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.

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: - ❌

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

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.

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

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.

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.

MCPServers.org | ModelScope | （更多MCP平台陆续上架中……） CRIC物业AI 是 克而瑞 专为物业行业打造的智能 AI 助理，于2025年4月25日 正式发布。 CRIC物业AI 通过行业知识库建设，结合多模态大模型 + RAG 技术，集成五大核心能力模块：行业研究、法律法规、社区治理、项目经营、文案写作，并在行业垂类知识基础上，拓展了 资讯舆情 和 人才培训 两大智能体。 克而瑞通过三个能力来构建其自身在物业AI合作领域优势： - 数据资产转化能力： 将10亿字行业语料、TB级多模态数据转化为物业行业的高质量数据集，并构建了一套行业数据质量评估体系，保障准确率和可信度； - 场景穿透能力： 聚焦20+物业行业垂直业务场景，定向选用对应领域知识库，精准匹配； - 生态进化能力： 通过每日实时监测超过500+可信资讯和数据来源，处理10万+实时数据的自更新系

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

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

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

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

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

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

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

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

An MCP server implementation that provides tools for retrieving and processing documentation through vector search, enabling AI assistants to augment their responses with relevant documentation context. - Vector-based documentation search and retrieval - Support for multiple documentation sources - Semantic search capabilities - Automated documentation processing - Real-time context augmentation f

Context compaction for AI code assistants – Give your AI structured, token-efficient access to 17+ languages including Elixir, Python, TypeScript, JavaScript, Rust, and more. Quick Install · Security · Developers · AI Assistants · Docs The core problem: AI code assistants waste context on blind searches. Grep dumps entire files when you only need a function signature, leaving less room for actual