“RAG”

Expert in building Retrieval-Augmented Generation systems.

Rag Knowledge MCP Server by MEOK AI Labs

Academic paper knowledge-base MCP server: PDF ingest, vector search with reranking, KB management.

Patterns for RAG pipelines, vector databases, embedding strategies, context-window management, and LLM orchestration.

Permission-aware RAG for agents — ingest docs, hybrid search, cited answers (hosted API).

Build, modify, or review retrieval-augmented generation systems.

Calypso multimodal RAG for grounded answers from docs, images, charts, and knowledge.

Use when ingesting, searching, citing, or auditing local project knowledge for a heavy-reasoning agent, especially markdown, text, JSON, and other documents…

Chunking strategies, embedding model selection, hybrid search, reranking, eval metrics

Rag Knowledge Graph MCP Server by MEOK AI Labs

PDF, DOCX and scans to Markdown, CSV tables and RAG chunks with OCR.

Ejecuta flujos RAG en Obsidian para responder, resumir, reorganizar o crear notas nuevas con citas trazables.

Build Retrieval-Augmented Generation (RAG) systems for LLM applications with vector databases and semantic search.

Fetch URLs and return clean Markdown for RAG — nav, ads and boilerplate stripped.

Elite workflow for building and optimizing production-grade Retrieval-Augmented Generation (RAG) systems.

Use this skill for public-safe reasoning about RAG and LangChain pipelines, document loading, splitting, embedding, vector stores, retrievers, prompts…

Use when the user asks about internal/private knowledge, documents, policies, or any indexed corpus that should be answered from a knowledge base rather than…

Use local retrieval-augmented generation before web search to ground research in saved knowledge-base documents.

Use local retrieval-augmented generation before web search to ground research in saved sandbox documents.

Preparar um corpus local rastreável e abrir o chat especialista por meio dos módulos e da CLI do projeto.

Use when working on the Orcanos RAG pipeline, query router, chunking, retrieval, or ETL indexing.

RAG pipeline retrieval accuracy and faithfulness benchmark standard

Store embeddings beside application data in Postgres, create vector indexes, and query nearest neighbors for semantic search, RAG, recommendations, or agent…

Use when designing RAG, knowledge assistants, document chat, or retrieval systems.