В каталоге 183 730 навыков в 14 категориях.
Caching strategies across the RAG stack. Semantic caching with GPTCache and LangChain, Redis-based embedding-similarity...
Security patterns for RAG and CAG systems with multi-tenant isolation. Use when building retrieval-augmented or cache-au...
Implement the /chat endpoint that performs agent-based RAG reasoning with proper input validation and response formattin...
Build the FastAPI-based RAG service using OpenAI Agents/ChatKit, Neon Postgres, and Qdrant for the textbook. Use when cr...
Improves the RAG (Retrieval-Augmented Generation) chatbot for the Physical AI & Humanoid Robotics textbook with strict g...
Audit corpus distribution by source type and repo; identify coverage gaps and underindexed topics
Manually improve corpus quality by adding missing docs and filling retrieval gaps
Curador do corpus RAG. Gerencia adição, organização e manutenção do conhecimento do projeto. Garante qualidade e acessib...
Assesses document authorization, tenant isolation, raw-chunk fields, citations, caches, exports, and sensitive-source le...
Production-grade RAG (Retrieval-Augmented Generation) system design patterns from OpenClaw. Use when implementing semant...
Design a Retrieval-Augmented Generation system end to end. Use when asked to design a RAG pipeline, a 'chat with your do...
Automatically applies when building RAG (Retrieval Augmented Generation) systems. Ensures proper chunking strategies, ve...
Use when building knowledge bases, ingesting documents, running semantic search, or adding LLM-synthesized Q&A over priv...
Detect and fix stale chunks (files that changed or were deleted since last indexing)
Expert in building Retrieval-Augmented Generation systems. Masters embedding models, vector databases, chunking strategi...
Use when the user has a RAG (Retrieval-Augmented Generation) system and wants to evaluate its quality — separating retri...
Iterate on RAG systems with structured evals instead of eyeballing. This skill should be used when the user is tuning a...
Set up a controlled RAG evaluation harness. Use when starting a new RAG benchmarking project, adding a new retrieval arc...
Comprehensive guide to evaluating Retrieval-Augmented Generation systems including retrieval metrics, generation quality...
Evaluate retrieval-augmented generation systems component by component - build a query set with labelled relevant passag...
Agent profile for evaluate RAG quality, chunking, retrieval, citations, hallucination risks, freshness, and regression s...
Run one controlled RAG experiment (chunking, embeddings, top-k, rerank, threshold, prompt or LLM) and save config, raw r...
Expert in Retrieval-Augmented Generation systems - knowledge bases, chunking strategies, embedding optimization, and pro...
Implement or repair Retrieval-Augmented Generation systems with measurable grounding quality. Use when the user requests...
Learn what RAG (retrieval-augmented generation) is by building a small one over your own documents, so an AI answers fro...
Hybrid search combining semantic and keyword retrieval for RAG pipelines. Implement BM25 + dense vector search with fusi...
Comprehensive guide for Retrieval-Augmented Generation (RAG) implementation using LangChain. This skill covers the compl...
Retrieval-Augmented Generation patterns including chunking, embeddings, vector stores, and retrieval optimization Use wh...
RAG (Retrieval-Augmented Generation) implementation workflow covering embedding selection, vector database setup, chunki...
AesopIDE with Antigravity-inspired UI"
Build Retrieval-Augmented Generation (RAG) systems for LLM applications with vector databases and semantic search. Use w...
Design, implement, and evaluate retrieval-augmented generation with traceable ingestion, privacy-aware indexing, hybrid...
Build and operate Retrieval-Augmented Generation (RAG) infrastructure with vector stores, embedding pipelines, and hybri...
Embed every file under targets/<name>/sources/ into a ChromaDB collection named target_<name> via the TLX rag module (Go...
Examine what's actually stored in the index for specific items
Grade a RAG system (an AI that answers from your documents) on the two ways it fails. A script checks whether retrieval...
Интеграция RAG (Retrieval Augmented Generation) с xAI Grok Collections и Google Gemini. Используй этот skill когда нужно...
Expert in managing RAG (Retrieval-Augmented Generation) indices for L'Oréal's BTDP infrastructure. **TRIGGER THIS SKILL...
Monitor and evaluate RAG systems with retrieval quality metrics, groundedness checks, hallucination detection, and conti...
RAG 파이프라인 최적화 스킬. 검색 품질, 리랭킹, 쿼리 확장, 하이브리드 검색 관련 작업에서 자동으로 활성화됩니다. retrieval, rerank, embedding, vector search, semantic...
Vertex AI RAG Engine integration patterns for grounding agent responses in private data sources including corpus managem...
Retrieval-Augmented Generation patterns and best practices. Implement chunking, embedding, retrieval, reranking, and gen...
RAG architecture patterns and optimization. Chunking strategies, embedding selection, retrieval tuning, multimodal RAG,...
RAG 아키텍처 — 청킹 전략, 임베딩 모델, 검색 최적화, vector store 연동, advanced RAG, RAGAS 평가. Use when 문서 기반 질의응답 시스템을 만들거나 검색 품질·환각 문제를 개선...
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 singl...
Transform textbook content based on the 10-dimension user profile to provide personalized learning experiences. Agent: A...
Use when building Retrieval-Augmented Generation systems. Covers document chunking, embedding generation, vector indexin...
Build retrieval-augmented generation systems that ground LLM responses in your data
Designs retrieval-augmented generation pipelines for document-based AI assistants. Includes chunking strategies, metadat...
Complete RAG (Retrieval-Augmented Generation) pipeline implementation with document ingestion, vector storage, semantic...
RAG-specific threat model — embedding manipulation, vector store poisoning, retrieval filter bypass, indirect prompt inj...
RAG systems with embeddings, vector stores, retrieval.
RAG pipeline design — chunking, embeddings, retrieval strategies, evaluation, and demo patterns.
RAG guidance — chunking, retrieval tuning, hybrid search, reranking, citations, evaluation, and production RAG.
Evaluate retrieval quality from the local RAG index
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.
Use this skill when you need evidence-bounded grounding, relevance, completeness, citation support, abstention, and answ...
This skill should be used when users ask questions about pod network development, pod smart contract language, pod APIs,...
Consulta ao corpus RAG do projeto. Busca conhecimento em decisoes, documentacao, learnings e padroes armazenados. Use qu...
Cross-encoder reranking and MMR diversity filtering for improved retrieval quality
Retrieval-Augmented Generation patterns for grounded LLM responses. Use when building RAG pipelines, constructing contex...
Use this skill when you need evidence-bounded query variants, chunking, filters, recall/precision proxies, ranking, fres...
Search RAG database for relevant content. Use for semantic queries over processed documents, code, or papers.
Security controls for RAG. Indirect prompt-injection via retrieved documents, PII detection/redaction (Microsoft Presidi...
Secure the trust boundaries RAG adds beyond a plain LLM app. Covers retrieval-time document authorization, tenant isolat...
Review a retrieval augmented generation system for ingestion poisoning, retrieval boundary failures, insecure chunking a...
Detects RAG pipelines that ingest external documents into LLM context without sanitization or trust gating. Use when bui...
Framework for state-of-the-art sentence, text, and image embeddings. Provides 5000+ pre-trained models for semantic simi...
RAG-specific best practices for LlamaIndex, ChromaDB, and Celery workers. Covers ingestion, retrieval, embeddings, and p...
Local RAG (Retrieval-Augmented Generation) with ChromaDB vector store. Ingest books, documents, PDFs, and text files, th...
Maps an authorized retrieval system's sources, connectors, chunking, embeddings, indexes, filters, ranking, caches, cita...
Build Retrieval-Augmented Generation (RAG) Q&A systems with Claude or OpenAI. Use for creating AI assistants that answer...
LeeWay-governed RAG (Retrieval-Augmented Generation) workflow. Use when a task requires rag systems procedures, validati...
Design retrieval-augmented generation pipelines—chunking, embeddings, retrieval, reranking, grounding, and evaluation. U...
Retrieval-Augmented Generation (RAG) implementation - vector databases, embedding models, chunking strategies, retrieval...
Build Retrieval-Augmented Generation systems to enhance LLMs with external knowledge. Use for question answering, docume...
Patterns for wrapping any agent with RAG context from ChromaDB. Use to add persistent memory to imported or external age...
Evaluate RAG pipelines with Ragas — measure faithfulness, answer relevancy, context precision/recall, and noise sensitiv...
RAGAS를 사용하여 LLM 애플리케이션을 체계적으로 평가하는 방법을 제공합니다.
Ragdoll physics skill for joint constraints and hit reactions.
Attacks the error list in one relentless pass — fix, rebuild, repeat, no commentary — until it builds clean; invoke via...
Automate Ragic tasks via Rube MCP (Composio). Always search tools first for current schemas.
Use Ragora MCP tools and REST API to discover, search, and synthesize answers from knowledge bases. Trigger when the use...
Use this repo skill for RAGs, a Streamlit app that builds configurable LlamaIndex RAG agents from natural-language setup...
Build or update a code graph index for C#/.NET repositories using ragsharp-graph.Triggers: build index, update index, re...
Query the ragsharp code graph for declarations, references, callers, callees, dependencies, and line-number evidence.Tri...
RAG 召回评测指标(召回率/准确率/MRR)与切片质量评估方法。当需要量化评测 RAG 检索效果与切片质量时使用。
This prompt instructs an image-editing AI to transform a user-provided face photo into a repeatable, warm, dim, vintage...
Signpost (not a knowledge pack) for the railway RAMS and signalling assurance standards: EN 50126-1:2017 and EN 50126-2:...
Search for Israel Rail train schedules using the railil CLI. Find routes between stations with fuzzy search, filter by d...
Build Rails applications with Active Record, migrations, controllers, strong parameters, and framework conventions.
Rails 7+ framework guardrails, patterns, and best practices for AI-assisted development. Use when working with Rails pro...
Use when working with the Rails CLI — commands, generators, database tasks, test subcommands, rake tasks, flags, and arg...
Expert skill for Ruby on Rails web development across Rails 7.2, 8.0, and 8.1. Covers ActiveRecord (associations, scopes...