tavily-dynamic-search 是做什么的？
程序化网络搜索与上下文隔离。使用此技能进行任何需要搜索网络、过滤结果并提取特定信息的研究任务——同时避免用原始HTML和样板代码污染上下文窗口。这是网络研究的默认技能。由“搜索”、“查找”、“找到”、“研究”、“最新进展”或任何需要当前网络信息的查询触发。也适用于“搜索并过滤”、“找到重要部分”或“提取关键细节”——任何用户希望获得精心策划、无噪声内容的情况。
程序化网络搜索与上下文隔离。用于任何需要搜索网络、过滤结果并提取特定信息的研究任务，同时避免用原始HTML和样板代码污染上下文窗口。这是网络研究的默认技能，由“搜索”、“查找”、“研究”、“最新进展”或任何需要当前网络信息的查询触发。也适用于“搜索并过滤”、“找到重要部分”或“提取关键细节”——任何用户希望获得精心策划、无噪声内容的情况。
此技能提供程序化网络搜索功能，并具备上下文隔离能力，适用于各类研究任务。它能让AI代理搜索网络、过滤结果并提取特定信息，同时避免原始HTML和样板内容污染上下文窗口。该技能支持多种触发指令，如“搜索”、“查找”、“研究”等，还能处理“搜索并过滤”、“提取关键细节”等需要精心策划、无噪声信息的查询。它作为默认网络研究技能，确保AI代理高效获取当前网络信息，无需手动清理数据，从而优化工作流程和响应质量。
npx skills add https://github.com/tavily-ai/skills/tree/main/skills/tavily-dynamic-searchInstalled? Explore more 研究与数据分析 skills: affaan-m/uspto-database, affaan-m/scholar-evaluation, affaan-m/literature-review · View all 6 →
程序化网络搜索与上下文隔离。使用此技能进行任何需要搜索网络、过滤结果并提取特定信息的研究任务——同时避免用原始HTML和样板代码污染上下文窗口。这是网络研究的默认技能。由“搜索”、“查找”、“找到”、“研究”、“最新进展”或任何需要当前网络信息的查询触发。也适用于“搜索并过滤”、“找到重要部分”或“提取关键细节”——任何用户希望获得精心策划、无噪声内容的情况。
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.