global-commenting
Write self-documenting code with minimal, evergreen comments that explain complex logic without documenting changes or temporary fixes. Use this skill when adding comments to any code file. When deciding whethe...
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Write self-documenting code with minimal, evergreen comments that explain complex logic without documenting changes or temporary fixes. Use this skill when adding comments to any code file. When deciding whethe...
Describes the primary technologies, frameworks, libraries, and language conventions used in this codebase.
Validates documentation consistency and auto-generates documentation stubs. Auto-activates when users ask about docs sync, broken links, stale content, or when code changes need documentation. Checks version co...
Find most up-to-date documentation for relevant tools, libs, etc.
Expert on GigLedger's architecture, app structure, feature modules, and state management. Use when implementing features, creating new modules, or understanding the architectural patterns. References docs/07_ap...
Use when documenting Python research code usage, setup, and experiment reproduction.
コード変更、API変更、新機能追加時にドキュメントを自動更新します。公開APIやインターフェースの変更、新クラス・関数・モジュールの追加、アーキテクチャの重要な変更、ユーザーからの明示的なドキュメント更新依頼時に起動...
Update IBL5 documentation during pull requests and feature development. Use when updating docs, creating README files, or documenting completed work.
Guide for creating and updating README files following autonomous-bits development standards. Use this when asked to create or update README files, ensure documentation completeness, or review README quality.
Expert Python development guidance for this codebase. Use when writing Python code, implementing features, or following project coding standards and patterns.
Create comprehensive spec with diagrams, flows, and thorough documentation. Use when: detailed spec, comprehensive spec, full spec, baldrick spec. Triggers on: detailed, comprehensive, full spec, thorough.
Generate PR documentation from completed spec and git diff. Use when: pr docs, generate docs, pull request docs, baldrick docs. Triggers on: pr docs, generate docs, document changes.
Token-efficient library documentation fetcher using Context7 MCP with 86.8% token savings through intelligent shell pipeline filtering. Fetches code examples, API references, and best practices for JavaScript,...
Document the reasoning behind implementation decisions as markdown files. Creates decision records that future developers can reference.
Best practices for creating clear, comprehensive, and maintainable documentation for code, APIs, and systems
Core development standards, patterns, and best practices for React and React Native projects. Use this when users need guidance on code quality (Biome, TypeScript), testing (Jest), project structure (Domain-Dri...
Adds OpenAPI standards (Swagger) documentation to .NET ASP.NET Core APIs with Scalar UI or Swagger UI. Configures Swashbuckle, generates OpenAPI specs, and sets up interactive API documentation endpoints.
Use when asked about library/framework APIs, version-specific features, or when user mentions outdated docs. Requires Context7 MCP server.
Generate comprehensive API documentation from code. Use when user wants to create API docs, document endpoints, or needs OpenAPI specs.
Fetches up-to-date, version-aware library documentation from the Context7 MCP server before working on a project.
Search technical documentation using executable scripts to detect query type, fetch from llms.txt sources (context7.com), and analyze results. Use when user needs: (1) Topic-specific documentation (features/com...
Advanced code search tool that finds definitions, usages, tests, and references across the entire codebase. Use when you need to understand how a symbol is used throughout the project.
ライブラリやフレームワークのドキュメントが必要な場合に自動発動。Context7 MCPを使用して最新の公式ドキュメントを取得する。
Use when design is complete and you need detailed implementation tasks for engineers with zero codebase context - creates comprehensive implementation plans with exact file paths, complete code examples, and ve...
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.