RAG

knowledge-ops is a knowledge-ops skill for managing a multi-layer knowledge base across local files, MCP memory, vector stores, and Git repos. Use it to ingest, organize, sync, deduplicate, and retrieve notes, conversations, docs, and project facts with clear storage boundaries.

vector-index-tuning helps tune vector search indexes for latency, recall, and memory. Use it to choose index types, adjust HNSW settings, and compare quantization options for RAG workflows.

rag-implementation is a practical skill for planning RAG systems with vector databases, embeddings, retrieval patterns, and grounded-answer workflows. Use it to compare stack options, shape architecture decisions, and guide install and usage for document Q&A, knowledge assistants, and semantic search.

similarity-search-patterns helps you choose distance metrics, index types, and hybrid retrieval patterns for semantic search and RAG workflows. Use it to plan production vector search tradeoffs around recall, latency, and scale.

The hybrid-search-implementation skill shows how to combine vector and keyword retrieval with RRF, linear fusion, reranking, and cascade patterns for RAG and search systems.

langchain-architecture is a design guide for building LangChain 1.x and LangGraph applications. Use it to choose between chains, agents, retrieval, memory, and stateful orchestration patterns before implementation.

embedding-strategies helps you choose and optimize embedding models for semantic search and RAG workflows, with practical guidance on chunking, model tradeoffs, multilingual content, and retrieval evaluation.

azure-search-documents-py is the Python Azure AI Search skill for backend development, covering install, auth, index design, vector search, hybrid search, semantic ranking, and agentic retrieval. Use the azure-search-documents-py skill when you need practical guidance from setup to working query patterns.

reunion is a local-first skill for building memorial chat agents from memories, chat logs, diaries, photos, and oral recollections, with Memory and Persona analysis, CLI use, and MCP server support for Agent Orchestration.