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Found 59 Skills
Command-line interface for ChromaDB - A stateless CLI for managing vector database collections, documents, and semantic search. Designed for AI agents and automation via the ChromaDB HTTP API v2.
Guide for using the Pinecone CLI (pc) to manage Pinecone resources from the terminal. The CLI supports ALL index types (standard, integrated, sparse) and all vector operations — unlike the MCP which only supports integrated indexes. Use for batch operations, vector management, backups, namespaces, CI/CD automation, and full control over Pinecone resources.
Import CSV or Excel files into seekdb vector database and manage collections. Supports automatic vectorization of specified columns using embedding functions. When users need to: (1) Read and preview Excel files, (2) Import CSV/Excel data into seekdb, (3) Create vector collections from tabular data, (4) Vectorize specific text columns for semantic search, (5) Batch insert product/document data with embeddings, (6) Delete collections, or (7) Access sample data files (sample_products.csv/xlsx) for testing - IMPORTANT: sample files are located in this skill's example-data/ directory, you MUST read this skill file first to get the correct path.
Open-source embedding database for AI applications. Store embeddings and metadata, perform vector and full-text search, filter by metadata. Simple 4-function API. Scales from notebooks to production clusters. Use for semantic search, RAG applications, or document retrieval. Best for local development and open-source projects.
Configure Qdrant vector database for GrepAI. Use this skill for high-performance vector search.
Configure LangChain4J vector stores for RAG applications. Use when building semantic search, integrating vector databases (PostgreSQL/pgvector, Pinecone, MongoDB, Milvus, Neo4j), implementing embedding storage/retrieval, setting up hybrid search, or optimizing vector database performance for production AI applications.
Build Retrieval-Augmented Generation (RAG) applications that combine LLM capabilities with external knowledge sources. Covers vector databases, embeddings, retrieval strategies, and response generation. Use when building document Q&A systems, knowledge base applications, enterprise search, or combining LLMs with custom data.
Search, query, and manage Weaviate vector database collections. Use for semantic search, hybrid search, keyword search, natural language queries with AI-generated answers, collection management, data exploration, filtered fetching, data imports from CSV/JSON/JSONL files, create example data and collection creation.
Different techniques to optimize the performance of Qdrant, including indexing strategies, query optimization, and hardware considerations. Use when you want to improve the speed and efficiency of your Qdrant deployment.
SQL Server 2025 and SqlPackage 170.2.70 (October 2025) - Vector databases, AI integration, and latest features
Build AI-first applications with RAG pipelines, embeddings, vector databases, agentic workflows, and LLM integration. Master prompt engineering, function calling, streaming responses, and cost optimization for 2025+ AI development.
3-Phase Knowledge Search strategy for the RLM Factory ecosystem. Auto-invoked when tasks involve finding code, documentation, or architecture context in the repository. Enforces the optimal search order: RLM Summary Scan (O(1)) -> Vector DB Semantic Search -> Grep/Exact Match. Never skip phases.