发现 文档写作 相关 Skills，比较来源仓库和用途，查看适合你工作流的技能说明。
Use when reviewing plugin quality, auditing plugins, analyzing the marketplace, checking plugins against Anthropic standards, or evaluating plugin architecture - provides systematic analysis methodology with va...
Use when writing or changing tests, adding mocks, or tempted to add test-only methods to production code - prevents testing mock behavior, production pollution with test-only methods, and mocking without unders...
Use when unsure about command syntax, options, or behavior, or when the user asks to "check the man page", "look up the manual", or "what does [command] do". Consult system man pages for accurate, version-speci...
Use when making git commits directly (not via /commit:new). Ensures safe shell quoting with heredoc format. Triggers on creating commits, staging and committing, any git commit operation.
Use when Python code runs slowly, needs profiling, requires async/await patterns, or needs concurrent execution - covers profiling tools, optimization patterns, and asyncio; measure before optimizing (plugin:py...
Use when starting feature work that needs isolation from current workspace or before executing implementation plans - creates isolated git worktrees with smart directory selection and safety verification
Use when about to claim work is complete, fixed, or passing, before committing or creating PRs - requires running verification commands and confirming output before making any success claims; evidence before as...
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes - four-phase framework (root cause investigation, pattern analysis, hypothesis testing, implementation) that ensures u...
Use when creating or editing skills, before deployment, to verify they work under pressure and resist rationalization - applies RED-GREEN-REFACTOR cycle to process documentation by running baseline without skil...
Use when facing 3+ independent failures that can be investigated without shared state or dependencies - dispatches multiple Claude agents to investigate and fix independent problems concurrently
Use when partner provides a complete implementation plan to execute in controlled batches with review checkpoints - loads plan, reviews critically, executes tasks in batches, reports for review between batches
Use when requesting or receiving code reviews - covers dispatching review subagent, handling feedback with technical rigor, and avoiding performative agreement
Use when tests have race conditions, timing dependencies, or inconsistent pass/fail behavior - replaces arbitrary timeouts with condition polling to wait for actual state changes, eliminating flaky tests from t...
Use when executing implementation plans with independent tasks in the current session - dispatches fresh subagent for each task with code review between tasks, enabling fast iteration with quality gates
Use when writing pytest tests, creating fixtures, mocking dependencies, or testing async code - provides patterns that verify actual behavior with proper fixtures and parametrization; prevents testing mocks ins...
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...
Use when errors occur deep in execution and you need to trace back to find the original trigger - systematically traces bugs backward through call stack, adding instrumentation when needed, to identify source o...
Use when the user asks to "refactor shell script", "fix bash style", "apply Google style guide to shell", "review shell script style", "format bash script", or when writing, reviewing, or refactoring shell/bash...
Use when writing narrative memos, 6-pagers, 1-pagers, press releases, or PRFAQs in Amazon style. Applies Amazon's no-PowerPoint writing standards with data over adjectives, active voice, and the "so what" test.
Persistent memory system for AI agents with semantic, episodic, and procedural memory types. Use when users want to (1) remember facts, preferences, or context across sessions, (2) track interaction history and...
Create an interactive HTML viewer to visualize and verify parsed metadata against source documents.
Create API agents that wrap external HTTP services (n8n, LangGraph, CrewAI, OpenAI endpoints). Configure request/response transforms, webhook status tracking, A2A protocol compliance. CRITICAL: Request transfor...
Ensure code quality before commits. Run lint, format, test, build. Use npm scripts: npm run lint, npm run format, npm test, npm run build. CRITICAL: All tests must pass, no lint errors, code must be formatted b...
Create and manage N8N workflows in Orchestrator AI. Use Helper LLM pattern for all LLM calls, configure webhook status tracking, handle API responses. CRITICAL: All workflows using Helper LLM must include requi...
默认按已收录数据中的 GitHub 仓库 Stars 排序。Stars 属于来源仓库，同一仓库的多个技能可能共享相同数值；它反映仓库关注度，不代表单个技能的安装量或质量评分。