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Found 35 Skills
Azure AI Search SDK for Python. Use for vector search, hybrid search, semantic ranking, indexing, and skillsets. Triggers: "azure-search-documents", "SearchClient", "SearchIndexClient", "vector search", "hybrid search", "semantic search".
Implement optimal chunking strategies in RAG systems and document processing pipelines. Use when building retrieval-augmented generation systems, vector databases, or processing large documents that require breaking into semantically meaningful segments for embeddings and search.
Optimize vector index performance for latency, recall, and memory. Use when tuning HNSW parameters, selecting quantization strategies, or scaling vector search infrastructure.
Implement Retrieval-Augmented Generation (RAG) systems with LangChain4j. Build document ingestion pipelines, embedding stores, vector search strategies, and knowledge-enhanced AI applications. Use when creating question-answering systems over document collections or AI assistants with external knowledge bases.
MSW search integration — (1) vector search for API docs and implementation guides (msw-guide-mcp or curl against mlua_Document_Retriever / mlua_API_Retriever), (2) REST API search for resources (sprite / animation / sound / resource pack / avatar). Use for 'find details, examples, or related APIs not in .d.mlua', 'need a SpriteRUID', 'monster sprite', 'background image', 'find a sound', 'avatar rendering', etc. Keywords: document search, API details, examples, guide, retriever, resource, sprite, animation, sound, RUID, resource pack, avatar.
Retrieval-Augmented Generation patterns including chunking, embeddings, vector stores, and retrieval optimization Use when: rag, retrieval augmented, vector search, embeddings, semantic search.
Anthropic's Contextual Retrieval technique for improved RAG. Use when chunks lose context during retrieval, implementing hybrid BM25+vector search, or reducing retrieval failures.
Provision a dedicated PolarDB-X distributed database instance instantly with no auth required. Each instance is a full 2C4G standard edition with MySQL compatibility, distributed transactions, and vector search.
Expert knowledge for Azure AI Search development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when designing indexes, skillsets, vector/semantic search, indexers, private endpoints, or RAG apps, and other Azure AI Search related development tasks. Not for Azure Cosmos DB (use azure-cosmos-db), Azure Data Explorer (use azure-data-explorer), Azure Synapse Analytics (use azure-synapse-analytics).
Guides MongoDB users through implementing and optimizing Atlas Search (full-text), Vector Search (semantic), and Hybrid Search solutions. Use this skill when users need to build search functionality for text-based queries (autocomplete, fuzzy matching, faceted search), semantic similarity (embeddings, RAG applications), or combined approaches. Also use when users need text containment, substring matching ('contains', 'includes', 'appears in'), case-insensitive or multi-field text search, or filtering across many fields with variable combinations. Provides workflows for selecting the right search type, creating indexes, constructing queries, and optimizing performance using the MongoDB MCP server.
Vector database selection, embedding storage, approximate nearest neighbor (ANN) algorithms, and vector search optimization. Use when choosing vector stores, designing semantic search, or optimizing similarity search performance.
Provides expertise on Chroma vector database integration for semantic search applications. Use when the user asks about vector search, embeddings, Chroma, semantic search, RAG systems, nearest neighbor search, or adding search functionality to their application.