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".
npx skills add https://github.com/vercel-labs/open-agents --skill ai-sdk
Before searching docs, check if node_modules/ai/docs/ exists. If not, install only the ai package using the project's package manager (e.g., pnpm add ai).
Do not install other packages at this stage. Provider packages (e.g., @ai-sdk/openai) and client packages (e.g., @ai-sdk/react) should be installed later when needed based on user requirements.
Everything you know about the AI SDK is outdated or wrong. Your training data contains obsolete APIs, deprecated patterns, and incorrect usage.
When working with the AI SDK:
ai package is installed (see Prerequisites)node_modules/ai/docs/ and node_modules/ai/src/ for current APIsuseChat has changed significantly - check Common Errors before writing client codecurl -s https://ai-gateway.vercel.sh/v1/models | jq -r '[.data[] | select(.id | startswith("provider/")) | .id] | reverse | .[]' (replacing provider with the relevant provider like anthropic, openai, or google) to get the full list with newest models first. Use the model with the highest version number (e.g., claude-sonnet-4-5 over claude-sonnet-4 over claude-3-5-sonnet).If you cannot find documentation to support your answer, state that explicitly.
Search bundled docs and source in node_modules/ai/:
grep "query" node_modules/ai/docs/grep "query" node_modules/ai/src/Provider packages include docs at node_modules/@ai-sdk/<provider>/docs/.
https://ai-sdk.dev/api/search-docs?q=your_query.md URLs from results (e.g., https://ai-sdk.dev/docs/agents/building-agents.md)Before searching source code, grep Common Errors for the failing property or function name. Many type errors are caused by deprecated APIs documented there.
If not found in common-errors.md:
node_modules/ai/src/ and node_modules/ai/docs/Always use the ToolLoopAgent pattern. Search node_modules/ai/docs/ for current agent creation APIs.
File conventions: See type-safe-agents.md for where to save agents and tools.
Type Safety: When consuming agents with useChat, always use InferAgentUIMessage<typeof agent> for type-safe tool results. See reference.
Before implementing agent consumption:
package.json to detect the project's framework/stackConvert 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.
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.
Machine learning in Python with scikit-learn. Use when working with supervised learning (classification, regression), unsupervised learning (clustering, dimensionality reduction), model evaluation, hyperparameter tuning, preprocessing, or building ML pipelines. Provides comprehensive reference documentation for algorithms, preprocessing techniques, pipelines, and best practices.
Take vercel-labs/ai-sdk 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.
The instructions reference pnpm.
Without those the skill loads but fails at the first command.