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Found 144 Skills
Search the web using Exa's AI-powered search API. Supports semantic search, content extraction, direct answers, and deep research with structured output.
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.
Index YouTube channel videos and transcripts for semantic search. Use when user says "index YouTube", "add YouTube channel", "update video index", or "index transcripts". Works with solograph MCP (if available) or standalone via yt-dlp.
Edge-optimized RAG memory system for OpenClaw with semantic search. Automatically loads memory files, provides intelligent recall, and enhances conversations with relevant context. Perfect for Jetson and edge devices (<10MB memory).
MemPalace — mine projects and conversations into a searchable memory palace. Use when asked about mempalace, memory palace, mining memories, searching memories, or palace setup.
Search real-time news with bias scoring, get live stock/ETF/crypto data with AI analysis, ML options pricing, balanced news synthesis, and meme search via the Helium MCP server.
This skill should be used to search the local Obsidian vault / markdown knowledge base by meaning, not just keywords, using the on-device qmd engine (BM25 + vector + LLM rerank). Trigger when the user asks to "search my vault/notes", "find notes about X", "what do my notes say about Y", "do I have anything on Z", "semantic search my knowledge base", or wants concept/cross-lingual retrieval over markdown. Fully local — nothing leaves the machine.
Tips and best practices for effective GrepAI searches. Use this skill to improve search result quality.
Complete RAG and search engineering skill. Covers chunking strategies, hybrid retrieval (BM25 + vector), cross-encoder reranking, query rewriting, ranking pipelines, nDCG/MRR evaluation, and production search systems. Modern patterns for retrieval-augmented generation and semantic search.
This skill should be used when the user asks to "search secondbrain", "find in knowledge base", "look up documentation", "search notes/ADRs/tasks", "find related content", "semantic search", or mentions wanting to find specific content across their secondbrain using natural language.
Persistent knowledge storage using basic-memory CLI. Use to save notes, search memories semantically, and build context for topics across sessions.
Provides patterns to build Retrieval-Augmented Generation (RAG) systems for AI 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.