Use up-to-date library and framework docs via Context7 MCP instead of training data. Activates for setup questions, API references, code examples, or when the user names a framework (e.g. React, Next.js, Prisma).
npx skills add https://github.com/loulanyue/awesome-claude-notes --skill documentation-lookup
When the user asks about libraries, frameworks, or APIs, fetch current documentation via the Context7 MCP (tools resolve-library-id and query-docs) instead of relying on training data.
/vercel/next.js) from a library name and query.Activate when the user:
Use this skill whenever the request depends on accurate, up-to-date behavior of a library, framework, or API. Applies across harnesses that have the Context7 MCP configured (e.g. Claude Code, Cursor, Codex).
Call the resolve-library-id MCP tool with:
Next.js, Prisma, Supabase).You must obtain a Context7-compatible library ID (format /org/project or /org/project/version) before querying docs. Do not call query-docs without a valid library ID from this step.
From the resolution results, choose one result using:
/org/project/v1.2.0).Call the query-docs MCP tool with:
/vercel/next.js).Limit: do not call query-docs (or resolve-library-id) more than 3 times per question. If the answer is unclear after 3 calls, state the uncertainty and use the best information you have rather than guessing.
libraryName: "Next.js", query: "How do I set up Next.js middleware?"./vercel/next.js) by name and benchmark score.libraryId: "/vercel/next.js", query: "How do I set up Next.js middleware?".middleware.ts example from the docs if relevant.libraryName: "Prisma", query: "How do I query with relations?"./prisma/prisma).libraryId and the query.include or select) with a short code snippet from the docs.libraryName: "Supabase", query: "What are the auth methods?".Convert PyTorch AT_DISPATCH macros to AT_DISPATCH_V2 format in ATen C++ code. Use when porting AT_DISPATCH_ALL_TYPES_AND*, AT_DISPATCH_FLOATING_TYPES*, or other dispatch macros to the new v2 API. For ATen kernel files, CUDA kernels, and native operator implementations.
Write docstrings for PyTorch functions and methods following PyTorch conventions. Use when writing or updating docstrings in PyTorch code.
Statistical models library for Python. Use when you need specific model classes (OLS, GLM, mixed models, ARIMA) with detailed diagnostics, residuals, and inference. Best for econometrics, time series, rigorous inference with coefficient tables. For guided statistical test selection with APA reporting use statistical-analysis.
Answer questions about the AI SDK and help build AI-powered features. Use when developers: (1) Ask about AI SDK functions like generateText, streamText, ToolLoopAgent, embed, or tools, (2) Want to build AI agents, chatbots, RAG systems, or text generation features, (3) Have questions about AI providers (OpenAI, Anthropic, Google, etc.), streaming, tool calling, structured output, or embeddings, (4) Use React hooks like useChat or useCompletion. Triggers on: "AI SDK", "Vercel AI SDK", "generateText", "streamText", "add AI to my app", "build an agent", "tool calling", "structured output", "useChat".
Create an llms.txt file from scratch based on repository structure following the llms.txt specification at https://llmstxt.org/
Use when working directly with the `esm` Python SDK, ESM3 or ESMC model IDs, Forge/Biohub inference clients, or ESMFold2 folding workflows.
Modal is a serverless cloud platform for running Python on demand, including on-demand GPUs. Use when deploying or serving AI/ML models, running GPU-accelerated workloads (training, fine-tuning, inference), serving web endpoints, scheduling batch jobs, or scaling Python code to cloud containers with the Modal SDK.
Use Therapeutics Data Commons through the PyTDC Python package for registry discovery, approved dataset access, task-aware splits, evaluator metrics, benchmark groups, and bounded molecular-oracle workflows.
Take loulanyue/awesome-claude-notes-ko-kr-documentation-lookup from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.