MCP CheckClaude skills for RAG
50 of 246 public skills on GitHub that mention RAG: skills named for it first, then by stars, at most 3 from one repository. Each links to its folder; copy it into ~/.claude/skills/ to use it.
rag implementation
Build Retrieval-Augmented Generation (RAG) systems for LLM applications with vector databases and semantic search. Use when implementing knowledge-grounded AI, building document Q&A systems, or integrating LLMs with external knowledge bases.
wshobson/agents★ 42kMITFolder:
rag-implementationqdrant vector search
High-performance vector similarity search engine for RAG and semantic search. Use when building production RAG systems requiring fast nearest neighbor search, hybrid search with filtering, or scalable vector storage with Rust-powered performance.
Orchestra-Research/AI-Research-SKILLs★ 14kMITFolder:
qdrantrag architect
Designs and implements production-grade RAG systems by chunking documents, generating embeddings, configuring vector stores, building hybrid search pipelines, applying reranking, and evaluating retrieval quality. Use when building RAG systems, vector…
Jeffallan/claude-skills★ 12kMITFolder:
rag-architecthunt rag vector
Hunt vector-store / embedding-layer weaknesses in RAG pipelines (OWASP LLM08 Vector and Embedding Weaknesses) — persistent corpus poisoning that survives across sessions and users (distinct from one-shot indirect prompt injection, which is owned by…
elementalsouls/Claude-BugHunter★ 5.3kMITFolder:
hunt-rag-vectorrag architecture review
Review an existing Retrieval-Augmented Generation system and find why it underperforms. Use when asked to review or audit a RAG pipeline, diagnose wrong/ungrounded answers from a 'chat with your docs' feature, or improve an already-built knowledge…
mohitagw15856/pm-claude-skills★ 1.5kMITFolder:
rag-architecture-reviewrag design doc
Design a Retrieval-Augmented Generation system end to end. Use when asked to design a RAG pipeline, a 'chat with your docs' feature, a knowledge assistant, or to debug why a RAG system gives wrong/ungrounded answers. Produces a RAG design doc — ingestion &…
mohitagw15856/pm-claude-skills★ 1.5kMITFolder:
rag-design-docrag observability evals
Monitor and evaluate RAG systems with retrieval quality metrics, groundedness checks, hallucination detection, and continuous regression testing.
BagelHole/DevOps-Security-Agent-Skills★ 1.2kMITFolder:
rag-observability-evalsvector database ops
Deploy, manage, and optimize vector databases for AI applications. Covers Qdrant, Weaviate, pgvector, and Pinecone — collection management, indexing strategies, backup, and performance tuning for production RAG and semantic search workloads.
BagelHole/DevOps-Security-Agent-Skills★ 1.2kMITFolder:
vector-database-opsrag infrastructure
Build and operate Retrieval-Augmented Generation (RAG) infrastructure with vector stores, embedding pipelines, and hybrid search. Covers ingestion, chunking strategies, reranking, and production deployment patterns.
BagelHole/DevOps-Security-Agent-Skills★ 1.2kMITFolder:
rag-infrastructurerag eval
Use when the user has a RAG (Retrieval-Augmented Generation) system and wants to evaluate its quality — separating retrieval issues from generation issues. Also use when the user mentions RAG evaluation, faithfulness checking, hallucination detection in…
agentscope-ai/OpenJudge★ 983Apache-2.0Folder:
05-rag-evaljourney rag
Use as the RAG build stage of the Butterbase journey. Implements the RAG section of 02-plan.md by delegating to rag-dev. Calls manage_rag_content (create_collection, ingest_document). Skipped if the plan has no knowledge-base feature.
butterbase-ai/butterbase-skills★ 535MITFolder:
journey-ragrag dev
Use when building knowledge bases, ingesting documents, running semantic search, or adding LLM-synthesized Q&A over private content with Butterbase RAG
butterbase-ai/butterbase-skills★ 535MITFolder:
rag-devusing vector databases
Vector database implementation for AI/ML applications, semantic search, and RAG systems. Use when building chatbots, search engines, recommendation systems, or similarity-based retrieval. Covers Qdrant (primary), Pinecone, Milvus, pgvector, Chroma,…
ancoleman/ai-design-components★ 525MITFolder:
using-vector-databasesagentsop multi tenant rag
Security-first SOP for multi-tenant RAG systems. Activate when a calling agent is building, reviewing, or debugging any retrieval pipeline whose vector store is shared across more than one user, organisation, workspace, customer, or permission scope.…
agentsope/SkillAlchemy★ 459MITFolder:
agentsop-multi-tenant-ragmcp local rag
Searches, saves, and maintains a local document index through a local RAG MCP server. Use when user says "search my docs", "save this page", "read around that chunk", "sync my index", or invokes `npx mcp-local-rag`.
shinpr/mcp-local-rag★ 425MITFolder:
mcp-local-ragAgentDB Vector Search
Implement semantic vector search with AgentDB for intelligent document retrieval, similarity matching, and context-aware querying. Use when building RAG systems, semantic search engines, or intelligent knowledge bases.
Microck/ordinary-claude-skills★ 408Licence: see repoFolder:
agentdb-vector-searchrag construction
Build RAG systems for construction knowledge bases. Create searchable AI-powered construction document systems
datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction★ 375MITFolder:
rag-constructionvector search
Implement semantic vector search for construction data. Build AI-powered search using embeddings and vector databases (Qdrant, ChromaDB) for intelligent querying of specifications, standards, and project documents.
datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction★ 375MITFolder:
vector-searchrag
Implements document chunking, embedding generation, vector storage, and retrieval pipelines for Retrieval-Augmented Generation systems. Use when building RAG applications, creating document Q&A systems, or integrating AI with knowledge bases.
giuseppe-trisciuoglio/developer-kit★ 366MITFolder:
raglangchain4j rag implementation patterns
Provides Retrieval-Augmented Generation (RAG) implementation patterns with LangChain4j for Java. Generates document ingestion pipelines, embedding stores, vector search, and semantic search capabilities. Use when building chat-with-documents systems,…
giuseppe-trisciuoglio/developer-kit★ 366MITFolder:
langchain4j-rag-implementation-patternslangchain4j vector stores configuration
Provides configuration patterns for LangChain4J vector stores in RAG applications. Use when building semantic search, integrating vector databases (PostgreSQL/pgvector, Pinecone, MongoDB, Milvus, Neo4j), implementing embedding storage/retrieval, setting up…
giuseppe-trisciuoglio/developer-kit★ 366MITFolder:
langchain4j-vector-stores-configurationrag quality testing
Use this skill when you need evidence-bounded grounding, relevance, completeness, citation support, abstention, and answer-level evidence in RAG outputs; triggers include RAG 质量 and RAG quality.
naodeng/awesome-qa-skills★ 288Licence: see repoFolder:
rag-quality-testingrag retrieval testing
Use this skill when you need evidence-bounded query variants, chunking, filters, recall/precision proxies, ranking, freshness, and retrieval evidence; triggers include 检索结果 and retrieval result.
naodeng/awesome-qa-skills★ 288Licence: see repoFolder:
rag-retrieval-testingrag data exposure audit
Assesses document authorization, tenant isolation, raw-chunk fields, citations, caches, exports, and sensitive-source leakage in retrieval systems. Use when an authorized RAG assessment must determine what data each identity can retrieve or disclose.
zero-lab-ai/skills★ 284Apache-2.0Folder:
rag-data-exposure-auditrag surface recon
Maps an authorized retrieval system's sources, connectors, chunking, embeddings, indexes, filters, ranking, caches, citations, and exposed retrieval fields. Use when scoping a RAG security assessment or explaining observed retrieval behavior.
zero-lab-ai/skills★ 284Apache-2.0Folder:
rag-surface-reconrag and memory
Patterns for Retrieval-Augmented Generation (RAG) and agent memory systems. Retrieves only relevant context, prevents context bloat, and maintains coherent state across sessions.
DevelopersGlobal/ai-agent-skills★ 216MITFolder:
rag-and-memorydspy rag pipeline
Use for RAG pipelines, retrieval augmented generation, ColBERTv2, context retrieval, multi-hop RAG, and grounded DSPy answers.
OmidZamani/dspy-skills★ 124MITFolder:
dspy-rag-pipelinegraph rag knowledge expert
Expert guide for Knowledge Graphs, GraphRAG, Microsoft GraphRAG, Neo4j Text2Cypher, multi-hop relational retrieval, and hybrid vector-graph search / Panduan ahli Knowledge Graph, GraphRAG, dan pencarian relasional multi-hop.
roedyrustam/vibes-plug★ 116MITFolder:
graph-rag-knowledge-expertvector db rag expert
Expert guide for high-performance Vector Databases, Deep RAG architectures, pgvector 0.8+ HNSW, Reciprocal Rank Fusion (RRF), Cross-Encoder Re-ranking, and Late Chunking / Panduan ahli Vector DB, arsitektur Deep RAG, pgvector HNSW, RRF, dan Re-ranking.
roedyrustam/vibes-plug★ 116MITFolder:
vector-db-rag-expertlibrary rag
Semantic search over a personal library using Nemotron-3-Embed-1B embeddings + sqlite-vec. Index books, documents, any text corpus; query by meaning. Includes EPUB→Markdown conversion and MCP server for auto-available search tools.
moonlight-lupin/agent-skills★ 113MITFolder:
library-ragtesting prompt injection in rag pipelines
Probe RAG applications for prompt injection via poisoned retrieved context and embedding manipulation.
adriannoes/awesome-agentic-ai★ 89MITFolder:
testing-prompt-injection-in-rag-pipelinesrag engineer
Provides Retrieval-Augmented Generation patterns covering embedding models, vector databases, chunking strategies, and retrieval optimization. Use when building RAG systems or when the user mentions RAG, vector search, embeddings, or retrieval-augmented…
tranhieutt/software_development_department★ 74MITFolder:
rag-engineervector database engineer
Provides vector database and semantic search patterns for Pinecone, Weaviate, Qdrant, Milvus, and pgvector in RAG and recommendation systems. Use when implementing vector search or when the user mentions vector database, semantic search, embeddings, or…
tranhieutt/software_development_department★ 74MITFolder:
vector-database-engineermonitor rag quality
Use this to measure and monitor the quality of a RAG (retrieval-augmented generation) pipeline - whether it retrieves the right context and answers faithfully. Trigger on "my RAG gives wrong answers", "is my retrieval any good", "the chatbot makes things…
ContextJet-ai/awesome-llm-observability★ 45Licence: see repoFolder:
monitor-rag-qualitylaravel vector search
Use when implementing semantic/vector search in Laravel 13 with PostgreSQL + pgvector.
Fusengine/claude-plugins★ 34MITFolder:
laravel-vector-searchapi vector db weaviate
Weaviate vector database patterns with weaviate-client v3 -- collection management, vectorizer modules, hybrid search, filtering, generative search (RAG), multi-tenancy, batch imports
agents-inc/skills★ 26MITFolder:
api-vector-db-weaviateapi vector db pinecone
Pinecone serverless vector database -- index management, vector operations, metadata filtering, namespaces, hybrid search, inference API
agents-inc/skills★ 26MITFolder:
api-vector-db-pineconeapi vector db qdrant
Qdrant vector database -- collection management, point operations, payload filtering, named vectors, quantization, recommendations, snapshots
agents-inc/skills★ 26MITFolder:
api-vector-db-qdrantsearchcans rag source curator zh
使用 SearchCans 的地域化 Google 或 Bing 搜索、Reader 提取、文件提取和可选页面截图创建小规模、多样、可作为证据的 RAG 来源清单。适用于 grounding pack、来源策展、知识库入库和预入库证据检查。
SearchCans/searchcans-skills-zh★ 3Apache-2.0Folder:
searchcans-rag-source-curator-zhliteparse
Local document and PDF parsing that returns spatial text with bounding boxes. Use for extracting text from PDFs, DOCX, Office files, and images; running OCR on scans; producing layout-preserved JSON for RAG; batch-ingesting folders of papers; or rendering…
K-Dense-AI/scientific-agent-skills★ 55kMITFolder:
liteparsemarkitdown
Converts heterogeneous documents and selected URIs to Markdown with Microsoft MarkItDown for text analysis, search, and LLM/RAG ingestion. Covers safe local conversion, streams, Office/PDF/data formats, batch workflows, plugins, vision OCR, Azure…
K-Dense-AI/scientific-agent-skills★ 55kMITFolder:
markitdownopen notebook
Organizes research with the self-hosted Open Notebook alternative to NotebookLM. Supports source ingestion (PDFs, web pages, audio, video, and Office documents), cited document chat, text and vector search, notes, custom transformations, and multi-speaker…
K-Dense-AI/scientific-agent-skills★ 55kMITFolder:
open-notebookai engineer
Build production-ready LLM applications, advanced RAG systems, and intelligent agents. Implements vector search, multimodal AI, agent orchestration, and enterprise AI integrations.
sickn33/agentic-awesome-skills★ 49kMITFolder:
ai-engineerai ml
AI and machine learning workflow covering LLM application development, RAG implementation, agent architecture, ML pipelines, and AI-powered features.
sickn33/agentic-awesome-skills★ 49kMITFolder:
ai-mlagent memory systems
Design tenant-scoped memory, retrieval and retention for agents. Use when planning RAG, conversation history, vector stores or remembering across sessions.
sickn33/agentic-awesome-skills★ 49kMITFolder:
agent-memory-systemsembedding strategies
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.
wshobson/agents★ 42kMITFolder:
embedding-strategieshybrid search implementation
Combine vector and keyword search for improved retrieval. Use when implementing RAG systems, building search engines, or when neither approach alone provides sufficient recall.
wshobson/agents★ 42kMITFolder:
hybrid-search-implementationcognee
Use this skill whenever the user asks about Cognee, AI memory, persistent agent memory, self-improving agents, agents learning from feedback, knowledge graphs, graph-based RAG, long-term memory for agents, short-term memory for agents, personalization,…
topoteretes/cognee★ 33kApache-2.0Folder:
cogneeprompt guard
Meta's 86M prompt injection and jailbreak detector. Filters malicious prompts and third-party data for LLM apps. 99%+ TPR, <1% FPR. Fast (<2ms GPU). Multilingual (8 languages). Deploy with HuggingFace or batch processing for RAG security.
Orchestra-Research/AI-Research-SKILLs★ 14kMITFolder:
prompt-guardchroma
Open-source embedding database for AI applications. Store embeddings and metadata, perform vector and full-text search, filter by metadata. Simple 4-function API. Scales from notebooks to production clusters. Use for semantic search, RAG applications, or…
Orchestra-Research/AI-Research-SKILLs★ 14kMITFolder:
chroma
Claude skills for RAG: common questions
What's the best Claude skill for RAG?
There are 246 of them, spread over 111 repositories on GitHub. Named for it and most starred: rag implementation (wshobson/agents, ★ 42k), qdrant vector search (Orchestra-Research/AI-Research-SKILLs, ★ 14k) and rag architect (Jeffallan/claude-skills, ★ 12k). Stars rate the repository a skill sits in, not the skill.
How do I install a Claude skill for RAG?
For RAG, take the rag-implementation folder from wshobson/agents (the folder that holds SKILL.md) into ~/.claude/skills/ to use it in every Claude Code project, or into .claude/skills/ for one project. In the Claude apps, zip that folder and upload it as a custom skill in settings.
Are all these RAG skills in English?
No: searchcans rag source curator zh is written mainly in Chinese, Japanese, Korean or Russian rather than English, though the RAG task is the same. Claude can follow it either way, but read it before you install.
Should I use a skill or an MCP server for RAG?
A skill teaches Claude a way of working; an MCP server gives it new tools, like access to RAG itself. We list 117 MCP servers for RAG if you need the connection.
Are these RAG skills official?
No. None of the 246 RAG skills comes from Anthropic's own skills repository, so all of them are community-made across 111 repositories.
Can I use these RAG skills at work?
Of the 50 RAG skills listed here, 46 sit in repositories under an open licence such as MIT or Apache 2.0, and every one states a licence. Check the licence file before you copy one into company work.
More topics: every “Claude skills for…” page.