> browse/registry

> chatgpt-apps

Build, scaffold, refactor, and troubleshoot ChatGPT Apps SDK applications that combine an MCP server and widget UI. Use when Codex needs to design tools, register UI resources, wire the MCP Apps bridge or ChatGPT compatibility APIs, apply Apps SDK metadata or CSP or domain settings, or produce a docs-aligned project scaffold. Prefer a docs-first workflow by invoking the openai-docs skill or OpenAI developer docs MCP tools before generating code.

> fireworks-ai

Expert guidance for Fireworks AI, the platform for running open-source LLMs (Llama, Mixtral, Qwen, etc.) with enterprise-grade speed and reliability. Helps developers integrate Fireworks' inference API, fine-tune models, and deploy custom model endpoints with function calling and structured output support.

> anthropic-sdk

Integrate Claude AI into applications with the Anthropic SDK. Use when a user asks to add Claude to an app, use Claude for text generation, build a chatbot with Claude, use Claude's long context window, implement tool use with Claude, stream Claude responses, use Claude for code generation, document analysis, or reasoning tasks. Covers Messages API, streaming, tool use, vision, system prompts, extended thinking, and batch processing.

> openai-sdk

Integrate OpenAI APIs into applications. Use when a user asks to add GPT or ChatGPT to an app, generate text with OpenAI, build a chatbot, use GPT-4 or o1 models, generate embeddings, use function calling, stream chat completions, build AI features, moderate content, generate images with DALL-E, transcribe audio with Whisper API, or integrate any OpenAI model. Covers Chat Completions, Assistants API, function calling, embeddings, streaming, vision, DALL-E, Whisper, and moderation.

> pentagi

Run AI-powered penetration testing with PentAGI. Use when a user asks to automate security testing, set up autonomous pentesting, deploy an AI-driven vulnerability scanner, build a self-hosted security testing platform, or conduct penetration tests with LLM-powered agents.

> e2b-sandbox

Run AI-generated code safely in cloud sandboxes with E2B — secure execution environments for LLM agents. Use when someone asks to "run code in a sandbox", "E2B", "execute AI-generated code safely", "code interpreter for AI", "sandboxed code execution", "run untrusted code", or "give my AI agent a computer". Covers sandbox creation, code execution, file system, process management, and custom environments.

> crawl4ai

You are an expert in Crawl4AI, the open-source web crawler built for AI applications. You help developers extract clean, structured data from websites for LLM training, RAG pipelines, and content analysis — with automatic markdown conversion, JavaScript rendering, CSS-based extraction, LLM-powered structured extraction, and session management for multi-page crawling.

> cerebras

Expert guidance for Cerebras Inference, the ultra-fast LLM inference service powered by the world's largest chip (Wafer-Scale Engine). Helps developers integrate Cerebras' API for applications requiring the fastest possible token generation — real-time chat, code completion, and interactive AI experiences.

> mem0

You are an expert in Mem0, the memory infrastructure for AI applications. You help developers add persistent, personalized memory to LLM-powered apps and agents — storing user preferences, conversation history, facts, and context that persists across sessions, enabling AI that remembers users, learns from interactions, and provides increasingly personalized responses.

> langsmith

Monitor, trace, debug, and evaluate LLM applications with LangSmith. Use when a user asks to trace LLM calls, debug chain executions, evaluate AI output quality, set up LLM observability, monitor agent performance, run prompt experiments, compare model outputs, create evaluation datasets, track token usage and latency, or build LLM testing pipelines. Covers tracing, datasets, evaluators, annotation queues, prompt hub, and production monitoring.

> langchain

Build LLM-powered applications with LangChain. Use when a user asks to create AI chains, build RAG pipelines, implement agents with tools, set up document loaders, create vector stores, build conversational AI, implement prompt templates, chain LLM calls, add memory to chatbots, or orchestrate language model workflows. Covers LangChain v0.3+ with LCEL (LangChain Expression Language), structured output, tool calling, retrieval, and production deployment patterns.

> cloudflare-ai

You are an expert in Cloudflare Workers AI, the serverless AI inference platform running on Cloudflare's global network. You help developers run LLMs, embedding models, image generation, speech-to-text, and translation models at the edge with zero cold starts, pay-per-use pricing, and integration with Workers, Pages, and Vectorize — enabling AI features without managing GPU infrastructure.

> traceloop

You are an expert in Traceloop and its OpenLLMetry SDK, the open-source observability framework that extends OpenTelemetry for LLM applications. You help developers instrument AI pipelines with automatic tracing for OpenAI, Anthropic, Cohere, LangChain, LlamaIndex, vector databases, and frameworks — exporting to any OpenTelemetry-compatible backend (Grafana Tempo, Jaeger, Datadog, Honeycomb, Traceloop Cloud).

> langgraph

Build stateful, multi-step AI agents and workflows with LangGraph. Use when a user asks to create AI agents with complex logic, build multi-agent systems, implement human-in-the-loop workflows, create state machines for LLMs, build agentic RAG, implement tool-calling agents with branching logic, create planning agents, build supervisor/worker patterns, or orchestrate multi-step AI pipelines with cycles, persistence, and streaming.

> vercel-ai-sdk

You are an expert in the Vercel AI SDK, the TypeScript toolkit for building AI-powered applications. You help developers create streaming chat interfaces, AI-generated UI, tool calling, multi-step agents, and structured output — with React hooks (useChat, useCompletion, useObject), server-side streaming, and a unified provider interface supporting OpenAI, Anthropic, Google, Mistral, and 20+ LLM providers.

> onnx

Open Neural Network Exchange format for model interoperability across frameworks. Export models from PyTorch, TensorFlow, and other frameworks to ONNX, optimize with ONNX Runtime, and deploy for cross-platform inference on CPU, GPU, and edge devices.

> haystack

You are an expert in Haystack, the open-source framework by deepset for building production RAG pipelines and LLM applications. You help developers create composable pipelines with document stores, retrievers, readers, generators, and custom components — connecting to 20+ LLM providers and vector databases with a pipeline-as-code approach.

> helicone

LLM observability proxy that sits between your app and LLM providers. Logs every request, enables caching, rate limiting, and provides cost analytics. Works with OpenAI, Anthropic, and other providers with a one-line integration change.

> peft-fine-tuning

Parameter-efficient fine-tuning for LLMs using LoRA, QLoRA, and 25+ methods. Use when a user asks to fine-tune a language model, train a custom LLM, adapt a model to their data, use LoRA or QLoRA, fine-tune Llama or Mistral, or train a model on consumer GPUs. Covers PEFT methods for 7B-70B parameter models.

> openai-realtime

Build voice-enabled AI applications with the OpenAI Realtime API. Use when a user asks to implement real-time voice conversations, stream audio with WebSockets, build voice assistants, or integrate OpenAI audio capabilities.