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Build production-ready MCP clients in TypeScript or Python. Handles connection lifecycle, transport abstraction, tool orchestration, security, and error handling. Use for integrating LLM applications with MCP servers.
Use when creating new skills or editing existing skills - applies prompt engineering principles to write clear, goal-focused process documentation that trusts LLM intelligence
Expert guidance for building full-stack applications with Next.js frontend and FastAPI backend. Use when integrating React/Next.js with Python FastAPI, building API routes, or implementing SSR/SSG with Python backends.
Analyze emotional tone, sentiment polarity, and psychological impact of text. Execute Python script for detailed sentiment analysis. Use when analyzing mood, attitude, or emotional content.
Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).
Expert guidance for LangGraph Python library. Build stateful, multi-actor applications with LLMs using nodes, edges, and state management. Use when working with LangGraph, building agent workflows, state machines, or complex multi-step LLM applications. Requires langgraph, langchain-core packages.
Coordinate multiple agents for software development across any language. Use for parallel execution of independent tasks, sequential chains with dependencies, swarm analysis from multiple perspectives, or iterative refinement loops. Handles Python, JavaScript, Java, Go, Rust, C#, and other languages.
Strategic automation architecture advisor. Use when users want to plan automation solutions, evaluate their tech stack (Shopify, Zoho, HubSpot, etc.), decide between n8n vs Python/Claude Code, or need guidance on production-ready automation design. Invokes plan mode for complex architectural decisions.
Design, implement, and debug a custom ChatKit backend in Python that powers the ChatKit UI without Agent Builder, using the OpenAI Agents SDK (and optionally Gemini via an OpenAI-compatible endpoint). Use this Skill whenever the user wants to run ChatKit on their own backend, connect it to agents, or integrate ChatKit with a Python web framework (FastAPI, Django, etc.).
Quick reference cheatsheets for Kailash SDK patterns, nodes, workflows, and best practices. Use when asking about 'quick tips', 'cheat sheet', 'quick reference', 'common mistakes', 'node selection', 'workflow patterns library', 'cycle patterns', 'production patterns', 'performance optimization', 'monitoring', 'security config', 'multi-tenancy', 'distributed transactions', 'saga pattern', 'custom nodes', 'PythonCode data science', 'ollama integration', 'directoryreader patterns', or 'environment variables'.
Expert guidance for creating strands-cli workflow specifications (YAML/JSON). Use when creating, modifying, or troubleshooting strands-cli specs for: (1) Multi-step agent workflows (chain, routing, parallel, graph patterns), (2) Tool configuration (python_exec, http_request, custom tools), (3) Runtime and provider setup (Bedrock, OpenAI, Ollama), (4) Input/output handling and templating, or (5) Debugging validation errors
This skill should be used when users want to run any workload on Hugging Face Jobs infrastructure. Covers UV scripts, Docker-based jobs, hardware selection, cost estimation, authentication with tokens, secrets management, timeout configuration, and result persistence. Designed for general-purpose compute workloads including data processing, inference, experiments, batch jobs, and any Python-based tasks. Should be invoked for tasks involving cloud compute, GPU workloads, or when users mention running jobs on Hugging Face infrastructure without local setup.
指南：创建高质量的 MCP（模型上下文协议）服务器，使 LLM 能够通过精心设计的工具与外部服务交互。在构建 MCP 服务器以集成外部 API 或服务时使用，无论是 Python（FastMCP）还是 Node/TypeScript（MCP SDK）。
Guides TimeGPT lab environment setup including Python dependencies, API key configuration, smoke testing, experiment workflows, and optional CI/CD integration. Inspects environment docs and scripts to provide step-by-step setup instructions, troubleshooting guidance, and onboarding for new developers. Use when setting up TimeGPT lab, troubleshooting environment issues, or running experiments.
Beginner workflow for LlamaIndex agents (Python). Use when the user wants FunctionAgent or ReActAgent with tools.
Use when building AI agents with Google's Agent Development Kit (ADK) Python - multi-agent systems, workflow agents, tool integration, Vertex AI deployment, or agent evaluation.
Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).
Build MCP servers for LLM-external service integration. Use for FastMCP (Python), MCP SDK (Node/TypeScript), tool design, API integration, resource providers.
Comprehensive data science, machine learning, and AI guide covering Python, deep learning, NLP, LLMs, prompt engineering, and MLOps. Use when building AI models, data pipelines, or machine learning systems.
Beginner workflow for OpenAI Agents SDK (Python or TypeScript). Use when the user wants a code-first agent app with tools, handoffs, streaming, and traces.
