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.

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

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

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

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: - ❌

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.

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

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.

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

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

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.

Official OpenAI plugin with OpenAPI schema for semantic search and retrieval-augmented generation (RAG) over personal or organizational documents.

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

👋 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

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.

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

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

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