60 Claude Code skills tagged Rag. Browse all AI provider, model, or runtime-related skills in the open ClaudSkills registry — free to install, one-click via the desktop app.
Showing all 60 skills.
Document chunking with multiple strategies including semantic, recursive, and fixed-size chunking
general
Batch embedding generation with caching, rate limiting, and multiple provider support
engineering
RAG 시스템 품질 평가 및 개선을 위한 스킬입니다. RAGAS 기반 LLM-as-Judge 평가, 사용자 페르소나 시뮬레이션, 합성 데이터 생성, 평가 결과 저장 및 분석 기능을 제공합니다.
general
RAG systems analyst and architect skill. Collects and clarifies requirements through structured dialogue, then transforms unstructured business or developer descriptions into forma
engineering
Cross-encoder reranking and MMR diversity filtering for improved retrieval quality
general
Retrieval-Augmented Generation patterns for grounded LLM responses. Use when building RAG pipelines, embedding documents, implementing hybrid search, contextual retrieval, HyDE, ag
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LlamaIndex agent and query engine setup for RAG-powered agents
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Query expansion, HyDE, and multi-query generation for improved retrieval
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Medical AI paper optimization for AI search engines (Perplexity, ChatGPT web, Elicit, Consensus, SciSpace) and RAG-based literature tools. Applies when drafting or reviewing titles
science
Add Qdrant embedding support to v3 WordPress components for RAG chatbot. Implements component-level content chunking for searchable, structured embeddings. Use when adding embeddin
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Build semantic vector search systems with AgentDB for intelligent document retrieval, RAG applications, and knowledge bases using embedding-based similarity matching
general
Use when designing or auditing a Salesforce data architecture to support AI features — Einstein, Agentforce, Data Cloud, or custom ML models. Covers field-level data quality requir
engineering
Retrieval-Augmented Generation patterns including chunking, embeddings, vector stores, and retrieval optimization Use when: rag, retrieval augmented, vector search, embeddings, sem
general
Interactive Archon integration for knowledge base and project management via REST API. On first use, asks for Archon host URL. Use when searching documentation, managing projects/t
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Interactive Archon integration for knowledge base and project management via REST API. On first use, asks for Archon host URL. Use when searching documentation, managing projects/t
general
Automatically intercepts and optimizes prompts using the prompt-learning MCP server. Learns from performance over time via embedding-indexed history. Uses APE, OPRO, DSPy patterns.
general
Scaffold, structure, and deploy the Physical AI textbook in Docusaurus with book-aware content and RAG-ready exports. Use when creating or updating the Docusaurus site, adding chap
general
An official MCP server for the Chroma open-source embedding database. Enables AI agents to create collections, add documents, perform vector search, full-text search, and metadata
general
Embedding backends (InsightFace/PyTorch+ONNXRuntime vs TensorRT). Use when optimizing embedding throughput or debugging drift/fallbacks.
engineering
Generate and manage text embeddings for semantic search, clustering, and similarity tasks
tools
Embedding Models — выбор, настройка и отладка моделей эмбеддингов. ИСПОЛЬЗУЙ когда выбираешь модель embeddings (E5, BGE, Jina, Giga), настраиваешь ONNX backend, исправляешь dimensi
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Optimizing vector embeddings for RAG systems through model selection, chunking strategies, caching, and performance tuning. Use when building semantic search, RAG pipelines, or doc
engineering
Implement reusable embedding functions using Gemini embedding models via LangChain with proper error handling and batching for sitemap-crawled content.
general
Guide to selecting and optimizing embedding models for vector search applications.
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Select and optimize embedding models for semantic search and RAG applications. Use when choosing embedding models, implementing chunking strategies, or optimizing embedding quality
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[STUB - Not implemented] Asymmetric embedding strategy with RETRIEVAL_DOCUMENT for ingestion and RETRIEVAL_QUERY for queries. PROACTIVELY activate for: [TODO: Define on implementat
general
Build RAG systems and semantic search with Gemini embeddings (gemini-embedding-001). 768-3072 dimension vectors, 8 task types, Cloudflare Vectorize integration. Prevents 13 documen
engineering
Image embedding size limits in markdown — base64 bloats 33%, use file references for images over 50KB
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Build and query vector stores with LangChain 1.0 without getting burned by flipped score semantics, embedding-dim mismatches, reranker quirks, and chunk-splitter bugs. Use when bui
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Use when you need to reduce LLM API spend, control token usage, route between models by cost/quality, implement prompt caching, or build cost observability for AI features. Trigger
engineering
Queries Notion databases and pages via the Notion API v1, then renders content blocks into PDF via WeasyPrint. Extracts text, tables, and inline images and preserves heading hierar
general
Use local Codex/Claude JSONL logs as evidence, then produce a human-written daily diary summary and write it into Obsidian. Keep directory filtering (for example rag-flow/rag-recal
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pgvector performance optimization: HNSW vs IVFFlat index selection and tuning, ef_search / m / ef_construction parameters, iterative scanning for filtered queries, scalar and binar
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Use when managing prompts in production at scale: versioning prompts, running A/B tests on prompts, building prompt registries, preventing prompt regressions, or creating eval pipe
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Qdrant Operations — управление коллекциями Qdrant, sparse vectors, snapshots. ИСПОЛЬЗУЙ когда создаёшь/настраиваешь коллекции Qdrant, мигрируешь с ChromaDB, настраиваешь named vect
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Use when the user asks to design RAG pipelines, optimize retrieval strategies, choose embedding models, implement vector search, or build knowledge retrieval systems.
general
Guide an agent through property search, buyer/renter preference capture, and evidence-backed shortlist notes from structured listing data.
science
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 noteb
general
Expert in building Retrieval-Augmented Generation systems. Masters embedding models, vector databases, chunking strategies, and retrieval optimization for LLM applications. Use whe
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Facebook's library for efficient similarity search and clustering of dense vectors. Supports billions of vectors, GPU acceleration, and various index types (Flat, IVF, HNSW). Use f
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Hybrid search combining semantic and keyword retrieval for RAG pipelines. Implement BM25 + dense vector search with fusion strategies.
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Design and implement Retrieval-Augmented Generation systems — chunking strategy, embedding selection, vector store setup, retrieval pipeline, re-ranking, and evaluation
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RAG (Retrieval-Augmented Generation) implementation workflow covering embedding selection, vector database setup, chunking strategies, and retrieval optimization.
general
Интеграция RAG (Retrieval Augmented Generation) с xAI Grok Collections и Google Gemini. Используй этот skill когда нужно добавить AI-чат с базой знаний, настроить RAG систему, инте
general
RAG architecture: embeddings, chunking strategies, hybrid search (BM25 + vector), reranking, CRAG/self-correcting, multi-hop reasoning, evaluation metrics. Triggers: RAG, embedding
engineering
Managed vector database for production AI applications. Fully managed, auto-scaling, with hybrid search (dense + sparse), metadata filtering, and namespaces. Low latency (<100ms p9
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Details on the Retrieval Augmented Generation pipeline, Ingestion, and Vector Search.
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Conception de pipelines RAG (Retrieval-Augmented Generation). Se déclenche avec "RAG", "retrieval augmented", "vector database", "embeddings", "knowledge base", "Pinecone", "Chroma
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트리거: "RAG 파이프라인", "벡터 검색", "문서 임베딩", "RAG 만들어줘", "retrieval augmented generation" 수행: 문서 청킹 전략 설계 → 임베딩 → 벡터 DB 저장 → 검색 파이프라인 코드 생성 출력: 완전한 RAG 파이프라인 코드 (LangChain 또는 LlamaIndex 기반
general
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 fi
engineering
Framework for state-of-the-art sentence, text, and image embeddings. Provides 5000+ pre-trained models for semantic similarity, clustering, and retrieval. Supports multilingual, do
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Build Retrieval Augmented Generation (RAG) pipelines with vector databases, embeddings, and context-aware responses. Adapted from Anthropic's Claude Cookbooks.
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Research GitHub, GitLab, and Bitbucket repositories using DeepWiki MCP server. Use when exploring unfamiliar codebases, understanding project architecture, or asking questions abou
science
Vector database expert for embeddings, similarity search, RAG patterns, and indexing strategies
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Audit Vector DB coverage -- compares the live filesystem manifest against the ChromaDB index to identify coverage gaps.
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Removes stale and orphaned chunks from the ChromaDB vector store for files that have been deleted or renamed. Use after files are removed or moved to keep the vector index in sync
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Ingests repository files into the ChromaDB vector store. Builds or updates the vector index from a manifest or directory scan using ingest.py. Use when new files need to be indexed
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Interactively initializes the Vector DB plugin. Guided discovery asks which folders to index, confirms the manifest, then scaffolds vector_profiles.json for high-performance In-Pro
general
Start the Native Python ChromaDB background server. Use when semantic search returns connection refused on port 8110, or when the user wants to enable concurrent agent read/writes.
engineering
Semantic search skill for retrieving code and documentation from the ChromaDB vector store. Use when you need concept-based search across the repository (Phase 2 of the 3-phase sea
general