tavily-dynamic-search 是做什麼的？
程式化網頁搜尋，隔離上下文。使用此技能處理任何需要搜尋網路、篩選結果並提取特定資訊的研究任務——同時避免原始HTML和樣板內容污染你的上下文窗口。這是網頁研究的預設技能。由「搜尋」、「查詢」、「查找」、「研究」、「最新消息」或任何需要當前網路資訊的查詢觸發。也應用於當被要求「搜尋並篩選」、「找出重要部分」或「提取關鍵細節」的情況——即使用者希望獲得精煉、無雜訊內容的任何場景。
程式化網頁搜尋，隔離上下文。用於任何需要搜尋網頁、篩選結果並提取特定資訊的研究任務——同時避免原始HTML和樣板內容污染上下文窗口。
此技能提供程式化網頁搜尋功能，專為需要即時網路資訊的AI代理設計。透過tavily-ai/tavily-dynamic-search，使用者可以執行精確的網頁查詢，自動過濾雜訊，僅擷取關鍵內容，從而節省上下文窗口空間。支援「搜尋」、「查詢」、「查找」、「研究」等自然語言觸發詞，以及需要篩選和提取重點的進階查詢。該技能可在Claude Code、Cursor等主流AI開發平台上使用，透過API呼叫底層搜尋引擎，返回結構化、無干擾的結果。開發者無需手動處理HTML解析或網頁抓取，只需提供查詢意圖即可獲得精煉的資訊摘要，適合進行市場研究、技術調研、新聞追蹤或任何依賴當前網路資訊的任務。
npx skills add https://github.com/tavily-ai/skills/tree/main/skills/tavily-dynamic-searchInstalled? Explore more 研究與資料分析 skills: affaan-m/uspto-database, affaan-m/scholar-evaluation, affaan-m/literature-review · View all 6 →
程式化網頁搜尋，隔離上下文。使用此技能處理任何需要搜尋網路、篩選結果並提取特定資訊的研究任務——同時避免原始HTML和樣板內容污染你的上下文窗口。這是網頁研究的預設技能。由「搜尋」、「查詢」、「查找」、「研究」、「最新消息」或任何需要當前網路資訊的查詢觸發。也應用於當被要求「搜尋並篩選」、「找出重要部分」或「提取關鍵細節」的情況——即使用者希望獲得精煉、無雜訊內容的任何場景。
This repo contains 11 individual skills — each has its own dedicated page.
Crawl any website and save pages as local markdown files. Use when you need to download documentation, knowledge bases, or web content for offline access or analysis. No code required - just provide a URL.
Extract content from specific URLs using Tavily's extraction API. Returns clean markdown/text from web pages. Use when you have specific URLs and need their content without writing code.
Comprehensive research grounded in web data with explicit citations. Use when you need multi-source synthesis—comparisons, current events, market analysis, detailed reports.
Search the web using Tavily's LLM-optimized search API. Returns relevant results with content snippets, scores, and metadata. Use when you need to find web content on any topic without writing code.
Build production-ready Tavily integrations with best practices baked in. Reference documentation for developers using coding assistants (Claude Code, Cursor, etc.) to implement web search, content extraction, crawling, and research in agentic workflows, RAG systems, or autonomous agents.
Web search, content extraction, crawling, and deep research via the Tavily CLI. Use this skill whenever the user wants to search the web, find articles, research a topic, look something up online, extract content from a URL, grab text from a webpage, crawl documentation, download a site's pages, discover URLs on a domain, or conduct in-depth research with citations. Also use when they say "fetch this page", "pull the content from", "get the page at https://", "find me articles about", or reference extracting data from external websites. This provides LLM-optimized web search, content extraction, site crawling, URL discovery, and AI-powered deep research — capabilities beyond what agents can do natively. Do NOT trigger for local file operations, git commands, deployments, or code editing tasks.
Crawl websites and extract content from multiple pages via the Tavily CLI. Use this skill when the user wants to crawl a site, download documentation, extract an entire docs section, bulk-extract pages, save a site as local markdown files, or says "crawl", "get all the pages", "download the docs", "extract everything under /docs", "bulk extract", or needs content from many pages on the same domain. Supports depth/breadth control, path filtering, semantic instructions, and saving each page as a local markdown file.
Extract clean markdown or text content from specific URLs via the Tavily CLI. Use this skill when the user has one or more URLs and wants their content, says "extract", "grab the content from", "pull the text from", "get the page at", "read this webpage", or needs clean text from web pages. Handles JavaScript-rendered pages, returns LLM-optimized markdown, and supports query-focused chunking for targeted extraction. Can process up to 20 URLs in a single call.
Discover and list all URLs on a website without extracting content, via the Tavily CLI. Use this skill when the user wants to find a specific page on a large site, list all URLs, see the site structure, find where something is on a domain, or says "map the site", "find the URL for", "what pages are on", "list all pages", or "site structure". Faster than crawling — returns URLs only. Essential when you know the site but not the exact page. Combine with extract for targeted content retrieval.
Conduct comprehensive AI-powered research with citations via the Tavily CLI. Use this skill when the user wants deep research, a detailed report, a comparison, market analysis, literature review, or says "research", "investigate", "analyze in depth", "compare X vs Y", "what does the market look like for", or needs multi-source synthesis with explicit citations. Returns a structured report grounded in web sources. Takes 30-120 seconds. For quick fact-finding, use tavily-search instead.
Search the web with LLM-optimized results via the Tavily CLI. Use this skill when the user wants to search the web, find articles, look up information, get recent news, discover sources, or says "search for", "find me", "look up", "what's the latest on", "find articles about", or needs current information from the internet. Returns relevant results with content snippets, relevance scores, and metadata — optimized for LLM consumption. Supports domain filtering, time ranges, and multiple search depths.
USPTO patent and trademark data workflow for official record lookup, PatentSearch queries, TSDR checks, assignment data, and reproducible IP research logs.
Structured scholarly-work evaluation for papers, proposals, literature reviews, methods sections, evidence quality, citation support, and research-writing feedback.
Systematic literature-review workflow for academic, biomedical, technical, and scientific topics, including search planning, source screening, synthesis, citation checks, and evidence logging.
The agent harness performance optimization system. Skills, instincts, memory, security, and research-first development for Claude Code, Codex, Opencode, Cursor and beyond.
Agent skill repository discovered by 10x-chat research.
Review backend code for quality, security, maintainability, and best practices based on established checklist rules. Use when the user requests a review, analysis, or improvement of backend files (e.g., `.py`) under the `api/` directory. Do NOT use for frontend files (e.g., `.tsx`, `.ts`, `.js`). Supports pending-change review, code snippets review, and file-focused review.