{
  "markdown": "# AI Skill Kit\n\nA curated collection of reusable, structured **AI skills** — each is a complete methodology + prompt + workflow that helps LLMs perform complex tasks more effectively.\n\nNot just prompt snippets. Each skill is a **battle-tested playbook** with clear structure, ready to plug into any LLM workflow.\n\nDesigned for:\n\n- Claude / ChatGPT / Gemini / Open-source LLMs\n- AI Agents & Copilot-style assistants\n- Developers and knowledge workers\n\n> **[中文版 README](README_CN.md)**\n\n---\n\n## What Makes This Different\n\n| | Prompt Collections | AI Skill Kit |\n|--|--|--|\n| Structure | One-liner prompts | Methodology + framework + examples |\n| Reusability | Copy-paste | Modular, plug into any workflow |\n| Quality control | Community contributed | Each skill tested and iterated |\n| Scope | Generic tasks | Domain-specific deep skills |\n\n---\n\n## Skill Categories\n\n```\nai-skill-kit/\n├── ai-engineering/     # RAG, Agent, Prompt, LLM debugging\n├── career/             # Interview, resume, capability profiling\n├── analysis/           # Decision making, product strategy, deep analysis\n├── learning/           # Structured learning paths, topic analysis\n├── creative/           # Writing, illustration, SVG generation\n├── development/        # Testing, evaluation, debugging\n└── workflow/           # Automation, statistics, weekly review\n```\n\n---\n\n## Skill Index\n\n### AI Engineering\n\n| Skill | Description |\n|------|------|\n| [rag-evaluator](ai-engineering/rag-evaluator) | Diagnose RAG pipeline issues across retrieval, generation, and consistency layers |\n| [prompt-optimizer](ai-engineering/prompt-optimizer) | Identify root causes of prompt failures and apply structured fixes |\n| [agent-designer](ai-engineering/agent-designer) | Design ReAct / Plan-Execute / Multi-Agent architectures with failure handling |\n| [llm-debugger](ai-engineering/llm-debugger) | Debug LLM apps in production: API errors, rate limits, token overflow, streaming |\n| [vector-db-guide](ai-engineering/vector-db-guide) | Vector DB selection and usage: Chroma, Milvus, pgvector, Qdrant compared |\n| [langchain-patterns](ai-engineering/langchain-patterns) | Core LangChain patterns: LCEL, RAG chain, memory, agents with real code |\n| [ai-solution-designer](ai-engineering/ai-solution-designer) | Design AI solutions with scenario evaluation, architecture, risk and ROI analysis |\n\n### Career\n\n| Skill | Description |\n|------|------|\n| [mock-interview](career/mock-interview) | Generate high-value interview questions with answer frameworks based on resume + JD |\n| [job-seeker-resume-cn](career/job-seeker-resume-cn) | Chinese resume optimization for general job market with platform algorithm strategies |\n| [ai-job-transition-resume](career/ai-job-transition-resume) | Resume optimization for AI engineer / AI pre-sales roles |\n| [ai-engineer-interviewer](career/ai-engineer-interviewer) | Conduct technical interviews for AI engineering positions with role-specific evaluation |\n| [interview-sparring](career/interview-sparring) | Diagnostic interview practice with dynamic weak-point identification and coaching |\n| [capability-miner](career/capability-miner) | Extract structured capability profiles from project memory and Git history |\n\n### Analysis\n\n| Skill | Description |\n|------|------|\n| [product-analysis](analysis/product-analysis) | Multi-dimensional product strategy analysis (user / solution / business / competition / risk) |\n| [decision-tree](analysis/decision-tree) | Structured decision making with explicit recommendations |\n| [deep-analysis](analysis/deep-analysis) | Multi-perspective Self-Debate: generate, critique, refine, loops until score >= 8 |\n\n### Learning\n\n| Skill | Description |\n|------|------|\n| [learn-anything](learning/learn-anything) | Structured learning path for any technology or skill |\n| [atdf-analyzer](learning/atdf-analyzer) | Systematically analyze AI topics using 8-dimensional ATDF framework |\n\n### Creative\n\n| Skill | Description |\n|------|------|\n| [article-illustrator](creative/article-illustrator) | SVG illustrations for articles and visual communication |\n| [article-writer](creative/article-writer) | Structured article generation from notes, outlines, or rough content |\n| [svg-generator](creative/svg-generator) | User-goal-first SVG generation workflow |\n| [svg-to-png](creative/svg-to-png) | Reliable SVG to PNG conversion with tool fallbacks |\n| [knowledge-viz](creative/knowledge-viz) | Knowledge Dynamization Engine — turn abstract concepts into interactive dark-themed HTML experiences |\n| [iceberg-knowledge-map](creative/iceberg-knowledge-map) | Render any knowledge domain as an interactive iceberg: linear main-line on the surface, layered deep-dives below, relation graph at the bottom |\n| [frontend-design](creative/frontend-design) | Distinctive, production-grade frontend interfaces that avoid generic AI aesthetics |\n\n### Development\n\n| Skill | Description |\n|------|------|\n| [add-tests](development/add-tests) | Add unit tests to existing code, identify testable functions, and ensure all tests pass |\n| [test-audit](development/test-audit) | Full-dimension test audit (React/Next.js): Unit/API/Component/Security/Integration/E2E coverage analysis and auto-generation |\n| [ok-testaudit-nuxt](development/ok-testaudit-nuxt) | Test-coverage audit for Nuxt 3 + Vitest + Playwright frontends — gap analysis and missing-test generation aligned to real Nuxt conventions |\n| [ok-testaudit-server](development/ok-testaudit-server) | Test-coverage audit for Spring Boot + Spring AI backends — multi-tenant isolation & LLM tool-chain gaps, JUnit/Mockito/Testcontainers/WireMock |\n| [ok-itest](development/ok-itest) | Generate executable integration-test specs, skeletons, and PR reports for Spring Boot + Spring AI (WireMock + Testcontainers + tenant isolation) |\n| [eval-debug](development/eval-debug) | Analyze interview JSONL files for model quality and program logic debugging |\n| [rag-eval](development/rag-eval) | Analyze RAG experimental results, interpret metrics, and identify anomalies |\n\n### Workflow\n\n| Skill | Description |\n|------|------|\n| [cc-stats](workflow/cc-stats) | Generate Claude Code usage statistics and productivity insights |\n| [claude-scheduler](workflow/claude-scheduler) | Schedule Claude Code tasks using macOS launchd for persistent background execution |\n| [retro](workflow/retro) | Analyze recent conversations to identify repeated operations for automation |\n| [weekly-output](workflow/weekly-output) | Synthesize weekly notes using BASB, Zettelkasten, T-shape, and GTD frameworks |\n| [weekly-report](workflow/weekly-report) | Single-page weekly status report PNG + Markdown for stakeholder reporting |\n\n---\n\n## Usage\n\nEach skill folder contains a `SKILL.md` with the complete prompt and methodology. To use a skill:\n\n1. Open the `SKILL.md` file in the skill folder\n2. Copy the prompt into your LLM conversation\n3. Follow the structured workflow\n\n---\n\n## License\n\nMIT\n",
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