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Found 1,079 Skills
Python development guidance with code quality standards, error handling, testing practices, and environment management. Use when writing, reviewing, or modifying Python code (.py files) or Jupyter notebooks (.ipynb files).
Deep Python code review of changed files using git diff analysis. Focuses on production quality, security vulnerabilities, performance bottlenecks, architectural issues, and subtle bugs in code changes. Analyzes correctness, efficiency, scalability, and production readiness of modifications. Use for pull request reviews, commit reviews, security audits of changes, and pre-deployment validation. Supports Django, Flask, FastAPI, pandas, and ML frameworks.
Predicts future bug hotspots by analyzing code complexity, churn, and historical defect patterns. Warns developers before a bug is even written.
Systematic code refactoring skill that transforms complex, hard-to-understand code into clear, well-documented, maintainable code while preserving correctness. Use when users request "readable", "maintainable", or "clean" code, during code reviews flagging comprehension issues, for legacy code modernization, or in educational/onboarding contexts. Applies structured refactoring patterns with validation.
Review pull requests. Use when user asks to "review a PR", "/review-pr", or wants to review a pull request.
Technical debt detection and remediation. Run at session end to find duplicated code, dead imports, security issues, and complexity hotspots. Triggers: 'find tech debt', 'scan for issues', 'check code quality', 'wrap up session', 'ready to commit', 'before merge', 'code review prep'. Always uses parallel subagents for fast analysis.
Implement pre-commit hooks and GitHub Actions for quality assurance. Use when asked to "setup CI/CD", "add pre-commit hooks", "create GitHub Actions", "setup quality gates", "automate testing", "add linting to CI", or any DevOps automation for code quality. Detects project type and configures appropriate tools.
Comprehensive repository analysis using Explore agents, web search, and Context7 to investigate codebase structure, technology stack, configuration, documentation quality, and provide actionable insights. Use this skill when asked to analyze, audit, investigate, or report on a repository or codebase. | Exploreエージェント、Web検索、Context7を用いた包括的なリポジトリ分析。コードベース構造、技術スタック、設定、ドキュメント品質を調査し、実用的な洞察を提供。リポジトリやコードベースの分析、監査、調査、レポート作成を依頼された場合に使用。
Acts as a Principal Software Engineer to review completed or in-progress work against project standards, tech stack choices, and the implementation plan. Use when the user wants a quality check on their code or before finalizing a track.
AI-powered systematic codebase analysis. Combines mechanical structure extraction with Claude's semantic understanding to produce documentation that captures not just WHAT code does, but WHY it exists and HOW it fits into the system. Includes pattern recognition, red flag detection, flow tracing, and quality assessment. Use for codebase analysis, documentation generation, architecture understanding, or code review.
Verify implementation matches change artifacts. Use when the user wants to validate that implementation is complete, correct, and coherent before archiving.
Use when reviewing a plan before implementation begins. Not for autonomous plan analysis — use plan-review agent instead. Challenges scope, walks through architecture/quality/tests/performance interactively with mandatory user checkpoints and opinionated recommendations.