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.

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.

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.

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

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

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

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

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: - ❌

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

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.

RAGMap is a lightweight MCP Registry-compatible subregistry + MCP server focused on RAG-related MCP servers. - Ingests the official MCP Registry, enriches records for RAG use-cases, and serves a subregistry API. - Exposes an MCP server (remote Streamable HTTP + local stdio) so agents can search/filter RAG MCP servers. MapRag is a discovery + routing layer for retrieval. It helps agents and humans

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

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.

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

1. 入门示例: tablestore-java-mcp-server 2. 基于 MCP 架构实现知识库答疑系统: tablestore-java-mcp-server-rag - 实现一个目前最常见的一类 AI 应用即答疑系统，支持基于私有知识库的问答，会对知识库构建和 RAG 做一些优化。 1. 入门示例: tablestore-python-mcp-server 1. Mem0-OpenMemory-MCP: tablestore-python-mem0-mcp-server 欢迎加入我们的钉钉公开群，与我们一起探讨 AI 技术。钉钉群号：36165029092

The implementation of Model Context Protocol (MCP) server for VictoriaMetrics. This provides access to your VictoriaMetrics instance and seamless integration with VictoriaMetrics APIs and documentation. It can give you a comprehensive interface for monitoring, observability, and debugging tasks related to your VictoriaMetrics instances, enable advanced automation and interaction capabilities for e

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.

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.

mcp-ragchat An MCP server that adds RAG-powered AI chat to any website. One command from Claude Code. Tell Claude Code "add AI chat to mysite.com" and it will crawl your content, build a local vector store, spin up a chat server, and hand you an embed snippet. No cloud infra. No database. Just one API key. 1. Clone and build 2. Configure Claude Code (~/.claude/mcp.json) Open Claude Code and say: C

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

🚀 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

Efficiently perform web searches using the mcp-local-rag server with semantic similarity ranking. Use this skill when you need to search the web for current information, research topics across multiple sources, or gather context from the internet without using external APIs. This skill teaches effective use of RAG-based web search with DuckDuckGo, Google, and multi-engine deep research capabilities.