Discover data ai automation tools skills, compare source repositories and read the instructions for each workflow.
Generate architecture overview documentation from codebase analysis. Uses tree-sitter-analyzer for precise code structure extraction. Use when documenting project structure, creating architecture diagrams, or w...
Generate environment setup guide documentation from codebase analysis. Detects package managers, required tools, environment variables, and startup commands. Use when creating setup guides, documenting environm...
Generate architecture overview documentation from codebase analysis. Uses tree-sitter-analyzer for precise code structure extraction. Triggers: architecture overview, project structure, module diagram.
Generate API specification from codebase - endpoints, types, schemas. Triggers: API docs, endpoint documentation, API仕様, OpenAPI, Swagger, REST API.
Storybook integration with spec.md. Auto-generates Stories from Component API. Triggers: storybook, stories, component api, props, argTypes, variants, csf3.
Bootstrap AI Developer Workflows (ADWs) infrastructure in any codebase. Use when user wants to: "set up ADWs", "bootstrap agentic workflows", "add AI developer workflows", "enable programmatic agent execution",...
Use when addressing PR review comments from Copilot, Claude, or human reviewers. Critically assesses each comment, recommends action (implement/push-back/discuss), and executes appropriately.
Perform a code review on recent changes
Manage git worktrees for parallel Claude Code development sessions
Jujutsu (jj) version control, load skill when hook output shows vcs=jj-colocated or vcs=jj in the system-reminder.
GitLab Flow workflow for implementing issues. Use when the user asks to work on a GitLab issue, fix an issue, implement an issue, or mentions an issue number to implement.
GitHub and Git workflow helpers; triggers on 'create pr', 'pr checks', 'retrigger tests', 'bump version', 'push'.
This skill should be used when users need to lint, format, or validate Python code using the Ruff command-line tool. Use this skill for tasks involving Python code quality checks, automatic code formatting, enf...
This skill should be used when users need to format, clean, lint, or validate Markdown files using the markdownlint-cli2 command-line tool. Use this skill for tasks involving Markdown file quality checks, autom...
CodeRabbit CLIを活用したコードレビューエージェント。コミット前チェック・ファイルレビュー・セキュリティフォーカス・自動修正を担当。ファイル依存関係を認識した深い分析、AI幻覚検出。Judgeを補完。
変数名改善、関数抽出、マジックナンバー定数化、デッドコード削除、コードレビュー。コードが読みにくい、リファクタリング、PRレビューが必要な時に使用。動作は変えない。
Sync dotfiles repository (homesick) with security checks. Use when: (1) Syncing dotfiles changes, (2) Committing dotfiles, (3) Pushing changes, (4) Running security checks, (5) After any file changes in ~/.clau...
Scan codebase to identify opportunities to replace custom implementations with well-maintained open source libraries. Creates worktree, implements changes, and submits PR for review. Multi-phase workflow with p...
Detect and track design token drift between Figma design systems and code implementations - report-only skill that identifies inconsistencies and creates wrangler issues for resolution
Update the wrangler plugin to the latest version by clearing all caching layers. Use when user says "update yourself", "update wrangler", or after pushing changes to the wrangler repo.
Capture user ideas verbatim in .wrangler/ideas/ directory using the MCP issues_create tool with type='idea'. Preserves exact user wording without interpretation or enhancement.
Clean up and organize markdown files dumped at project root. Routes files to appropriate directories (memos/, docs/, devops/docs/) or deletes obsolete content. Follows project file organization guidelines from...
Create distinctive, production-grade frontend interfaces with high design quality. Use this skill when the user asks to build web components, pages, or applications. Generates creative, polished code that avoid...
Validates project wrangler version on session start, detects breaking changes, and recommends updates
The default order uses GitHub repository stars from our indexed data. Stars belong to the source repository and may be shared by multiple skills. They indicate repository interest, not skill installs or an independent quality score.