test-quality-audit
Scan test files for anti-patterns including mesa-optimization, disabled tests, trivial assertions, and error swallowing
Découvrez les skills blockchain code quality, comparez leurs dépôts et consultez leurs instructions.
Scan test files for anti-patterns including mesa-optimization, disabled tests, trivial assertions, and error swallowing
Automated code validation for diff review, change hygiene, and red flag detection. Use when reviewing git diffs, PRs, or changed files for test disabling patterns, secrets, path portability issues, security fla...
Pre-merge security validation detecting secrets, user-specific paths, insecure SSH configurations, and security-weakening flags. Use before committing code/documentation, before creating PRs, or during QA valid...
Test quality validation detecting mesa-optimization, happy-path bias, vacuous assertions, and error-swallowing anti-patterns. Use when reviewing test files for quality issues, evaluating test meaningfulness, or...
Comprehensive code review skill for Wasp applications. Use when reviewing code, PRs, or architectural changes. Covers Wasp-specific patterns, import rules, auth checks, TDD compliance, multi-tenant permissions,...
Complete code quality workflow with ESLint, Prettier, TypeScript, Husky hooks, and type safety standards. Run before every commit/push.
Guide for writing ast-grep rules to perform structural code search and analysis. Use when users need to search codebases using Abstract Syntax Tree (AST) patterns, find specific code structures, or perform comp...
Automatically maintain repository cleanliness and organization by scanning for misplaced files, organizing loose scripts, and ensuring professional folder structure
Natural language wrapper for code review - automatically triggers /code-review when users express intent to review code, check for bugs, or validate code quality
Comprehensive checklist and best practices for effective code reviews that catch bugs and improve code quality
Formats and styles content for professional GitHub presentation. Use when the user asks to style files for GitHub, format documentation, improve markdown presentation, or prepare repository for publishing. Appl...
Reviews test coverage and suggests missing test cases for error paths, edge cases, and business logic. Activates when users write tests or implement new features.
Automatically generates and updates API documentation from code changes with multiple format support
Natural language wrapper for validation commands - automatically triggers /validate-and-fix when users request quality checks, linting, or auto-fixes
Request and process code review efficiently with a simple rubric and patch plan
Reviews Python code for PEP8 compliance, type hints, modern best practices (Python 3.9+), and code quality. Detects anti-patterns, validates documentation, and provides severity-based recommendations. Integrate...
Systematic code review guidelines and checklist. Use this skill when reviewing code, conducting pull request reviews, or establishing code quality standards. Provides comprehensive review criteria covering func...
Detects performance anti-patterns like N+1 queries, nested loops, large file operations, and inefficient algorithms. Suggests fast fixes before issues reach production.
Automatically choose the best output style (tables, bullets, YAML, HTML, concise) to improve scanability and save tokens
Proactively reviews error handling patterns and suggests improvements using Result types, proper error propagation, and idiomatic patterns. Activates when users write error handling code or use unwrap/expect.
OpenAI Codex CLIを使用したコードレビュー、分析、コードベースへの質問を実行する。使用場面: (1) コードレビュー依頼時、(2) コードベース全体の分析、(3) 実装に関する質問、(4) バグの調査、(5) リファクタリング提案、(6...
Evaluates if error handling is sufficient for new code - checks try-catch coverage, logging, user messages, retry logic. Focuses on external calls and user-facing code.
Validate pseudo-code completeness and identify gaps
Check if current changes meets the quality standards and satisfy the given code review comments (if already given).
Le classement utilise les étoiles du dépôt GitHub indexé. Plusieurs skills peuvent partager ce nombre : ce n’est ni un nombre d’installations ni une note de qualité.