tavily-research 是做什麼的？
透過 Tavily CLI 進行全面的 AI 驅動研究，並附帶引用來源。當使用者需要深入調查、詳細報告、比較、市場分析、文獻回顧，或提到「研究」、「調查」、「深入分析」、「比較 X 與 Y」、「市場狀況如何」，或需要多來源整合與明確引用時，可使用此技能。它會回傳一份以網路來源為基礎的結構化報告，預計耗時 30 至 120 秒。若僅需快速查詢事實，請改用 tavily-search。
透過 Tavily CLI 進行全面的 AI 驅動研究，並附帶引用來源。當使用者需要深入調查、詳細報告、比較、市場分析、文獻回顧，或提到「研究」、「調查」、「深入分析」、「比較 X 與 Y」、「市場狀況如何」，或需要多來源整合與明確引用時，可使用此技能。它會回傳一份以網路來源為基礎的結構化報告，預計耗時 30 至 120 秒。若僅需快速查詢事實，請改用 tavily-search。
tavily-ai/tavily-research 是一個專為 AI 代理工具（如 Claude Code、Cursor 或 Codex）設計的技能，能透過 Tavily CLI 執行深入、多來源的網路研究，並產生結構化報告與明確引用。此技能特別適合需要詳細報告、比較分析、市場調查或文獻回顧的場景，當使用者說出「研究」、「調查」、「深入分析」、「比較 X 與 Y」或「市場狀況如何」等關鍵字時便會觸發。它會花費 30 到 120 秒來收集和綜合來自多個網頁的資料，最終回傳一份附有引用來源的結構化報告。對於僅需快速查詢基本事實的情況，開發者應改用較快的 tavily-search 技能。此技能支援多種 AI 代理平台，並透過標準化 CLI 介面運作，方便整合至自動化工作流程中。
npx skills add https://github.com/tavily-ai/skills/tree/main/skills/tavily-researchInstalled? 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 →
透過 Tavily CLI 進行全面的 AI 驅動研究，並附帶引用來源。當使用者需要深入調查、詳細報告、比較、市場分析、文獻回顧，或提到「研究」、「調查」、「深入分析」、「比較 X 與 Y」、「市場狀況如何」，或需要多來源整合與明確引用時，可使用此技能。它會回傳一份以網路來源為基礎的結構化報告，預計耗時 30 至 120 秒。若僅需快速查詢事實，請改用 tavily-search。
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.
Programmatic web search with context isolation. Use this skill for any research task where you need to search the web, filter results, and extract specific information — without polluting your context window with raw HTML and boilerplate. This is the default skill for web research. Triggered by "search for", "look up", "find", "research", "what's the latest on", or any query that requires current web information. Also use when asked to "search and filter", "find the important parts", or "extract the key details" — any case where the user wants curated, noise-free content.
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.
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.