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Found 334 Skills
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Azure OpenAI SDK for .NET. Client library for Azure OpenAI and OpenAI services. Use for chat completions, embeddings, image generation, audio transcription, and assistants. Triggers: "Azure OpenAI", "AzureOpenAIClient", "ChatClient", "chat completions .NET", "GPT-4", "embeddings", "DALL-E", "Whisper", "OpenAI .NET".
This skill generates a comprehensive set of Frequently Asked Questions (FAQs) from the course description, course content, learning graphs, concept lists, MicroSims, and glossary terms to help students understand common questions and prepare content for chatbot integration. Use this skill after course description, learning graph, glossary, and at least 30% of chapter content exist.
Manage workspace knowledge files and libraries in the Cargo content domain — upload, list, rename, move, and remove files (PDFs, CSVs, text), and create or sync native and connector-backed libraries for retrieval-augmented generation (RAG). Use when the user wants to upload or organize knowledge files, build a knowledge library, or sync an external knowledge source. To attach these to an agent, use the cargo-ai skill.
DSPy declarative framework for automatic prompt optimization treating prompts as code with systematic evaluation and compilers
Mistral AI efficient open models. Use for efficient AI.
Comprehensive skill for building, deploying, and managing multi-agent AI systems with Agno framework
Perform autonomous, multi-step research using the Gemini Deep Research Agent (Interactions API). Supports web search, file/directory context, and resilient streaming.
LLM and ML model deployment for inference. Use when serving models in production, building AI APIs, or optimizing inference. Covers vLLM (LLM serving), TensorRT-LLM (GPU optimization), Ollama (local), BentoML (ML deployment), Triton (multi-model), LangChain (orchestration), LlamaIndex (RAG), and streaming patterns.
Design LLM applications using LangChain 1.x and LangGraph for agents, memory, and tool integration. Use when building LangChain applications, implementing AI agents, or creating complex LLM workflows.
Implement efficient similarity search with vector databases. Use when building semantic search, implementing nearest neighbor queries, or optimizing retrieval performance.
Testing strategies for LangChain4j-powered applications. Mock LLM responses, test retrieval chains, and validate AI workflows. Use when testing AI-powered features reliably.