Tavily CLI를 통한 웹 검색, 콘텐츠 추출, 크롤링 및 심층 조사. 사용자가 웹 검색, URL 콘텐츠 추출, 사이트 크롤링, 심층 조사 등을 요청할 때 이 스킬을 사용하세요.
이 스킬은 Tavily CLI를 활용하여 AI 에이전트에 네이티브 웹 검색, 콘텐츠 추출, 사이트 크롤링, URL 발견 및 AI 기반 심층 조사 기능을 제공합니다. '이 페이지를 가져와줘', 'https://의 콘텐츠를 가져와' 또는 '주제에 대한 기사를 찾아줘'와 같은 요청에 반응하도록 설계되어, 에이전트가 LLM에 최적화된 검색 결과와 구조화된 콘텐츠를 얻을 수 있게 해줍니다. 주요 사용 사례로는 웹 정보 검색, URL에서 텍스트/데이터 추출, 문서 사이트 크롤링, 도메인의 URL 발견, 인용을 포함한 포괄적인 심층 조사 등이 있습니다. 로컬 파일 작업, Git 명령어, 배포 또는 코드 편집 작업에는 트리거되지 않아야 합니다. 이 스킬은 다양한 개발 환경에서 AI 에이전트의 기능을 확장하여 외부 웹 데이터에 원활하게 접근하고 분석할 수 있게 합니다.
npx skills add https://github.com/tavily-ai/skills/tree/main/skills/tavily-cliInstalled? Explore more 연구 & 데이터 분석 skills: affaan-m/uspto-database, affaan-m/scholar-evaluation, affaan-m/literature-review · View all 6 →
웹 검색, 콘텐츠 추출, 크롤링 및 Tavily CLI를 통한 심층 조사. 사용자가 웹을 검색하고, 기사를 찾고, 주제를 조사하고, 온라인에서 무언가를 찾고, URL에서 콘텐츠를 추출하고, 웹페이지에서 텍스트를 가져오고, 문서를 크롤링하고, 사이트 페이지를 다운로드하고, 도메인의 URL을 발견하거나 인용을 포함한 심층 조사를 수행하려 할 때 이 스킬을 사용하세요. 또한 사용자가 "이 페이지를 가져와줘", "콘텐츠를 가져와", "https://의 페이지를 가져와", "에 대한 기사를 찾아줘"라고 말하거나 외부 웹사이트에서 데이터를 추출하는 것을 언급할 때도 사용하세요. 이는 LLM에 최적화된 웹 검색, 콘텐츠 추출, 사이트 크롤링, URL 발견 및 AI 기반 심층 조사를 제공합니다. 이는 에이전트가 기본적으로 수행할 수 있는 것 이상의 기능입니다. 로컬 파일 작업, Git 명령어, 배포 또는 코드 편집 작업에는 트리거하지 마세요.
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.
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.
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.