352 skills · sorted by relevance
Designing search engines and vector DBs for full-text, vector, and hybrid retrieval, including permission-aware retrieval for multi-tenant or per-role corpora. Use for search design, index optimization, the RAG retrieval layer, or deciding where ACL filtering belongs in the query path.
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Search for vectors using semantic similarity. Requires authentication. Use for Agentuity cloud platform operations
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AgentDB Vector Search Optimization operates on 3 fundamental principles:
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Advanced AgentDB Vector Search Implementation operates on 3 fundamental principles:
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Store embeddings beside application data in Postgres, create vector indexes, and query nearest neighbors for semantic search, RAG, recommendations, or agent memory retrieval.
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Semantic code search using Phase 1 vector embeddings and Phase 2 hybrid search.
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Pinecone serverless vector database -- index management, vector operations, metadata filtering, namespaces, hybrid search, inference API
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Padrões de Retrieval-Augmented Generation incluindo chunking, embeddings, vector stores e otimização de recuperação. Use quando: rag, retrieval augmented, vector search, embeddings, semantic search.
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Spectral vector search using graph Laplacian eigenstructure. Use when cosine/L2 similarity misses latent structure in your embeddings.
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Implement efficient similarity search with vector databases. Use when building semantic search, implementing nearest neighbor queries, or optimizing retrieval performance.
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Expert in vector databases, embedding strategies, and semantic search implementation. Masters Pinecone, Weaviate, Qdrant, Milvus, and pgvector for RAG applications, recommendation systems, and similar
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Utilizes the Vectorworks RAG + MCP server to search and retrieve Python and VectorScript API documentation.
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Retrieval-Augmented Generation patterns including chunking, embeddings, vector stores, and retrieval optimization Use when: rag, retrieval augmented, vector search, embeddings, semantic search.
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Expert in building Retrieval-Augmented Generation systems. Masters embedding models, vector databases, chunking strategies, and retrieval optimization for LLM applications. Use when: building RAG, vector search, embeddings, semantic search, document retrieval.
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Build search applications with vector, hybrid, and semantic search capabilities.
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Build search applications with vector, hybrid, and semantic search capabilities.
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Build search applications with vector, hybrid, and semantic search capabilities.
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Build search applications with vector, hybrid, and semantic search capabilities.
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Search personal knowledge bases, notes, docs, and meeting transcripts locally using qmd — a hybrid retrieval engine with BM25, vector search, and LLM reranking. Supports CLI and MCP integration.
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Converts documents into clean, chunked datasets suitable for embeddings and vector search. Produces chunked JSONL files with metadata, deduplication logic, and quality checks. Use when preparing "training data", "vector datasets", "document processing", or "embedding data".
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Process satellite imagery and map data for geospatial research. Helps align coordinate systems and image grids, calculate vegetation and terrain measures, summarize data by area, and analyze road networks while preserving masks and source information.
Find context from past coding sessions in Hindsight long-term memory. Reuse memories already loaded for the session, search for missing details, and reason over accumulated records when answering questions about decisions, project context, or preferences.
Build AI applications with LangChain, including chatbots with conversation history and systems that search documents before answering. The skill provides examples for connecting models, calling tools, retrieving information, and tracing application runs.
Find and remove stored content from cognee using its SDK, HTTP API, or CLI. Inspect documents before deletion, choose document or dataset removal, or keep raw files while clearing derived graph relationships and search vectors.