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Found 127 Skills
Semantic search for Marp presentations using vector embeddings. Use when finding relevant slides by topic, retrieving slide content, or exploring presentation materials. Triggers on "find slides about...", "search presentations for...", "get slide content", "what slides cover...", or any Marp/presentation search query.
Configure pgvector extension for vector search in Supabase - includes embedding storage, HNSW/IVFFlat indexes, hybrid search setup, and AI-optimized query patterns. Use when setting up vector search, building RAG systems, configuring semantic search, creating embedding storage, or when user mentions pgvector, vector database, embeddings, semantic search, or hybrid search.
Interact with the Denser Retriever API to build and query knowledge bases. Use this skill whenever the user wants to create a knowledge base, upload documents (files or URLs), search/query a knowledge base, list or delete knowledge bases or documents, check document processing status, or check account usage/balance. Also trigger when the user mentions 'denser retriever', 'knowledge base', 'document search', 'semantic search', 'RAG pipeline', or wants to index and search their files.
Semantic and multi-modal search across documents using LanceDB vector embeddings. Use when searching knowledge bases, finding information semantically, ingesting documents for RAG, or performing vector similarity search. Triggers on "search documents", "semantic search", "find in knowledge base", "vector search", "index documents", "LanceDB", or RAG/embedding operations.
Local RAG system management with RLAMA. Create semantic knowledge bases from local documents (PDF, MD, code, etc.), query them using natural language, and manage document lifecycles. This skill should be used when building local knowledge bases, searching personal documents, or performing document Q&A. Runs 100% locally with Ollama - no cloud, no data leaving your machine.
Exa AI-native semantic search via Composio API. Use when: (1) Searching the web with natural language queries (2) Getting citation-backed answers to research questions (3) Finding pages similar to a given URL (4) Retrieving full content from search results Exa understands meaning - queries don't need exact keyword matches.
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.
Use when text embeddings are needed from Alibaba Cloud Model Studio models for semantic search, retrieval-augmented generation, clustering, or offline vectorization pipelines.
Semantic search, context management, and document indexing via OpenViking. Use when the user asks to: index/import documents or files into a knowledge base, perform semantic search across indexed content, browse or explore indexed resources, get summaries/overviews of indexed documents, manage an OpenViking instance, or integrate structured context retrieval into workflows. Also use when sub-agents need to retrieve relevant context from a large document collection.
Search 21st.dev component registry for production-ready React components. Finds components by natural language description, filters by framework and style system, returns ranked results with install instructions. Use when looking for UI components, finding alternatives to existing components, or sourcing design system building blocks.
MemPalace — Local AI memory with 96.6% recall. Semantic search, temporal knowledge graph, palace architecture (wings/rooms/drawers). Free, no cloud, no API keys.
Search and manage Alma's memory and conversation history. Use when the user asks about past conversations, personal facts, preferences, or anything that requires recalling information ("你知道我...吗", "我们之前聊过...", "你还记得...", "帮我找之前说的..."). Also used to store new memories and search through archived chat threads.