Adopt100/100
PageIndex📑 PageIndex: Document Index for Vectorless, Reasoning-based RAG
Stars34K
Trust86/100
Audit90/100
Quality100/100
RiskSafe to try
RAG and knowledgeResearch agents
$ npx skills add VectifyAI/PageIndexUse-case shortlist
Find skills for document ingestion, retrieval, embeddings, source-grounded answers, and agent workflows that need reliable private knowledge.
Decision prompt
I need my agent to build a RAG workflow over documents, retrieve reliable context, and answer with grounded sources.
Published by OpenAgentSkill. Candidates are matched to this guide from available registry metadata. Repository stars describe popularity, not task success or safety. Check each source, license and current review before installing.
Recommended shortlist
📑 PageIndex: Document Index for Vectorless, Reasoning-based RAG
$ npx skills add VectifyAI/PageIndexLlamaIndex is the leading document agent and OCR platform
$ npx skills add run-llama/llama_indexMilvus is a high-performance, cloud-native vector database built for scalable vector ANN search
$ npx skills add milvus-io/milvusThis repository showcases various advanced techniques for Retrieval-Augmented Generation (RAG) systems. Each technique has a detailed notebook tutorial.
$ npx skills add NirDiamant/RAG_TechniquesHow to use this guide
Pick a small but representative set of documents before scaling ingestion.
Ask known-answer questions and inspect whether the right source material appears.
Require the agent to show the evidence behind each answer before shipping.
Evaluation notes
The point of a RAG skill is not only retrieval. It should help an agent ingest clean material, retrieve relevant context, and keep answers grounded in sources.
Many failures happen before retrieval. Web scraping, PDF parsing, OCR, and document cleanup skills often matter as much as the RAG layer.
FAQ
If the source material lives on the web, a scraping skill can be the upstream ingestion layer. For private files, document processing may be more important.
Start with a skill that can ingest and retrieve from your actual source format, then add companions for parsing, crawling, or evaluation.
More candidates
PDF Parser for AI-ready data. Automate PDF accessibility. Open-source.
🌌 A complete search engine and RAG pipeline in your browser, server or edge network with support for full-text, vector, and hybrid search in less than 2kb.
Deeplake is AI Data Runtime for Agents. It provides serverless postgres with a multimodal datalake, enabling scalable retrieval and training.
🔍大模型应用开发实战一：RAG 技术全栈指南，在线阅读地址：https://datawhalechina.github.io/all-in-rag/
Turn any PDF or image document into structured data for your AI. A powerful, lightweight OCR toolkit that bridges the gap between images/PDFs and LLMs. Supports 100+ languages.
Stop renting your intelligence. Own it with AnythingLLM. Everything you need for a powerful local-first agent experience
Build AI Agents, Visually
[EMNLP2025] "LightRAG: Simple and Fast Retrieval-Augmented Generation"
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