Total 50,891 skills, AI & Machine Learning has 8520 skills
Showing 12 of 8520 skills
Model Context Protocol (MCP) server implementation patterns with LangChain4j. Use when building MCP servers to extend AI capabilities with custom tools, resources, and prompt templates.
Set up and run Ralph Wiggum loop - autonomous AI coding with clean slate iterations, PRD-driven features, and CI quality gates. Use for long-running autonomous coding tasks.
Self-modifying AI agent configuration via ruler + MCP + DuckDB. All behavior mods become one-liners.
Complete AI agent operating system setup with Kanban task management. Use when setting up multi-agent coordination, task tracking, or configuring an agent team. Includes theme selection (DBZ, One Piece, Marvel, etc.), workflow enforcement (all tasks through board), browser setup, GitHub integration, and memory enhancement (Supermemory, QMD).
Integration patterns and best practices for adding persistent memory to LLM agents using the Letta Learning SDK
Extract text from PDFs for LLM consumption. Use when processing PDFs for RAG, document analysis, or text extraction. Supports API services (Mistral OCR) and local tools (PyMuPDF, pdfplumber). Handles text-based PDFs, tables, and scanned documents with OCR.
Retrieval-Augmented Generation patterns including chunking, embeddings, vector stores, and retrieval optimizationUse when "rag, retrieval augmented, vector search, embeddings, semantic search, document qa, rag, retrieval, embeddings, vector, search, llm" mentioned.
Sets up vector databases for semantic search including Pinecone, Chroma, pgvector, and Qdrant with embedding generation and similarity search. Use when users request "vector database", "semantic search", "embeddings storage", "Pinecone setup", or "similarity search".
Transform Claude Code into an AI Scientist that orchestrates research workflows using tree-based hypothesis exploration. Triggers on "research project", "scientific experiment", "run experiments", "AI scientist", "tree search experimentation", "systematic study".
Convert audio/video to text using Whisper, with support for word-level timestamps. Use this when users need speech-to-text conversion, audio-to-text transcription, video-to-text extraction, subtitle generation, transcribe audio, speech to text, generate subtitles, or speech recognition.
AI risk assessment using NIST AI RMF 1.0 framework. Evaluate AI systems across 4 core functions (Govern, Map, Measure, Manage) for trustworthy and responsible AI deployment.
AI trustworthiness testing using OWASP AI Testing Guide v1. Execute 44 test cases across 4 layers (Application, Model, Infrastructure, Data) with practical payloads and remediation.