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Found 372 Skills
Expert in Apache Kafka, Event Streaming, and Real-time Data Pipelines. Specializes in Kafka Connect, KSQL, and Schema Registry.
Building AAA-quality games and real-time experiences with Unreal Engine 5Use when "unreal, ue5, ue4, unreal engine, blueprints, blueprint, actor component, gameplay ability, gas unreal, niagara, nanite, lumen, world partition, level streaming, unreal multiplayer, unreal replication, gamemode, gamestate, playerstate, playercontroller, pawn, character class, uclass, ustruct, uenum, uproperty, ufunction, unreal, ue5, blueprints, c++, gamedev, aaa, real-time, rendering, nanite, lumen, niagara, gameplay, replication, multiplayer, gas" mentioned.
Complete guide for OpenAI's Assistants API v2: stateful conversational AI with built-in tools (Code Interpreter, File Search, Function Calling), vector stores for RAG (up to 10,000 files), thread/run lifecycle management, and streaming patterns. Both Node.js SDK and fetch approaches. ⚠️ DEPRECATION NOTICE: OpenAI plans to sunset Assistants API in H1 2026 in favor of Responses API. This skill remains valuable for existing apps and migration planning. Use when: building stateful chatbots with OpenAI, implementing RAG with vector stores, executing Python code with Code Interpreter, using file search for document Q&A, managing conversation threads, streaming assistant responses, or encountering errors like "thread already has active run", vector store indexing delays, run polling timeouts, or file upload issues. Keywords: openai assistants, assistants api, openai threads, openai runs, code interpreter assistant, file search openai, vector store openai, openai rag, assistant streaming, thread persistence, stateful chatbot, thread already has active run, run status polling, vector store error
Build AI-first applications with RAG pipelines, embeddings, vector databases, agentic workflows, and LLM integration. Master prompt engineering, function calling, streaming responses, and cost optimization for 2025+ AI development.
Build conversational AI agents using Pydantic AI + OpenRouter. Use when creating type-safe Python agents with tool calling, validation, and streaming.
Guide for advanced Next.js App Router patterns including Route Handlers, Parallel Routes, Intercepting Routes, Server Actions, error boundaries, draft mode, and streaming with Suspense. CRITICAL for server actions (action.ts, actions.ts files, 'use server' directive), setting cookies from client components, and form handling. Use when requirements involve server actions, form submissions, cookies, mutations, API routes, `route.ts`, parallel routes, intercepting routes, or streaming. Essential for separating server actions from client components.
Use when working with the OpenAI API (Responses API) or OpenAI platform features (tools, streaming, Realtime API, auth, models, rate limits, MCP) and you need authoritative, up-to-date documentation (schemas, examples, limits, edge cases). Prefer the OpenAI Developer Documentation MCP server tools when available; otherwise guide the user to enable `openaiDeveloperDocs`.
RabbitMQ message broker with AMQP protocol. Covers exchanges, queues, bindings, and messaging patterns. Use for reliable message delivery and complex routing scenarios. USE WHEN: user mentions "rabbitmq", "amqp", "exchanges", "routing patterns", "topic exchange", "fanout", asks about "message routing", "work queues", "request/reply", "flexible routing" DO NOT USE FOR: high-throughput streaming - use `kafka` or `pulsar`; cloud-native - use `nats`; AWS-native - use `sqs`; JMS required - use `activemq`; simple pub/sub - use `redis-pubsub`
Anthropic Claude API patterns for Python and TypeScript. Covers Messages API, streaming, tool use, vision, extended thinking, batches, prompt caching, and Claude Agent SDK. Use when building applications with the Claude API or Anthropic SDKs.
Chat with LLM models using ModelsLab's OpenAI-compatible Chat Completions API. Supports 60+ models including DeepSeek R1, Meta Llama, Google Gemini, Qwen, and Mistral with streaming, function calling, and structured outputs.
Expert guidance for building production-grade AI agents and workflows using Pydantic AI (the `pydantic_ai` Python library). Use this skill whenever the user is: writing, debugging, or reviewing any Pydantic AI code; asking how to build AI agents in Python with Pydantic; asking about Agent, RunContext, tools, dependencies, structured outputs, streaming, multi-agent patterns, MCP integration, or testing with Pydantic AI; or migrating from LangChain/LlamaIndex to Pydantic AI. Trigger even for vague requests like "help me build an AI agent in Python" or "how do I add tools to my LLM app" — Pydantic AI is very likely what they need.
Use this skill when working with the A2A (Agent-to-Agent) protocol - agent interoperability, multi-agent communication, agent discovery, agent cards, task lifecycle, streaming, and push notifications. Triggers on any A2A-related task including implementing A2A servers/clients, building agent cards, sending messages between agents, managing tasks, and configuring push notification webhooks.