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Search your coding memory for past work, sessions, and implementation details. \n \n Queries personal session memories, project/repo memories, or both in parallel using optional scope flags ( --user , --repo , --both ) \n Returns formatted results with timestamps and relevance scores to help recall previous decisions, implementations, and coding patterns \n Designed for retrieving context about past work when users ask about earlier sessions, how something was implemented, or what they worked on

Efficient similarity search patterns for vector databases and semantic retrieval systems. \n \n Covers four major vector database implementations: Pinecone, Qdrant, pgvector with PostgreSQL, and Weaviate, each with production-ready code templates \n Explains three index types (Flat, HNSW, IVF+PQ) with trade-offs between search speed, recall accuracy, and data scale \n Includes four distance metrics (Cosine, Euclidean, Dot Product, Manhattan) and guidance on when to use each \n Demonstrates hybri

Web search optimized for AI agents with real-time data retrieval. \n \n Single command interface ( scripts/search ) for querying the web with results tailored for agent consumption \n Supports JSON output formatting for structured data integration into agent workflows \n Requires Tavily API key for authentication; free API key available from tavily.com \n

Web search and content extraction via Tavily and Exa APIs through inference.sh CLI. \n \n Five search and extraction apps: Tavily Search Assistant (AI-powered answers with sources), Tavily Extract (multi-URL content extraction), Exa Search (smart web search), Exa Answer (direct factual responses), and Exa Extract (web page analysis) \n Designed for research workflows, RAG pipelines, fact-checking, and content aggregation with LLM integration examples \n Requires inference.sh CLI ( infsh ) instal

Use scripts/serper_search.py to call the Serper API and return JSON search results.

Modern file and content search.

This skill automates the process of extracting high-quality multi-modal content (text + images) from Xiaohongshu (小红书) and actively assists you in generating a deeply integrated, analytical final report for the user. Due to Xiaohongshu's aggressive anti-scraping mechanisms, direct HTTP requests or naive scraping often result in 404s or blocks. This skill natively bypasses these by simulating a real user through the playwright-cli in a headed browser window.

The useSearchParams hook requires TWO things:

Category: test

SwiftUI search is environment-based and navigation-consumed. You attach .searchable() to a view, but a navigation container (NavigationStack, NavigationSplitView, or TabView) renders the actual search field. This indirection is the source of most search bugs.