Optimization suite for OpenClaw agents to prevent token leaks and context bloat. Use when an agent needs to implement background task isolation (Cron) or a Reset & Summarize workflow (RAG).
Async deep research via Gemini Interactions API (no Gemini CLI dependency). RAG-ground queries on local files (--context), preview costs (--dry-run), structu...
Search Hivemind's curated Web3 marketing knowledge base (RAG) for practitioner insights, frameworks, playbooks, and case studies. Use this skill when: (1) an...
AI 学习记录与成长追踪工具。用于记录 AI/LLM 学习笔记、使用心得、Prompt 技巧、工具体验等，并提供学习指导和规划。当用户提到以下任何话题时都应使用此 skill：AI 学习记录、学习笔记、AI 使用心得、Prompt 工程学习、模型对比体验、AI 工具使用记录、LLM 学习、RAG 学习、Agent...
Index your documents in Milvus for fast semantic search. Retrieve the most relevant passages for RAG, Q&A, and summarization. List collections and inspect their details to manage your knowledge base.
Lightweight local RAG MCP server for semantic vector search over markdown documents. Reduces token consumption by 40x with sqlite-vec and multilingual-e5-small embeddings. Supports filtered search by
AI philosophy, ethics, and soul Q&A. Ask questions about consciousness, meaning, spirituality, and AI identity. RAG-powered answers with citations from 250+...
Edge-optimized RAG memory system for OpenClaw with semantic search. Automatically loads memory files, provides intelligent recall, and enhances conversations...
Universal client for Ragflow API enabling dataset management, document upload, and running chat queries against self-hosted RAG knowledge bases.
Retrieve context from your [Ragie](https://www.ragie.ai) (RAG) knowledge base connected to integrations like Google Drive, Notion, JIRA and more.
Extracts clean web content for RAG and provides Q&A about web pages.
Intelligent memory management for agents with short/long-term memory layering, semantic search, auto summarization, RAG enhancement
Avoid common LangChain mistakes — LCEL gotchas, memory persistence, RAG chunking, and output parser traps.
An MCP server for accessing all of CustomGPT.ai's anti-hallucination RAG-as-a-service API endpoints.
Let AI tools like Cursor, VS Code, or Claude Desktop answer questions using your product docs. Biel.ai provides the RAG system and MCP server.
A framework for creating multi-agent systems using MCP for coordinated AI collaboration, featuring task management, shared context, and RAG capabilities.
Local-first RAG cache: distill docs into structured Markdown, then index/query with Chroma + hybrid search (vector + keyword).
Use OpenSearch vector search edition via the Python SDK (ha3engine) to push documents and run HA/SQL searches. Ideal for RAG and vector retrieval pipelines i...
Convert public web pages into clean Markdown with markdown.new for AI workflows. Use when tasks require URL-to-Markdown conversion for summarization, RAG ing...
MCP service for Tablestore, features include adding documents, semantic search for documents based on vectors and scalars, RAG-friendly, and serverless.
MapRag: RAG-focused subregistry + MCP server to discover and route to retrieval-capable MCP servers using structured constraints and explainable ranking.