ESLint", "migrate from Prettier", "biome rules", "biome configuration" DO NOT USE FOR: ESLint configuration - Biome is an ESLint replacement, Prettier configuration - Biome is a Prettier replacement, TypeScript compilation - use TypeScript compiler, Code quality principles - use `clean-code` skill
Evidence-based investigative code review using deductive reasoning to determine what actually happened versus what was claimed. Use when verifying implementation claims, investigating bugs, validating fixes, or conducting root cause analysis. Elementary approach to finding truth through systematic observation.
R package development with devtools, testthat, and roxygen2. Use when the user is working on an R package, running tests, writing documentation, or building package infrastructure.