Use when building RAG systems, vector databases, or knowledge-grounded AI applications requiring semantic search, document retrieval, or context augmentation.
Use when the user asks to design RAG pipelines, optimize retrieval strategies, choose embedding models, implement vector search, or build knowledge retrieval systems.
Use when building RAG systems, vector databases, or knowledge-grounded AI applications requiring semantic search, document retrieval, or context augmentation.
Designs and implements production-grade RAG systems by chunking documents, generating embeddings, configuring vector stores, building hybrid search pipelines, applying re — from…
RAG pipeline design — document chunking, embedding strategies, retrieval optimization, and answer generation
Open-source embedding database for AI applications. Store embeddings and metadata, perform vector and full-text search, filter by metadata. Simple 4-function API.
RAG-grounded code generation with source citations. Triggers on: grounded code, ground this, cite sources, show me with sources, how do I with attune, reference attune docs,…
Build RAG systems for construction knowledge bases. Create searchable AI-powered construction document systems
Improve RAG corpus quality by adding missing docs, rewriting weak chunks, and filling retrieval gaps.
Curador do corpus RAG. Gerencia adição, organização e manutenção do conhecimento do projeto. Garante qualidade e acessibilidade. — from majiayu000/claude-skill-registry
Curador do corpus RAG. Gerencia adição, organização e manutenção do conhecimento do projeto. Garante qualidade e acessibilidade. — from majiayu000/claude-skill-registry
Production-grade RAG (Retrieval-Augmented Generation) system design patterns from OpenClaw. Use when implementing semantic search, vector retrieval, memory systems, or knowledge…
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…
Automatically applies when building RAG (Retrieval Augmented Generation) systems. Ensures proper chunking strategies, vector database patterns, embedding management, reranking,…
Expert in building Retrieval-Augmented Generation systems. Masters embedding models, vector databases, chunking strategies, and retrieval optimization for LLM application — from…
Expert in building Retrieval-Augmented Generation systems. Masters embedding models, vector databases, chunking strategies, and retrieval optimization for LLM application — from…
Expert in building Retrieval-Augmented Generation systems. Masters embedding models, vector databases, chunking strategies, and retrieval optimization for LLM application — from…
Expert in building Retrieval-Augmented Generation systems. Masters embedding models, vector databases, chunking strategies, and retrieval optimization for LLM application — from…
Filesystem RAG benchmarks: corpus/, train.json, evaluate_rag.py (RAGAS quality). Not for prod monitoring, latency/throughput benchmarking (use rag-perf), or evals outside this…
Batch-evaluate rag index quality with pre-registered probes — search results + the active AI session's diagnosis and tuning suggestions.
Evaluate retrieval and citation behavior for RAG pipelines from deterministic JSONL fixtures. Use when an agent needs offline Recall@K, reciprocal rank, context precision,…
Measure RAG pipeline quality with context precision/recall, faithfulness, answer relevancy, and groundedness using Ragas and DeepEval, with golden datasets and pass/fail…
N-hop graph exploration around a specific entity — reports what is connected to what as a visual/table.
Create a rag index snapshot tarball (vec.db + graph/ + manifest + settings + compatibility header). Example utterances "make a rag snapshot", "rag-export ~/Downloads/foo.tar.gz",…
Facebook's library for efficient similarity search and clustering of dense vectors. Supports billions of vectors, GPU acceleration, and various index types (Flat, IVF, HN — from…
Record 👍/👎 feedback on the most recent search results to improve the quality of future searches. Example utterances "this result was good", "rag feedback positive", "that last…
Aggregates accumulated search feedback to report what is searched most, which sources match well, and weak spots (sources with many 👎), and proposes improvements.
Scrape external documents (Confluence, Google Drive, Notion, etc.) via installed official connectors and put them into the local index.
Keep a retrieval corpus current as source documents change, with incremental updates, deletion propagation, and a stated staleness budget.
Full usage guide for the rag plugin — one-line concept, quick start, index of 17 skills, FAQ, troubleshooting, Windows/macOS install.
Hybrid search combining semantic and keyword retrieval for RAG pipelines. Implement BM25 + dense vector search with fusion strategies.
Design and implement Retrieval-Augmented Generation systems — chunking strategy, embedding selection, vector store setup, retrieval pipeline, re-ranking, and evaluation
RAG (Retrieval-Augmented Generation) implementation workflow covering embedding selection, vector database setup, chunking strategies, and retrieval optimization.
Build Retrieval-Augmented Generation (RAG) systems for LLM applications with vector databases and semantic search.
Build Retrieval-Augmented Generation (RAG) systems for LLM applications with vector databases and semantic search.
Build Retrieval-Augmented Generation (RAG) systems for LLM applications with vector databases and semantic search.
Retrieval-Augmented Generation patterns including chunking, embeddings, vector stores, and retrieval optimization Use when: rag, retrieval augmented, vector search, embed — from…
Build Retrieval-Augmented Generation (RAG) systems for LLM applications with vector databases and semantic search.
RAG (Retrieval-Augmented Generation) implementation workflow covering embedding selection, vector database setup, chunking strategies, and retrieval optimization.
Build Retrieval-Augmented Generation (RAG) systems for LLM applications with vector databases and semantic search.
Build Retrieval-Augmented Generation (RAG) systems for LLM applications with vector databases and semantic search.
Build Retrieval-Augmented Generation (RAG) systems for LLM applications with vector databases and semantic search.
Build Retrieval-Augmented Generation (RAG) systems for LLM applications with vector databases and semantic search.
Build Retrieval-Augmented Generation (RAG) systems for LLM applications with vector databases and semantic search.
Implement retrieval-augmented generation systems. Use when building knowledge-intensive applications, document search, Q&A systems, or need to ground LLM responses in external…
Apply a snapshot tarball sent by a colleague to the current project's `.noory/rag/`. Auto-verifies compatibility.
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,…
Build RAG (Retrieval-Augmented Generation) knowledge bases for businesses — turn documents, SOPs, policies, product manuals into AI assistants that answer questions accurately.
Composite RAG maintenance skill — runs a full retrieval index audit end-to-end: measure quality, find corpus gaps, detect stale chunks, and curate (add missing docs, rewrite weak…
Vertex AI RAG Engine integration patterns for grounding agent responses in private data sources including corpus management, retrieval tool creation, and citation extraction.
Chunking strategies, embedding model selection, hybrid search, reranking, eval metrics
Performance benchmarking for a deployed NVIDIA RAG Blueprint server: profiling pass + aiperf load test driven by a single YAML config.
Transform textbook content based on the 10-dimension user profile to provide personalized learning experiences. Agent: AIEngineer
Managed vector database for production AI applications. Fully managed, auto-scaling, with hybrid search (dense + sparse), metadata filtering, and namespaces.
Details on the Retrieval Augmented Generation pipeline, Ingestion, and Vector Search. — from param087/agent-ml-skills
Build retrieval-augmented generation systems that ground LLM responses in your data — from eddiebelaval/squire
Build retrieval-augmented generation systems that ground LLM responses in your data — from majiayu000/claude-skill-registry
Conception de pipelines RAG (Retrieval-Augmented Generation). Se déclenche avec "RAG", "retrieval augmented", "vector database", "embeddings", "knowledge base", "Pinecone — from…
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…
Add a rag evaluation probe (pre-registered question) — register a frequently asked question in `.noory/rag/probes.json`.