프로그래밍 방식의 웹 검색 및 컨텍스트 격리. 원시 HTML과 보일러플레이트로 컨텍스트 창을 오염시키지 않고 웹을 검색하고 결과를 필터링하며 특정 정보를 추출해야 하는 모든 연구 작업에 사용합니다.
이 스킬은 프로그래밍 방식의 웹 검색 기능을 제공하여, 사용자가 웹에서 정보를 검색하고, 결과를 필터링하며, 특정 정보를 추출할 수 있게 해줍니다. 핵심 장점은 컨텍스트 격리(context isolation)로, 원시 HTML이나 불필요한 보일러플레이트 코드가 LLM의 컨텍스트 창을 오염시키는 것을 방지합니다. '검색해 줘', '찾아 봐', '연구해 줘', '최신 소식이 뭐야'와 같은 자연어 트리거에 반응하며, '검색 및 필터링', '중요한 부분 찾기', '핵심 세부 사항 추출' 등의 요청에도 사용됩니다. Tavily API를 기반으로 하여 정제되고 노이즈가 없는 콘텐츠를 제공함으로써, 연구 작업을 더 효율적이고 집중적으로 수행할 수 있도록 돕습니다. 다양한 AI 에이전트 플랫폼(Claude Code, Cursor 등)에서 기본 웹 연구 스킬로 활용됩니다.
npx skills add https://github.com/tavily-ai/skills/tree/main/skills/tavily-dynamic-searchInstalled? Explore more 연구 & 데이터 분석 skills: affaan-m/uspto-database, affaan-m/scholar-evaluation, affaan-m/literature-review · View all 6 →
컨텍스트 격리를 갖춘 프로그래밍 방식의 웹 검색입니다. 원시 HTML과 보일러플레이트로 컨텍스트 창을 오염시키지 않고 웹을 검색하고, 결과를 필터링하며, 특정 정보를 추출해야 하는 모든 연구 작업에 이 스킬을 사용하세요. 이것은 웹 연구를 위한 기본 스킬입니다. "search for", "look up", "find", "research", "what's the latest on" 또는 최신 웹 정보가 필요한 모든 쿼리에 의해 트리거됩니다. 또한 "search and filter", "find the important parts", "extract the key details" — 즉, 사용자가 선별되고 노이즈 없는 콘텐츠를 원하는 모든 경우에 사용하세요.
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.