tavily-best-practices은(는) 무엇을 하나요?
모범 사례가 내장된 프로덕션 수준의 Tavily 통합을 구축하세요. 코드 어시스턴트(Claude Code, Cursor 등)를 사용하는 개발자가 에이전트 워크플로, RAG 시스템 또는 자율 에이전트에서 웹 검색, 콘텐츠 추출, 크롤링 및 리서치를 구현하기 위한 참조 문서입니다.
모범 사례가 내장된 프로덕션 수준의 Tavily 통합을 구축하세요. 코드 어시스턴트(Claude Code, Cursor 등)를 사용하는 개발자가 에이전트 워크플로, RAG 시스템 또는 자율 에이전트에서 웹 검색, 콘텐츠 추출, 크롤링 및 리서치를 구현하기 위한 참조 문서입니다.
이 스킬은 Tavily API를 활용하여 프로덕션 환경에서 신뢰할 수 있는 웹 검색, 콘텐츠 추출, 크롤링 및 리서치 기능을 구현하기 위한 모범 사례 참조 문서입니다. 주로 Claude Code, Cursor와 같은 코딩 어시스턴트를 사용하는 개발자를 대상으로 하며, 에이전트 워크플로, RAG(Retrieval-Augmented Generation) 시스템, 또는 자율 에이전트에 Tavily를 통합하는 방법을 안내합니다. 이 스킬은 Tavily API 엔드포인트 호출, 응답 처리, 오류 처리, 속도 제한, 캐싱 전략 등 실제 구현에 필요한 핵심 지침을 제공합니다. SKILL.md 문서에는 Tavily의 다양한 기능(웹 검색, 뉴스 검색, 콘텐츠 추출, 크롤링)을 사용하는 방법과 함께, 효율적이고 안정적인 통합을 위한 코드 예제와 설정 방법이 포함되어 있습니다.
npx skills add https://github.com/tavily-ai/skills/tree/main/skills/tavily-best-practicesInstalled? Explore more 연구 & 데이터 분석 skills: affaan-m/uspto-database, affaan-m/scholar-evaluation, affaan-m/literature-review · View all 6 →
모범 사례가 내장된 프로덕션 수준의 Tavily 통합을 구축하세요. 코드 어시스턴트(Claude Code, Cursor 등)를 사용하는 개발자가 에이전트 워크플로, RAG 시스템 또는 자율 에이전트에서 웹 검색, 콘텐츠 추출, 크롤링 및 리서치를 구현하기 위한 참조 문서입니다.
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.
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.
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.