RAG AI Agent Skills

MCP Server for Driflyte. The Driflyte MCP Server exposes tools that allow AI assistants to query and retrieve topic-specific knowledge from recursively crawled and indexed web pages. With this MCP server, Driflyte acts as a bridge between diverse, topic-aware content sources (web, GitHub, and more) and AI-powered reasoning, enabling richer, more accurate answers. - Deep Web Crawling: Recursively f

English · 简体中文 · 日本語 👋 join us on Discord and WeChat If you like Dingo, please give us a ⭐ on GitHub! Dingo is A Comprehensive AI Data, Model and Application Quality Evaluation Tool, designed for ML practitioners, data engineers, and AI researchers. It helps you systematically assess and improve the quality of training data, fine-tuning datasets, and production AI systems. 🎯 Production-Grade Qua

Select and optimize embedding models for semantic search and RAG applications. Use when choosing embedding models, implementing chunking strategies, or optimizing embedding quality for specific domains.

Vectara-MCP provides any agentic application with access to fast, reliable RAG with reduced hallucination, powered by Vectara's Trusted RAG platform, through the MCP protocol. You can install the package directly from PyPI: - Security: Built-in authentication via bearer tokens - Encryption: HTTPS ready - Rate Limiting: 100 requests/minute by default - CORS Protection: Configurable origin validatio

English | 简体中文 🧠 The data layer for AI systems. Skill Seekers turns any documentation, GitHub repo, or PDF into structured knowledge assets—ready to power AI Skills (Claude, Gemini, OpenAI), RAG pipelines (LangChain, LlamaIndex, Pinecone), and AI coding assistants (Cursor, Windsurf, Cline) in minutes, not hours. Skill Seekers is the universal preprocessing layer that sits between raw documentatio

Implement efficient similarity search with vector databases. Use when building semantic search, implementing nearest neighbor queries, or optimizing retrieval performance.

Provides score interpretation (< 0.3 good, > 0.5 skip), query optimization, and source naming for query_documents, ingest_file, ingest_data tools. Use this skill when working with RAG, searching documents, ingesting files, saving web content, or handling PDF, HTML, DOCX, TXT, Markdown.

A vibe-coded memory management system with RAG capabilities for Claude via the Model Context Protocol (MCP). SimpleMem is an MCP server that provides persistent memory storage and retrieval for Claude and other MCP clients. It combines traditional file-based storage with modern RAG (Retrieval-Augmented Generation) capabilities, including semantic search and automatic relationship discovery. Think

Biel.ai MCP Server Connect your IDE to your product docs Give AI tools like Cursor, VS Code, and Claude Desktop access to your company's product knowledge through the Biel.ai platform. Biel.ai provides a hosted Retrieval-Augmented Generation (RAG) layer that makes your documentation searchable and useful to AI tools. This enables smarter completions, accurate technical answers, and context-aware s

skill-depot replaces the "dump all skill frontmatter into context" approach with selective, semantic retrieval. Agent skills are stored as Markdown files and indexed with vector embeddings — only the relevant skills are loaded when needed, keeping context lean. - Semantic Search — Find skills by meaning, not just keywords, using embedded vector search - Fully Local — No API keys, no cloud. U

🚀 Try ApeRAG Live Demo - Experience the full platform capabilities with our hosted demo ApeRAG is a production-ready RAG (Retrieval-Augmented Generation) platform that combines Graph RAG, vector search, and full-text search with advanced AI agents. Build sophisticated AI applications with hybrid retrieval, multimodal document processing, intelligent agents, and enterprise-grade management feature

Pre-ingestion verification for epistemic quality in RAG systems. Ensures documents are properly qualified before entering knowledge bases. Produces CGD (Clarity-Gated Documents) and validates SOT (Source of Truth) files.

Comprehensive guide to implementing RAG systems including vector database selection, chunking strategies, embedding models, and retrieval optimization. Use when building RAG systems, implementing semantic search, optimizing retrieval quality, or debugging RAG performance issues.

Implementation of an MCP server for the RAG Web Browser Actor. This Actor serves as a web browser for large language models (LLMs) and RAG pipelines, similar to a web search in ChatGPT. The easiest way to get the same web browsing capabilities is to use mcp.apify.com with default settings. - ✅ No local setup required - ✅ Always up-to-date - ✅ Access to 6,000+ Apify Actors (including RAG Web Browse

A Model Context Protocol (MCP) server that provides local vector database functionality using FAISS for Retrieval-Augmented Generation (RAG) applications. - Local Vector Storage: Uses FAISS for efficient similarity search without external dependencies - Document Ingestion: Automatically chunks and embeds documents for storage - Semantic Search: Query documents using natural language with sentence

knowledge-to-action-mcp is an MCP server for people whose real project context lives in notes, decisions, roadmaps, and meeting docs, not just code. Most Obsidian MCP servers stop at "read a note" or "search a vault." This one goes further: That means an MCP client can move from: If you work out of Obsidian, your important context is usually spread across: - roadmap notes - meeting notes - decisio