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Found 1,637 Skills
Provides Qdrant vector database integration patterns with LangChain4j. Handles embedding storage, similarity search, and vector management for Java applications. Use when implementing vector-based retrieval for RAG systems, semantic search, or recommendation engines.
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.
Call the Recoupable API from the sandbox to fetch artist data, socials, organizations, research, documents and any other platform resource — and to invoke external connector actions (Google Docs / Drive / Sheets edits, Gmail, TikTok, Instagram, etc.) via Recoupable's shared connections. Use whenever you're asked for Recoup data, a Recoupable platform resource, or to read/write something outside Recoup like a Google Doc URL or a spreadsheet. Triggers on phrases like "look up artist", "fetch from recoup", "artist data", "artist socials", "organizations", "artist report", "research", "create new artist", "create artist", "onboard artist", "add artist", "edit this Google Doc", "read this doc", "update the spreadsheet", "send an email", "post on TikTok", "save to Drive", or whenever the user pastes a docs.google.com / drive.google.com / sheets.google.com URL. Always load this before writing curl calls against recoup-api.vercel.app.
Revolut X Telegram notification connector. Use when the user asks to "set up Telegram alerts", "add Telegram bot", "manage Telegram connection", "test Telegram notification", or runs revx connector telegram commands.
Build Retrieval-Augmented Generation (RAG) systems for LLM applications with vector databases and semantic search. Use when implementing knowledge-grounded AI, building document Q&A systems, or integrating LLMs with external knowledge bases.
Implement efficient similarity search with vector databases. Use when building semantic search, implementing nearest neighbor queries, or optimizing retrieval performance.
Redis performance optimization and best practices. Use this skill when working with Redis data structures, Redis Query Engine (RQE), vector search with RedisVL, semantic caching with LangCache, or optimizing Redis performance.
Scaffold a complete Power Apps Code App project with PAC CLI setup, SDK integration, and connector configuration
Data framework for building LLM applications with RAG. Specializes in document ingestion (300+ connectors), indexing, and querying. Features vector indices, query engines, agents, and multi-modal support. Use for document Q&A, chatbots, knowledge retrieval, or building RAG pipelines. Best for data-centric LLM applications.
Use when building RAG systems, vector databases, or knowledge-grounded AI applications requiring semantic search, document retrieval, or context augmentation.
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.
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.