Tavily CLI を使用してウェブサイト上のすべてのURLを検出・一覧表示します（コンテンツ抽出なし）。大規模サイトで特定のページを探す、URL一覧表示、サイト構造の確認、ドメイン内の情報の場所を探す、「サイトをマッピング」「URLを見つける」「ページを一覧表示」といったユーザーリクエストに最適です。クローリングより高速でURLのみを返します。サイトは知っているが正確なページがわからない場合に必須で、対象コンテンツ取得のために抽出スキルと組み合わせて使用します。
tavily-ai/tavily-map スキルは、Tavily CLI を活用してウェブサイト上の全URLを高速に検出・一覧表示するためのものです。このスキルは、コンテンツを抽出せずにURLのみを取得するため、クローリングよりもはるかに高速です。特に、ユーザーがサイトの構造を把握したい、特定のページのURLを見つけたい、あるいは「サイトをマッピングして」「URLを見つけて」「全ページをリストアップして」といったリクエストを行った場合に有効です。このスキルは、サイトは知っているが正確なページがわからないシナリオで不可欠であり、対象コンテンツを取得するために `tavily-ai/tavily-extract` スキルと組み合わせて使用できます。対応プラットフォームは Tavily CLI であり、開発者やAIエージェントがウェブサイトのナビゲーションやコンテンツ収集を効率的に行うのに役立ちます。
npx skills add https://github.com/tavily-ai/skills/tree/main/skills/tavily-mapInstalled? Explore more ライティング＆編集 skills: steipete/notion, langchain-ai/langchain, bytedance/podcast-generation · View all 6 →
Tavily CLI を使用して、ウェブサイト上のコンテンツを抽出せずにすべてのURLを発見して一覧表示します。ユーザーが大規模サイトで特定のページを探したい場合、すべてのURLを一覧表示したい場合、サイト構造を確認したい場合、ドメイン内の何かの場所を探したい場合、または「サイトをマッピングしてください」「URLを見つけてください」「どのページがありますか」「すべてのページを一覧表示してください」「サイト構造」などと言った場合にこのスキルを使用してください。クローリングよりも高速で、URLのみを返します。サイトは知っているが正確なページがわからない場合に不可欠です。対象コンテンツを取得するために、抽出スキルと組み合わせて使用してください。
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.
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.
Notion CLI/API for pages, Markdown content, data sources, files, comments, search, Workers, and raw API calls.
The agent engineering platform.
Use this skill when the user requests to generate, create, or produce podcasts from text content. Converts written content into a two-host conversational podcast audio format with natural dialogue.
Analyze and optimize tweets for maximum reach using Twitter's open-source algorithm insights. Rewrite and edit user tweets to improve engagement and visibility based on how the recommendation system ranks content.
Extracts and analyzes competitors' ads from ad libraries (Facebook, LinkedIn, etc.) to understand what messaging, problems, and creative approaches are working. Helps inspire and improve your own ad campaigns.
Context7 Platform -- Up-to-date code documentation for LLMs and AI code editors