Search private knowledge
Use these skills to ingest documents, index knowledge, retrieve relevant context, and make agents better at answering with grounded sources.
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I need my agent to build a RAG workflow over documents and retrieve reliable context.
Agent should be able to
Resolve
Returns the best skill, alternatives, install handoff, risk summary, and safety gate.
Text plan
Plain text version for Codex, Claude Code, Cursor, and custom agent runtimes.
Browse
Open the filtered registry view for this workflow and compare candidates manually.
Recommended stack
Workflow map
Index documents
Search a knowledge base
Summarize source material
Ground answers in retrieved context
Best first installs
Qdrant - 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/
Install
Agent install candidate
Risk
Safe to try
Agent fit
Claude Code + CLI
Updated
Jun 30, 2026
$ npx skills add qdrant/qdrantA multi-modal vector database that supports upserts and vector queries using unified SQL (MySQL-Compatible) on structured and unstructured data, while meeting the requirements of high concurrency and ultra-low latency.
Install
Agent install candidate
Risk
Safe to try
Agent fit
Claude Code + CLI
Updated
May 25, 2026
$ npx skills add dingodb/dingoThis repository showcases various advanced techniques for Retrieval-Augmented Generation (RAG) systems. Each technique has a detailed notebook tutorial.
Install
Human review before install
Risk
Safe to try
Agent fit
Claude Code + OpenAI Agents
Updated
Jun 17, 2026
$ npx skills add NirDiamant/RAG_TechniquesSkill shortlist
local-first semantic code search engine
Low metadata risk · Review the audit page, then allow agent install in a sandboxed workflow.
Open-source LLM knowledge platform: turn raw documents into a queryable RAG, an autonomous reasoning agent, and a self-maintaining Wiki.
Review before production · Review the audit page, then allow agent install in a sandboxed workflow.
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.
Low metadata risk · Allow agent install in a sandbox or low-risk workspace, then promote after one successful narrow task.
💡 All-in-one AI framework for semantic search, LLM orchestration and language model workflows
Low metadata risk · Allow agent install in a sandbox or low-risk workspace, then promote after one successful narrow task.
Developer-friendly OSS embedded retrieval library for multimodal AI. Search More; Manage Less.
Low metadata risk · Allow agent install in a sandbox or low-risk workspace, then promote after one successful narrow task.
21 Lessons, Get Started Building with Generative AI
Low metadata risk · Allow agent install in a sandbox or low-risk workspace, then promote after one successful narrow task.
Find related notes and excerpts while writing. Your link building copilot displays relevant content in graph + list view. A local embedding model powers semantic search. Zero setup. No API key.
Review before production · Require human approval before installing into a real workspace.
The AI-native database built for LLM applications, providing incredibly fast hybrid search of dense vector, sparse vector, tensor (multi-vector), and full-text.
Low metadata risk · Review the audit page, then allow agent install in a sandboxed workflow.
Semantic search over videos using Gemini Embedding 2 or Qwen3-VL.
Low metadata risk · Allow agent install in a sandbox or low-risk workspace, then promote after one successful narrow task.
Local persistent memory store for LLM applications including claude desktop, github copilot, codex, antigravity, etc.
Low metadata risk · Review the audit page, then allow agent install in a sandboxed workflow.
Fast Open-Source Search & Clustering engine × for Vectors & Arbitrary Objects × in C++, C, Python, JavaScript, Rust, Java, Objective-C, Swift, C#, GoLang, and Wolfram 🔍
Low metadata risk · Review the audit page, then allow agent install in a sandboxed workflow.
Stop renting your intelligence. Own it with AnythingLLM. Everything you need for a powerful local-first agent experience
Low metadata risk · Allow agent install in a sandbox or low-risk workspace, then promote after one successful narrow task.
A lightning-fast search engine API bringing AI-powered hybrid search to your sites and applications.
Review before production · Require human approval before installing into a real workspace.
MTEB: Massive Text Embedding Benchmark
Low metadata risk · Allow agent install in a sandbox or low-risk workspace, then promote after one successful narrow task.
Jupyter Notebooks to help you get hands-on with Pinecone vector databases
Low metadata risk · Review the audit page, then allow agent install in a sandboxed workflow.
FAQ
These answers are written for both human builders and agents consuming the Registry API.
Start by comparing Qdrant, Dingo, RAG Techniques. OpenAgentSkill ranks them by workflow fit, GitHub adoption, trust score, safety gate, and install readiness.
Yes. Use the linked Registry API prompt to query /api/skills/search with the task: "I need my agent to build a RAG workflow over documents and retrieve reliable context." and retrieve install handoff links for the top results.
No. Start with the highest-fit skill, test it in a sandbox workflow, and add companion skills only when the task needs extra coverage.