by affaan-m
iterative-retrieval is a workflow pattern for progressively refining context retrieval in agentic work. It helps subagents avoid too much or too little context, making it useful for iterative-retrieval usage, install decisions, and iterative-retrieval for Workflow Automation.
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 wshobson
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.
by wshobson
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.
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-ai-contentunderstanding-py is the Python skill for Azure AI Content Understanding. It extracts structured content from documents, images, audio, and video for RAG workflows and automation. Use it when you need reliable multimodal extraction, Azure authentication, and repeatable pipeline-ready output.
