Loading...
Loading...
Found 44 Skills
This skill provides production-ready AI chat UI components built on shadcn/ui for conversational AI interfaces. Use when building ChatGPT-style chat interfaces with streaming responses, tool/function call displays, reasoning visualization, or source citations. Provides 30+ components including Message, Conversation, Response, CodeBlock, Reasoning, Tool, Actions, Sources optimized for Vercel AI SDK v5. Prevents common setup errors with Next.js App Router, Tailwind v4, shadcn/ui integration, AI SDK v5 migration, component composition patterns, voice input browser compatibility, responsive design issues, and streaming optimization. Keywords: ai-elements, vercel-ai-sdk, shadcn, chatbot, conversational-ai, streaming-ui, chat-interface, ai-chat, message-components, conversation-ui, tool-calling, reasoning-display, source-citations, markdown-streaming, function-calling, ai-responses, prompt-input, code-highlighting, web-preview, branch-navigation, thinking-display, perplexity-style, claude-artifacts
Guide for adding new AI provider packages to the AI SDK. Use when creating a new @ai-sdk/<provider> package to integrate an AI service into the SDK.
Capture API response test fixture.
List the contents of an npm package tarball before publishing. Use when the user wants to see what files are included in an npm bundle, verify package contents, or debug npm publish issues.
Add new or remove obsolete model IDs for existing AI SDK providers. Use when adding a model to a provider, removing an obsolete model, or processing a list of model changes from an issue. Triggers on "add model", "remove model", "new model ID", "obsolete model", "update model IDs".
Develop examples for AI SDK functions. Use when creating, running, or modifying examples under examples/ai-functions/src to validate provider support, demonstrate features, or create test fixtures.
Guide for adding new AI function examples, for testing specific features against the actual provider APIs.
Mem0 CLI -- the command-line interface for mem0 memory operations. TRIGGER when: user mentions "mem0 cli", "mem0 command line", "@mem0/cli", "mem0-cli", "pip install mem0-cli", "npm install -g @mem0/cli", or is running mem0 commands in a terminal/shell (mem0 add, mem0 search, mem0 list, mem0 get, mem0 init, mem0 config, mem0 import). Also triggers when query includes CLI flags like --user-id, --output, --json, --agent, or describes bash/zsh/terminal/shell usage. DO NOT TRIGGER when: user asks about programmatic SDK integration in Python/TS code (use mem0 skill), or Vercel AI SDK provider (use mem0-vercel-ai-sdk skill).
Vercel AI SDK v5 for backend AI (text generation, structured output, tools, agents). Multi-provider. Use for server-side AI or encountering AI_APICallError, AI_NoObjectGeneratedError, streaming failures.
Build RAG pipelines with Exa.ai for real-time web retrieval. Use when building retrieval-augmented generation, integrating Exa with LangChain, LlamaIndex, Vercel AI SDK, or implementing AI agents with web search capabilities. Triggers on: RAG pipeline, retrieval augmented generation, Exa LangChain, Exa LlamaIndex, ExaSearchRetriever, ExaSearchResults, Exa MCP, Exa tool calling, Claude tool use, AI agent web search, grounded generation, citation generation, fact checking, hallucination detection, OpenAI compatibility, chat completions.
Design patterns for building AI-powered interfaces like chatbots and intelligent assistants in React.
Use when billing for AI model token usage — setting up @commet/ai-sdk tracked() middleware, configuring balance consumption model plans with AI model pricing, tracking input/output/cache tokens, cost calculation with margins, or building AI products that need usage-based billing.