Build LLM applications using Dify's visual workflow platform. Use when creating AI chatbots, implementing RAG pipelines, developing agents with tools, managing knowledge bases, deploying LLM apps, or building workflows with drag-and-drop. Supports hundreds of LLMs, Docker/Kubernetes deployment.
Guides creation of 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). Covers tool design, authentication, Docker deployment, and evaluation creation. NOT when consuming existing MCP servers (use the server directly).
Comprehensive FastAPI knowledge for building production-ready APIs from basic to planet-scale. Use when building FastAPI applications, implementing REST APIs, setting up database operations with SQLModel, implementing authentication (OAuth2/JWT), deploying to Docker/Kubernetes, or needing guidance on middleware, WebSockets, background tasks, dependency injection, security, scalability, or performance optimization. Triggers include "FastAPI", "build API", "REST endpoint", "SQLModel", "OAuth2", "JWT", "deploy FastAPI", "Docker FastAPI", "Kubernetes", "API security", or questions about Python web frameworks.
Use buzzllm CLI to perform web searches, execute Python code in Docker, search/analyze local codebases, or generate code change blocks. Trigger when user asks for web search, Python execution, code analysis, or code modifications via LLM.
Complete guide for Panaversity Hackathon II (Todo Spec-Driven Development) Phases 2-5. Use when working on Phase 3 (MCP + OpenAI Agents + ChatKit chatbot), Phase 4 (Kubernetes with Minikube, Helm, Docker), Phase 5 (Cloud deployment with Dapr, Kafka, advanced features), or implementing bonus features (Reusable Intelligence, Cloud-Native Blueprints, Urdu support, Voice commands). Includes detailed references, scaffolding scripts, troubleshooting guides, and implementation patterns for AI-powered todo chatbot with event-driven architecture.
Expert guidance for building REST APIs with FastAPI framework in Python. Use when (1) creating new FastAPI projects from scratch, (2) implementing API endpoints with routing, (3) working with Pydantic models for validation, (4) setting up dependency injection, (5) implementing authentication (OAuth2, JWT, API keys), (6) integrating databases (SQLAlchemy sync/async), (7) writing tests for FastAPI apps, (8) deploying FastAPI to production (Docker, Gunicorn), or (9) implementing advanced features like WebSockets, middleware, background tasks.
Execute agentic coding tools directly via bash commands with output capture. Use this when the user requests to run AI agents (Gemini, Claude Code, etc.) and get their results directly.
Setup and use Docker AI (Gordon) for intelligent container operations
Comprehensive FastAPI development skill for building production-ready Python APIs from Hello World to enterprise applications. Use this skill when the user asks about FastAPI or wants to build APIs with Python, including: (1) Creating new FastAPI projects or applications, (2) Building REST APIs with path operations, query parameters, request bodies, (3) Implementing authentication and authorization with OAuth2, JWT tokens, API keys, (4) Database integration with SQLModel, SQLAlchemy, CRUD operations, (5) Project structuring with APIRouter, dependencies, middleware, (6) Testing FastAPI applications with TestClient and pytest, (7) Deployment strategies including Docker, cloud platforms, HTTPS configuration, (8) Advanced features like WebSockets, background tasks, CORS, file uploads, (9) Debugging FastAPI issues or improving existing FastAPI code, (10) Questions about FastAPI best practices, patterns, or architecture. Triggers include phrases like "build an API", "create FastAPI app", "add authentication", "set
Search and retrieve information from the vector knowledge base (RAG)
Deploy and manage Docker containers on Cloudflare's global network alongside Workers. Use when building applications that need to run Python code, process large files (multi-GB zips, video transcoding), execute CLI tools, run AI inference, create code sandboxes, or any workload requiring more memory/CPU than Workers provide. Triggers include requests to run containers, execute arbitrary code, process large files, deploy backend services in Python/Go/Rust, or integrate heavyweight compute with Workers.
Railway Docker cache troubleshooting for Dr. Sophia AI deployments. Covers cache detection, force rebuild methods, version verification, deployment validation. Use when Railway shows old code after deployment, deployments not updating, Docker cache issues, or version mismatches between local and deployed code.
Fetches latest documentation for programming libraries, frameworks, and tools. Use when studying current APIs, best practices, release notes, or feature updates. Triggers for requests like "What's new in React?", "Latest LangChain docs", "Current Docker best practices".
Expert guidance for deploying Agno AGI agents and AI applications to production using Docker, FastAPI, monitoring, CI/CD, and cloud platforms
Comprehensive FastAPI development skill for building APIs from hello world to production-ready applications. Use when building REST APIs, microservices, ML/AI APIs, or full-stack applications with FastAPI. Covers project scaffolding, database integration (PostgreSQL, MySQL, MongoDB, SQLite), testing, Docker deployment, background tasks, file uploads, and production best practices.
Build AI agents with Hugging Face's SmolAgents framework. Use when creating code-executing agents, tool-calling agents, multi-agent systems, agentic RAG, text-to-SQL pipelines, web browsing agents, or any multi-step AI workflows. Covers CodeAgent, ToolCallingAgent, custom tools, MCP integration, memory management, secure code execution (E2B, Docker, Blaxel), and model configuration (HF Inference, LiteLLM, Transformers, Ollama).
Comprehensive FastAPI development assistance including project setup, route creation, database integration, authentication, and deployment. Use when Claude needs to work with FastAPI projects for: (1) Creating new FastAPI applications, (2) Adding API endpoints and routes, (3) Setting up database connections with SQLAlchemy/SQLModel, (4) Implementing authentication and security, (5) Creating Pydantic models, (6) Docker deployment, or any other FastAPI development tasks
Fine-tune LLMs with Unsloth using GRPO or SFT. Supports FP8, vision models, mobile deployment, Docker, packing, GGUF export. Use when: train with GRPO, fine-tune, reward functions, SFT training, FP8 training, vision fine-tuning, phone deployment, docker training, packing, export to GGUF.
Automated LGTM + Alloy observability stack deployment using Docker Compose. Use when setting up Claude Code observability infrastructure locally.
