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…
Prüft Rahmenverträge, Einzelabrufe, Statements of Work und tatsächliche Projektsteuerung im Sozialversicherungsstatus Prüfer.
Modélise le raisonnement juridique d'un magistrat français pour l'analyse de dossiers civils. Utiliser ce skill pour analyser un litige et identifier les questions juridi — from…
Activate autonomous Ralph Wiggum loop mode for iterative task completion. Use when you have a well-defined task with clear completion criteria that benefits from persistent,…
Iterative development loop methodology for autonomous AI work. Configure self-correcting coding loops that iterate until completion criteria are met, integrate with Archon for…
Execute an autonomous development loop that picks one task per iteration, implements it, verifies it, and commits the result — each iteration in a fresh context window.
Autonomous feature development - setup and execution. Triggers on: ralph, set up ralph, run ralph, run the loop, implement tasks.
Long-running iterative development loops with pacing control and verifiable progress. Use when tasks require multiple iterations, many discrete steps, or periodic reflection with…
P2P chat between Claude Code instances using real-a2a. Use when chatting with other Claudes, joining a P2P room, or communicating agent-to-agent.
RFC-driven multi-agent DAG execution pattern with quality gates, merge queues, and work unit orchestration.
AgentDB integration specialist for RAN ML systems with vector storage, pattern recognition, and distributed training coordination.
Causal inference and discovery for RAN optimization with Graphical Posterior Causal Models (GPCM), intervention effect prediction, and causal relationship learning.
DSPy-based mobility optimization with temporal patterns, handover management, and 15% improvement target.
Comprehensive RAN optimization with swarm coordination, cognitive consciousness, and 15-minute closed-loop autonomous cycles.
Reinforcement learning engineering for RAN systems with policy gradients, experience replay, and AgentDB integration.
Raxol terminal framework for TUI apps and AI agents in Elixir. TRIGGER when: code imports Raxol modules (Raxol.Agent, Raxol.Headless, Raxol.Core), mix.exs lists :raxol or…
Symphony: the Raxol tracker-driven coding-agent orchestrator (raxol_symphony, pre-alpha). Turns tracker issues into autonomous agent runs in isolated workspaces, surfaces evidence…
專責處理 RIF (Required Behavior Frame) 類型的需求。讀取規格目錄結構，生成/審查 Event Handler 設計與實作。支援冪等性、重試、死信佇列。
Search channels, inspect threads, move files, and send or edit Slack messages through an agent-oriented CLI with structured output.