Use-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.
Recommended shortlist
This repository showcases various advanced techniques for Retrieval-Augmented Generation (RAG) systems. Each technique has a detailed notebook tutorial.
$ npx skills add NirDiamant/RAG_TechniquesOpen-source LLM knowledge platform: turn raw documents into a queryable RAG, an autonomous reasoning agent, and a self-maintaining Wiki.
$ npx skills add Tencent/WeKnora📑 PageIndex: Document Index for Vectorless, Reasoning-based RAG
$ npx skills add VectifyAI/PageIndexQdrant - High-performance, massive-scale Vector Database and Vector Search Engine for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/
$ npx skills add qdrant/qdrantHow 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
Private AI platform for agents, assistants and enterprise search. Built-in Agent Builder, Deep research, Document analysis, Multi-model support, and API connectivity for agents.
Weaviate is an open-source vector database that stores both objects and vectors, allowing for the combination of vector search with structured filtering with the fault tolerance and scalability of a cloud-native database​.
💡 All-in-one AI framework for semantic search, LLM orchestration and language model workflows
Stop renting your intelligence. Own it with AnythingLLM. Everything you need for a powerful local-first agent experience
Developer-friendly OSS embedded retrieval library for multimodal AI. Search More; Manage Less.
Milvus is a high-performance, cloud-native vector database built for scalable vector ANN search
Cognee is the open-source AI memory platform for agents. Give your AI agents persistent long-term memory across sessions with a self-hosted knowledge graph engine.
🌌 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.
Next guides
Use-case shortlist
Compare skills for crawling sites, extracting structured data, converting pages to markdown, and feeding reliable web context into agent workflows.
Platform shortlist
A focused guide for builders using Codex-style coding agents: repository inspection, issue triage, implementation planning, testing, and browser verification skills.
Installation guide
A step-by-step workflow for adding install-ready skills to a Codex-style agent setup while keeping validation, permissions, and rollback in mind.
Use-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.
Recommended shortlist
This repository showcases various advanced techniques for Retrieval-Augmented Generation (RAG) systems. Each technique has a detailed notebook tutorial.
$ npx skills add NirDiamant/RAG_TechniquesOpen-source LLM knowledge platform: turn raw documents into a queryable RAG, an autonomous reasoning agent, and a self-maintaining Wiki.
$ npx skills add Tencent/WeKnora📑 PageIndex: Document Index for Vectorless, Reasoning-based RAG
$ npx skills add VectifyAI/PageIndexQdrant - High-performance, massive-scale Vector Database and Vector Search Engine for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/
$ npx skills add qdrant/qdrantHow 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
Private AI platform for agents, assistants and enterprise search. Built-in Agent Builder, Deep research, Document analysis, Multi-model support, and API connectivity for agents.
Weaviate is an open-source vector database that stores both objects and vectors, allowing for the combination of vector search with structured filtering with the fault tolerance and scalability of a cloud-native database​.
💡 All-in-one AI framework for semantic search, LLM orchestration and language model workflows
Stop renting your intelligence. Own it with AnythingLLM. Everything you need for a powerful local-first agent experience
Developer-friendly OSS embedded retrieval library for multimodal AI. Search More; Manage Less.
Milvus is a high-performance, cloud-native vector database built for scalable vector ANN search
Cognee is the open-source AI memory platform for agents. Give your AI agents persistent long-term memory across sessions with a self-hosted knowledge graph engine.
🌌 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.
Next guides
Use-case shortlist
Compare skills for crawling sites, extracting structured data, converting pages to markdown, and feeding reliable web context into agent workflows.
Platform shortlist
A focused guide for builders using Codex-style coding agents: repository inspection, issue triage, implementation planning, testing, and browser verification skills.
Installation guide
A step-by-step workflow for adding install-ready skills to a Codex-style agent setup while keeping validation, permissions, and rollback in mind.