Total 56,145 skills, AI & Machine Learning has 9352 skills
Showing 12 of 9352 skills
RAG-specific best practices for LlamaIndex, ChromaDB, and Celery workers. Covers ingestion, retrieval, embeddings, and performance.
Comprehensive guide for managing vector databases including Pinecone, Weaviate, and Chroma for semantic search, RAG systems, and similarity-based applications
Generate images, videos, and audio with fal.ai serverless AI. Use when building AI image generation, video generation, image editing, or real-time AI features. Triggers on fal.ai, fal, AI image generation, Flux, SDXL, real-time AI, serverless AI.
Battle-tested Claude Code workflows from power users. Self-correcting memory, parallel worktrees, wrap-up rituals, and the 80/20 AI coding ratio. Distilled from real production use.
Create or refactor Ship Faster-style skills (SKILL.md + references/ + scripts/). Use when adding a new skill, tightening trigger descriptions, splitting long docs into references, defining artifact-first I/O contracts, or packaging/validating a skill.
Model Context Protocol expert for building MCP servers, tools, resources, and client integrationsUse when "mcp server, model context protocol, claude code extension, building ai tools, tool definition, mcp transport, stdio transport, sse transport, resource provider, prompt template, mcp, model-context-protocol, claude-code, ai-tools, llm-integration, anthropic, server, protocol" mentioned.
Review, audit, and harden AI skills for security risks including prompt injection, hidden instructions, tool misuse, data exfiltration, and malicious payloads; use when analyzing SKILL.md, scripts, references, or assets for vulnerabilities and when producing remediation guidance.
Convert HuggingFace transformer models to ONNX format for browser inference with Transformers.js and WebGPU. Use when given a HuggingFace model link to convert to ONNX, when setting up optimum-cli for ONNX export, when quantizing models (fp16, q8, q4) for web deployment, when configuring Transformers.js with WebGPU acceleration, or when troubleshooting ONNX conversion errors. Triggers on mentions of ONNX conversion, Transformers.js, WebGPU inference, optimum export, model quantization for browser, or running ML models in the browser.
MLflow ML lifecycle management. Use for ML experiment tracking.
CLI for Limitless.ai Pendant with lifelog management, FalkorDBLite semantic graph, vector embeddings, and DAG pipelines. Use for personal memory queries, semantic search across lifelogs/chats/persons/topics, entity extraction, and knowledge graph operations. Triggers include "lifelog", "pendant", "limitless", "personal memory", "semantic search", "graph query", "extraction".
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.
Build MCP (Model Context Protocol) servers using the official Python SDK. Covers FastMCP high-level API with @mcp.tool(), @mcp.resource(), @mcp.prompt() decorators, FastAPI/Starlette integration, transports (stdio, SSE, streamable-http), and database integration.