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`.
List the registered rag evaluation questions (probes) — print every entry in `.noory/rag/probes.json` as a table.
Remove a rag evaluation question (probe) — delete an item by ID from `.noory/rag/probes.json`. Example utterances "remove probe", "delete the auth-flow probe", "drop this…
RAG-specific prompt engineering techniques and best practices. RAG 專屬提示工程技術與最佳實踐。 Use when: building retrieval-augmented generation pipelines, grounding LLM answers in documents,…
Index quality cleanup — alias merge · community re-detection · community summary generation. Utterance examples "rag rebalancing", "rag-rebalance", "graph cleanup", "entity…
Re-chunk and re-extract entities from only the changed files, then load them into the vector/graph. Example utterances "rag reindex", "rag update", "rag-reindex", "reflect the…
Remove an indexing source — take it out of settings and clean up the chunks/entity mentions belonging to that source.
Use when designing or fixing the retrieval side of a RAG system, choosing chunking strategy (fixed-size / recursive / semantic), implementing hybrid search (BM25 + dense) with RRF…
Search RAG database for relevant content. Use for semantic queries over processed documents, code, or papers.
Framework for state-of-the-art sentence, text, and image embeddings. Provides 5000+ pre-trained models for semantic similarity, clustering, and retrieval.
Guide to the three patterns for sharing a rag index/materials with a team. Example utterances "how to share rag", "share the index with my team", "how do I give it to someone…
RAG-specific best practices for LlamaIndex, ChromaDB, and Celery workers. Covers ingestion, retrieval, embeddings, and performance.
Build Retrieval Augmented Generation (RAG) pipelines with vector databases, embeddings, and context-aware responses. Adapted from Anthropic's Claude Cookbooks.
Report the current rag index status — file/chunk/entity/relation/community counts, embedding model, disk usage.
Build Retrieval-Augmented Generation (RAG) Q&A systems with Claude or OpenAI. Use for creating AI assistants that answer questions from document collections, technical libraries,…
Build Retrieval-Augmented Generation systems to enhance LLMs with external knowledge. Use for question answering, document search, knowledge bases, reducing hallucinations, and…
Universal Ragflow API client for RAG operations. Create datasets, upload documents, run chat queries against knowledge bases. Self-hosted RAG platform integration.
End-to-end runbook for deploying, operating, troubleshooting, and monitoring RAGFlow (runtime ops only).
Ragic integration. Manage Organizations. Use when the user wants to interact with Ragic data.
Automate Ragic tasks via Rube MCP (Composio). Always search tools first for current schemas. — from general/automation
Local-first RAG cache: distill docs into structured Markdown, then index/query with Chroma + hybrid search (vector + keyword).
Rahmenvereinbarungen, Abrufe und Mini-Wettbewerbe steuern: Laufzeit, Hoechstmengen, Abrufmechanik, Rangfolge, Transparenz und Vertragsmanagement: Rahmenvereinbarungen, Abrufe und…
Rahmenvereinbarung und Abrufe der Vergabestelle steuern: prüft Beteiligte, Schätz- und Höchstmenge, Laufzeit, Einzelabruf oder Mini-Wettbewerb, objektive Abrufregeln, Restvolumen…
Rahmenvereinbarung: Internationaler Bezug und Schnittstellen: Rahmenvereinbarung: Internationaler Bezug und Schnittstellen.
Rahmenvereinbarung mit internationalem Bezug prüfen: Schnittstellen, Abrufe, Laufzeit, Höchstmenge, Vertragsänderung, Rechtsschutz und Dokumentation.
Prüft Rahmenverträge, Einzelabrufe, Statements of Work und tatsächliche Projektsteuerung im Sozialversicherungsstatus Prüfer.
Verknüpft Rahmenvertrag, Bestellungen und Leistungsanlagen durch klare Abschlussmechanik, Parteizuordnung, Versionen, Vorrang und Fortgeltung offener Einzelaufträge.