RAG AI Agent Skills

Build production-ready LLM applications, advanced RAG systems, and

rust-cargo-docs-rag-mcp is an MCP (Model Context Protocol) server that provides tools for Rust crate documentation lookup. It allows LLMs to look up documentation for Rust crates they are unfamiliar with. This README focuses on how to build, version, release, and install the project using two common paths: 1. pkgx (build/install locally from source) 2. Docker image (published to GitHub Container R

Expert in building Retrieval-Augmented Generation systems. Masters embedding models, vector databases, chunking strategies, and retrieval optimization for LLM applications. Use when: building RAG, vector search, embeddings, semantic search, document retrieval.

Every product will be AI-powered. The question is whether you'll build it right or ship a demo that falls apart in production. This skill covers LLM integration patterns, RAG architecture, prompt engineering that scales, AI UX that users trust, and cost optimization that doesn't bankrupt you. Use when: keywords, file_patterns, code_patterns.

Drop any book into it. Play inside the story. Got a favorite fantasy novel? A classic adventure module? A weird obscure sci-fi book from the 70s? Drop the PDF in, and DM Claude extracts every character, location, item, and plot thread, then drops you into that world as whoever you want to be.

Combine vector and keyword search for improved retrieval. Use when implementing RAG systems, building search engines, or when neither approach alone provides sufficient recall.

Production-ready patterns for building LLM applications. Covers RAG pipelines, agent architectures, prompt IDEs, and LLMOps monitoring. Use when designing AI applications, implementing RAG, building agents, or setting up LLM observability.

What a repository knows about itself. rag-rat is a local repo-intelligence index and MCP server for coding agents. It keeps source files read-only, writes only its own SQLite database, and answers with provenance on every result — current source, the code graph, git/GitHub history, and durable, source-anchored repo memories that persist across sessions and agents. Every coding harness already has

Lint any URL for LLM readiness. Get a 0-100 score for token efficiency, RAG readiness, agent compatibility, and LLM extraction quality. Context CLI is an LLM Readiness Linter that checks how well a URL is structured for AI consumption. As LLM-powered search engines, RAG pipelines, and AI agents become primary consumers of web content, your pages need to be optimized for token efficiency, structure

Free Local RAG for Claude Code - Save Tokens & Time 日本語版はこちら | Japanese Version DevRag is a lightweight RAG (Retrieval-Augmented Generation) system designed specifically for developers using Claude Code. Stop wasting tokens by reading entire documents - let vector search find exactly what you need. When using Claude Code, reading documents with the Read tool consumes massive amounts of tokens: - ❌

This project provides a Model Context Protocol (MCP) server that allows interaction with a Label Studio instance using the label-studio-sdk. It enables programmatic management of labeling projects, tasks, and predictions via natural language or structured calls from MCP clients. Using this MCP Server, you can make requests like: "Create a project in label studio with this data ..." "How many tasks

THE PROJECT HAS BEEN SPLIT AND MOVED TO INDIVIDUAL REPOSITORIES. - Google Kit: Tools for Gmail, Google Calendar, Google Chat - RAG Kit: Tools for RAG, Memory - Dev Kit: Tools for developers, jira, confluence, gitlab, github, ... - Fetch Kit: Tools for fetch, scrape, ... - Research Kit: Tools for research, academic, reasoning, ... A powerful Model Context Protocol (MCP) server implementation with i

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.

Self-hosted AI knowledge assistant (formerly Danswer). Connects to your team's Slack, Google Drive, Confluence, GitHub and turns them into a Q&A agent with source citations. The default open-source Glean alternative.

Expert in vector databases, embedding strategies, and semantic search implementation. Masters Pinecone, Weaviate, Qdrant, Milvus, and pgvector for RAG applications, recommendation systems, and similar

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

Read and write to a Pinecone index. The server implements the ability to read and write to a Pinecone index. - semantic-search: Search for records in the Pinecone index. - read-document: Read a document from the Pinecone index. - list-documents: List all documents in the Pinecone index. - pinecone-stats: Get stats about the Pinecone index, including the number of records, dimensions, and namespace

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

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

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

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

Client-side knowledge graph creator for codebases with a built-in Graph RAG agent. Runs entirely in the browser — no server. Point it at a repo and get an interactive graph plus a chat agent that answers with graph-aware context.

🚀 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

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