by affaan-m
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.
by wshobson
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.
by wshobson
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.
by wshobson
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.
by wshobson
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.
by microsoft
azure-search-documents-ts helps backend developers build Azure AI Search solutions with the @azure/search-documents SDK. Use it for index creation, document upload, keyword, vector, hybrid, and semantic search, plus credential and environment setup. It is a practical azure-search-documents-ts guide for backend development.
by microsoft
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.
by microsoft
azure-search-documents-dotnet is a .NET skill for Azure AI Search. It helps backend developers choose the right clients, install the SDK, and apply azure-search-documents-dotnet usage for full-text, semantic, vector, and hybrid search with clear guidance for indexing, querying, and auth.
