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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.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},"tavily","Tavily","https:\u002F\u002Fpexgzepcugksgbtrxkhf.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Forg-logos\u002Ftavily.png","tavily-ai",[13,15,18],{"name":9,"slug":8,"type":14},"tag",{"name":16,"slug":17,"type":14},"Search","search",{"name":19,"slug":20,"type":14},"API Development","api-development",426,"https:\u002F\u002Fgithub.com\u002Ftavily-ai\u002Fskills","2026-04-06T18:54:03.550741",null,36,[],{"repoUrl":22,"stars":21,"forks":25,"topics":28,"description":24},[],"https:\u002F\u002Fgithub.com\u002Ftavily-ai\u002Fskills\u002Ftree\u002FHEAD\u002Fskills\u002Ftavily-best-practices","---\nname: tavily-best-practices\ndescription: \"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.\"\n---\n\n# Tavily\n\nTavily is a search API designed for LLMs, enabling AI applications to access real-time web data.\n\n## Installation\n\n**Python:**\n```bash\npip install tavily-python\n```\n\n**JavaScript:**\n```bash\nnpm install @tavily\u002Fcore\n```\n\nSee **[references\u002Fsdk.md](references\u002Fsdk.md)** for complete SDK reference.\n\n## Client Initialization\n\n```python\nfrom tavily import TavilyClient\n\n# Uses TAVILY_API_KEY env var (recommended)\nclient = TavilyClient()\n\n#With project tracking (for usage organization)\nclient = TavilyClient(project_id=\"your-project-id\")\n\n# Async client for parallel queries\nfrom tavily import AsyncTavilyClient\nasync_client = AsyncTavilyClient()\n```\n\n## Choosing the Right Method\n\n**For custom agents\u002Fworkflows:**\n\n| Need | Method |\n|------|--------|\n| Web search results | `search()` |\n| Content from specific URLs | `extract()` |\n| Content from entire site | `crawl()` |\n| URL discovery from site | `map()` |\n\n**For out-of-the-box research:**\n\n| Need | Method |\n|------|--------|\n| End-to-end research with AI synthesis | `research()` |\n\n## Quick Reference\n\n### search() - Web Search\n\n```python\nresponse = client.search(\n    query=\"quantum computing breakthroughs\",  # Keep under 400 chars\n    max_results=10,\n    search_depth=\"advanced\"\n)\nprint(response)\n```\nKey parameters: `query`, `max_results`, `search_depth` (ultra-fast\u002Ffast\u002Fbasic\u002Fadvanced), `include_domains`, `exclude_domains`, `time_range`\n\nSee **[references\u002Fsearch.md](references\u002Fsearch.md)** for complete search reference.\n\n### extract() - URL Content Extraction\n\n```python\n# Simple one-step extraction\nresponse = client.extract(\n    urls=[\"https:\u002F\u002Fdocs.example.com\"],\n    extract_depth=\"advanced\"\n)\nprint(response)\n```\nKey parameters: `urls` (max 20), `extract_depth`, `query`, `chunks_per_source` (1-5)\n\nSee **[references\u002Fextract.md](references\u002Fextract.md)** for complete extract reference.\n\n### crawl() - Site-Wide Extraction\n\n```python\nresponse = client.crawl(\n    url=\"https:\u002F\u002Fdocs.example.com\",\n    instructions=\"Find API documentation pages\",  # Semantic focus\n    extract_depth=\"advanced\"\n)\nprint(response)\n```\nKey parameters: `url`, `max_depth`, `max_breadth`, `limit`, `instructions`, `chunks_per_source`, `select_paths`, `exclude_paths`\n\nSee **[references\u002Fcrawl.md](references\u002Fcrawl.md)** for complete crawl reference.\n\n### map() - URL Discovery\n\n```python\nresponse = client.map(\n    url=\"https:\u002F\u002Fdocs.example.com\"\n)\nprint(response)\n```\n\n### research() - AI-Powered Research\n\n```python\nimport time\n\n# For comprehensive multi-topic research\nresult = client.research(\n    input=\"Analyze competitive landscape for X in SMB market\",\n    model=\"pro\"  # or \"mini\" for focused queries, \"auto\" when unsure\n)\nrequest_id = result[\"request_id\"]\n\n# Poll until completed\nresponse = client.get_research(request_id)\nwhile response[\"status\"] not in [\"completed\", \"failed\"]:\n    time.sleep(10)\n    response = client.get_research(request_id)\n\nprint(response[\"content\"])  # The research report\n```\n\nKey parameters: `input`, `model` (\"mini\"\u002F\"pro\"\u002F\"auto\"), `stream`, `output_schema`, `citation_format`\n\nSee **[references\u002Fresearch.md](references\u002Fresearch.md)** for complete research reference.\n\n## Detailed Guides\n\nFor complete parameters, response fields, patterns, and examples:\n\n- **[references\u002Fsdk.md](references\u002Fsdk.md)** - Python & JavaScript SDK reference, async patterns, Hybrid RAG\n- **[references\u002Fsearch.md](references\u002Fsearch.md)** - Query optimization, search depth selection, domain filtering, async patterns, post-filtering\n- **[references\u002Fextract.md](references\u002Fextract.md)** - One-step vs two-step extraction, query\u002Fchunks for targeting, advanced mode\n- **[references\u002Fcrawl.md](references\u002Fcrawl.md)** - Crawl vs Map, instructions for semantic focus, use cases, Map-then-Extract pattern\n- **[references\u002Fresearch.md](references\u002Fresearch.md)** - Prompting best practices, model selection, streaming, structured output schemas\n- **[references\u002Fintegrations.md](references\u002Fintegrations.md)** - LangChain, LlamaIndex, CrewAI, Vercel AI SDK, and framework 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Extraction",{"type":37,"tag":66,"props":658,"children":660},{"className":156,"code":659,"language":158,"meta":71,"style":71},"response = client.crawl(\n    url=\"https:\u002F\u002Fdocs.example.com\",\n    instructions=\"Find API documentation pages\",  # Semantic focus\n    extract_depth=\"advanced\"\n)\nprint(response)\n",[661],{"type":37,"tag":74,"props":662,"children":663},{"__ignoreMap":71},[664,672,680,688,695,702],{"type":37,"tag":78,"props":665,"children":666},{"class":80,"line":81},[667],{"type":37,"tag":78,"props":668,"children":669},{},[670],{"type":42,"value":671},"response = client.crawl(\n",{"type":37,"tag":78,"props":673,"children":674},{"class":80,"line":173},[675],{"type":37,"tag":78,"props":676,"children":677},{},[678],{"type":42,"value":679},"    url=\"https:\u002F\u002Fdocs.example.com\",\n",{"type":37,"tag":78,"props":681,"children":682},{"class":80,"line":183},[683],{"type":37,"tag":78,"props":684,"children":685},{},[686],{"type":42,"value":687},"    instructions=\"Find API documentation pages\",  # Semantic focus\n",{"type":37,"tag":78,"props":689,"children":690},{"class":80,"line":192},[691],{"type":37,"tag":78,"props":692,"children":693},{},[694],{"type":42,"value":589},{"type":37,"tag":78,"props":696,"children":697},{"class":80,"line":201},[698],{"type":37,"tag":78,"props":699,"children":700},{},[701],{"type":42,"value":474},{"type":37,"tag":78,"props":703,"children":704},{"class":80,"line":209},[705],{"type":37,"tag":78,"props":706,"children":707},{},[708],{"type":42,"value":482},{"type":37,"tag":44,"props":710,"children":711},{},[712,713,719,720,726,727,733,734,740,741,747,748,753,754,760,761],{"type":42,"value":487},{"type":37,"tag":74,"props":714,"children":716},{"className":715},[],[717],{"type":42,"value":718},"url",{"type":42,"value":495},{"type":37,"tag":74,"props":721,"children":723},{"className":722},[],[724],{"type":42,"value":725},"max_depth",{"type":42,"value":495},{"type":37,"tag":74,"props":728,"children":730},{"className":729},[],[731],{"type":42,"value":732},"max_breadth",{"type":42,"value":495},{"type":37,"tag":74,"props":735,"children":737},{"className":736},[],[738],{"type":42,"value":739},"limit",{"type":42,"value":495},{"type":37,"tag":74,"props":742,"children":744},{"className":743},[],[745],{"type":42,"value":746},"instructions",{"type":42,"value":495},{"type":37,"tag":74,"props":749,"children":751},{"className":750},[],[752],{"type":42,"value":634},{"type":42,"value":495},{"type":37,"tag":74,"props":755,"children":757},{"className":756},[],[758],{"type":42,"value":759},"select_paths",{"type":42,"value":495},{"type":37,"tag":74,"props":762,"children":764},{"className":763},[],[765],{"type":42,"value":766},"exclude_paths",{"type":37,"tag":44,"props":768,"children":769},{},[770,771,779],{"type":42,"value":136},{"type":37,"tag":60,"props":772,"children":773},{},[774],{"type":37,"tag":141,"props":775,"children":777},{"href":776},"references\u002Fcrawl.md",[778],{"type":42,"value":776},{"type":42,"value":780}," for complete crawl reference.",{"type":37,"tag":422,"props":782,"children":784},{"id":783},"map-url-discovery",[785],{"type":42,"value":786},"map() - URL Discovery",{"type":37,"tag":66,"props":788,"children":790},{"className":156,"code":789,"language":158,"meta":71,"style":71},"response = client.map(\n    url=\"https:\u002F\u002Fdocs.example.com\"\n)\nprint(response)\n",[791],{"type":37,"tag":74,"props":792,"children":793},{"__ignoreMap":71},[794,802,810,817],{"type":37,"tag":78,"props":795,"children":796},{"class":80,"line":81},[797],{"type":37,"tag":78,"props":798,"children":799},{},[800],{"type":42,"value":801},"response = client.map(\n",{"type":37,"tag":78,"props":803,"children":804},{"class":80,"line":173},[805],{"type":37,"tag":78,"props":806,"children":807},{},[808],{"type":42,"value":809},"    url=\"https:\u002F\u002Fdocs.example.com\"\n",{"type":37,"tag":78,"props":811,"children":812},{"class":80,"line":183},[813],{"type":37,"tag":78,"props":814,"children":815},{},[816],{"type":42,"value":474},{"type":37,"tag":78,"props":818,"children":819},{"class":80,"line":192},[820],{"type":37,"tag":78,"props":821,"children":822},{},[823],{"type":42,"value":482},{"type":37,"tag":422,"props":825,"children":827},{"id":826},"research-ai-powered-research",[828],{"type":42,"value":829},"research() - AI-Powered Research",{"type":37,"tag":66,"props":831,"children":833},{"className":156,"code":832,"language":158,"meta":71,"style":71},"import time\n\n# For comprehensive multi-topic research\nresult = client.research(\n    input=\"Analyze competitive landscape for X in SMB market\",\n    model=\"pro\"  # or \"mini\" for focused queries, \"auto\" when unsure\n)\nrequest_id = result[\"request_id\"]\n\n# Poll until completed\nresponse = client.get_research(request_id)\nwhile response[\"status\"] not in [\"completed\", \"failed\"]:\n    time.sleep(10)\n    response = client.get_research(request_id)\n\nprint(response[\"content\"])  # The research report\n",[834],{"type":37,"tag":74,"props":835,"children":836},{"__ignoreMap":71},[837,845,852,860,868,876,884,891,899,906,914,922,931,940,949,957],{"type":37,"tag":78,"props":838,"children":839},{"class":80,"line":81},[840],{"type":37,"tag":78,"props":841,"children":842},{},[843],{"type":42,"value":844},"import time\n",{"type":37,"tag":78,"props":846,"children":847},{"class":80,"line":173},[848],{"type":37,"tag":78,"props":849,"children":850},{"emptyLinePlaceholder":177},[851],{"type":42,"value":180},{"type":37,"tag":78,"props":853,"children":854},{"class":80,"line":183},[855],{"type":37,"tag":78,"props":856,"children":857},{},[858],{"type":42,"value":859},"# For comprehensive multi-topic research\n",{"type":37,"tag":78,"props":861,"children":862},{"class":80,"line":192},[863],{"type":37,"tag":78,"props":864,"children":865},{},[866],{"type":42,"value":867},"result = client.research(\n",{"type":37,"tag":78,"props":869,"children":870},{"class":80,"line":201},[871],{"type":37,"tag":78,"props":872,"children":873},{},[874],{"type":42,"value":875},"    input=\"Analyze competitive landscape for X in SMB market\",\n",{"type":37,"tag":78,"props":877,"children":878},{"class":80,"line":209},[879],{"type":37,"tag":78,"props":880,"children":881},{},[882],{"type":42,"value":883},"    model=\"pro\"  # or \"mini\" for focused queries, \"auto\" when unsure\n",{"type":37,"tag":78,"props":885,"children":886},{"class":80,"line":218},[887],{"type":37,"tag":78,"props":888,"children":889},{},[890],{"type":42,"value":474},{"type":37,"tag":78,"props":892,"children":893},{"class":80,"line":227},[894],{"type":37,"tag":78,"props":895,"children":896},{},[897],{"type":42,"value":898},"request_id = result[\"request_id\"]\n",{"type":37,"tag":78,"props":900,"children":901},{"class":80,"line":235},[902],{"type":37,"tag":78,"props":903,"children":904},{"emptyLinePlaceholder":177},[905],{"type":42,"value":180},{"type":37,"tag":78,"props":907,"children":908},{"class":80,"line":244},[909],{"type":37,"tag":78,"props":910,"children":911},{},[912],{"type":42,"value":913},"# Poll until completed\n",{"type":37,"tag":78,"props":915,"children":916},{"class":80,"line":253},[917],{"type":37,"tag":78,"props":918,"children":919},{},[920],{"type":42,"value":921},"response = client.get_research(request_id)\n",{"type":37,"tag":78,"props":923,"children":925},{"class":80,"line":924},12,[926],{"type":37,"tag":78,"props":927,"children":928},{},[929],{"type":42,"value":930},"while response[\"status\"] not in [\"completed\", \"failed\"]:\n",{"type":37,"tag":78,"props":932,"children":934},{"class":80,"line":933},13,[935],{"type":37,"tag":78,"props":936,"children":937},{},[938],{"type":42,"value":939},"    time.sleep(10)\n",{"type":37,"tag":78,"props":941,"children":943},{"class":80,"line":942},14,[944],{"type":37,"tag":78,"props":945,"children":946},{},[947],{"type":42,"value":948},"    response = client.get_research(request_id)\n",{"type":37,"tag":78,"props":950,"children":952},{"class":80,"line":951},15,[953],{"type":37,"tag":78,"props":954,"children":955},{"emptyLinePlaceholder":177},[956],{"type":42,"value":180},{"type":37,"tag":78,"props":958,"children":960},{"class":80,"line":959},16,[961],{"type":37,"tag":78,"props":962,"children":963},{},[964],{"type":42,"value":965},"print(response[\"content\"])  # The research report\n",{"type":37,"tag":44,"props":967,"children":968},{},[969,970,976,977,983,985,991,992,998,999],{"type":42,"value":487},{"type":37,"tag":74,"props":971,"children":973},{"className":972},[],[974],{"type":42,"value":975},"input",{"type":42,"value":495},{"type":37,"tag":74,"props":978,"children":980},{"className":979},[],[981],{"type":42,"value":982},"model",{"type":42,"value":984}," (\"mini\"\u002F\"pro\"\u002F\"auto\"), ",{"type":37,"tag":74,"props":986,"children":988},{"className":987},[],[989],{"type":42,"value":990},"stream",{"type":42,"value":495},{"type":37,"tag":74,"props":993,"children":995},{"className":994},[],[996],{"type":42,"value":997},"output_schema",{"type":42,"value":495},{"type":37,"tag":74,"props":1000,"children":1002},{"className":1001},[],[1003],{"type":42,"value":1004},"citation_format",{"type":37,"tag":44,"props":1006,"children":1007},{},[1008,1009,1017],{"type":42,"value":136},{"type":37,"tag":60,"props":1010,"children":1011},{},[1012],{"type":37,"tag":141,"props":1013,"children":1015},{"href":1014},"references\u002Fresearch.md",[1016],{"type":42,"value":1014},{"type":42,"value":1018}," for complete research reference.",{"type":37,"tag":50,"props":1020,"children":1022},{"id":1021},"detailed-guides",[1023],{"type":42,"value":1024},"Detailed Guides",{"type":37,"tag":44,"props":1026,"children":1027},{},[1028],{"type":42,"value":1029},"For complete parameters, response fields, patterns, and examples:",{"type":37,"tag":1031,"props":1032,"children":1033},"ul",{},[1034,1047,1059,1071,1083,1095],{"type":37,"tag":1035,"props":1036,"children":1037},"li",{},[1038,1045],{"type":37,"tag":60,"props":1039,"children":1040},{},[1041],{"type":37,"tag":141,"props":1042,"children":1043},{"href":143},[1044],{"type":42,"value":143},{"type":42,"value":1046}," - Python & JavaScript SDK reference, async patterns, Hybrid RAG",{"type":37,"tag":1035,"props":1048,"children":1049},{},[1050,1057],{"type":37,"tag":60,"props":1051,"children":1052},{},[1053],{"type":37,"tag":141,"props":1054,"children":1055},{"href":540},[1056],{"type":42,"value":540},{"type":42,"value":1058}," - Query optimization, search depth selection, domain filtering, async patterns, post-filtering",{"type":37,"tag":1035,"props":1060,"children":1061},{},[1062,1069],{"type":37,"tag":60,"props":1063,"children":1064},{},[1065],{"type":37,"tag":141,"props":1066,"children":1067},{"href":646},[1068],{"type":42,"value":646},{"type":42,"value":1070}," - One-step vs two-step extraction, query\u002Fchunks for targeting, advanced mode",{"type":37,"tag":1035,"props":1072,"children":1073},{},[1074,1081],{"type":37,"tag":60,"props":1075,"children":1076},{},[1077],{"type":37,"tag":141,"props":1078,"children":1079},{"href":776},[1080],{"type":42,"value":776},{"type":42,"value":1082}," - Crawl vs Map, instructions for semantic focus, use cases, Map-then-Extract pattern",{"type":37,"tag":1035,"props":1084,"children":1085},{},[1086,1093],{"type":37,"tag":60,"props":1087,"children":1088},{},[1089],{"type":37,"tag":141,"props":1090,"children":1091},{"href":1014},[1092],{"type":42,"value":1014},{"type":42,"value":1094}," - Prompting best practices, model selection, streaming, structured output schemas",{"type":37,"tag":1035,"props":1096,"children":1097},{},[1098,1106],{"type":37,"tag":60,"props":1099,"children":1100},{},[1101],{"type":37,"tag":141,"props":1102,"children":1104},{"href":1103},"references\u002Fintegrations.md",[1105],{"type":42,"value":1103},{"type":42,"value":1107}," - LangChain, LlamaIndex, CrewAI, Vercel AI SDK, and framework integrations",{"type":37,"tag":1109,"props":1110,"children":1111},"style",{},[1112],{"type":42,"value":1113},"html .light .shiki span {color: var(--shiki-light);background: var(--shiki-light-bg);font-style: var(--shiki-light-font-style);font-weight: var(--shiki-light-font-weight);text-decoration: var(--shiki-light-text-decoration);}html.light .shiki span {color: var(--shiki-light);background: var(--shiki-light-bg);font-style: var(--shiki-light-font-style);font-weight: var(--shiki-light-font-weight);text-decoration: var(--shiki-light-text-decoration);}html .default .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .dark .shiki span {color: var(--shiki-dark);background: var(--shiki-dark-bg);font-style: var(--shiki-dark-font-style);font-weight: var(--shiki-dark-font-weight);text-decoration: var(--shiki-dark-text-decoration);}html.dark .shiki span {color: var(--shiki-dark);background: var(--shiki-dark-bg);font-style: var(--shiki-dark-font-style);font-weight: var(--shiki-dark-font-weight);text-decoration: var(--shiki-dark-text-decoration);}",{"items":1115,"total":959},[1116,1122,1137,1152,1162,1175,1186,1199,1212,1229,1246,1263],{"slug":4,"name":4,"fn":5,"description":6,"org":1117,"tags":1118,"stars":21,"repoUrl":22,"updatedAt":23},{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[1119,1120,1121],{"name":19,"slug":20,"type":14},{"name":16,"slug":17,"type":14},{"name":9,"slug":8,"type":14},{"slug":1123,"name":1123,"fn":1124,"description":1125,"org":1126,"tags":1127,"stars":21,"repoUrl":22,"updatedAt":1136},"tavily-cli","conduct web research with Tavily CLI","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:\u002F\u002F\", \"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.\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[1128,1131,1134,1135],{"name":1129,"slug":1130,"type":14},"CLI","cli",{"name":1132,"slug":1133,"type":14},"Research","research",{"name":16,"slug":17,"type":14},{"name":9,"slug":8,"type":14},"2026-04-06T18:54:02.065814",{"slug":1138,"name":1138,"fn":1139,"description":1140,"org":1141,"tags":1142,"stars":21,"repoUrl":22,"updatedAt":1151},"tavily-crawl","crawl websites and extract content","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 \u002Fdocs\", \"bulk extract\", or needs content from many pages on the same domain. Supports depth\u002Fbreadth control, path filtering, semantic instructions, and saving each page as a local markdown file.\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[1143,1146,1147,1148],{"name":1144,"slug":1145,"type":14},"Automation","automation",{"name":1129,"slug":1130,"type":14},{"name":9,"slug":8,"type":14},{"name":1149,"slug":1150,"type":14},"Web Scraping","web-scraping","2026-04-06T18:54:09.921446",{"slug":1153,"name":1153,"fn":1154,"description":1155,"org":1156,"tags":1157,"stars":21,"repoUrl":22,"updatedAt":1161},"tavily-dynamic-search","search the web via Tavily","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.\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[1158,1159,1160],{"name":1132,"slug":1133,"type":14},{"name":16,"slug":17,"type":14},{"name":9,"slug":8,"type":14},"2026-04-14T04:50:31.944584",{"slug":1163,"name":1163,"fn":1164,"description":1165,"org":1166,"tags":1167,"stars":21,"repoUrl":22,"updatedAt":1174},"tavily-extract","extract markdown content from URLs","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.\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[1168,1169,1172,1173],{"name":1129,"slug":1130,"type":14},{"name":1170,"slug":1171,"type":14},"Content Creation","content-creation",{"name":9,"slug":8,"type":14},{"name":1149,"slug":1150,"type":14},"2026-04-06T18:54:08.624436",{"slug":1176,"name":1176,"fn":1177,"description":1178,"org":1179,"tags":1180,"stars":21,"repoUrl":22,"updatedAt":1185},"tavily-map","map website URLs with Tavily","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.\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[1181,1182,1183,1184],{"name":1129,"slug":1130,"type":14},{"name":16,"slug":17,"type":14},{"name":9,"slug":8,"type":14},{"name":1149,"slug":1150,"type":14},"2026-04-06T18:54:04.832149",{"slug":1187,"name":1187,"fn":1188,"description":1189,"org":1190,"tags":1191,"stars":21,"repoUrl":22,"updatedAt":1198},"tavily-research","conduct AI-powered research with Tavily","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.\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[1192,1193,1194,1197],{"name":1129,"slug":1130,"type":14},{"name":1132,"slug":1133,"type":14},{"name":1195,"slug":1196,"type":14},"Summarization","summarization",{"name":9,"slug":8,"type":14},"2026-04-06T18:54:07.378619",{"slug":1200,"name":1200,"fn":1201,"description":1202,"org":1203,"tags":1204,"stars":21,"repoUrl":22,"updatedAt":1211},"tavily-search","search the web with Tavily","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.\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[1205,1206,1209,1210],{"name":1129,"slug":1130,"type":14},{"name":1207,"slug":1208,"type":14},"LLM","llm",{"name":16,"slug":17,"type":14},{"name":9,"slug":8,"type":14},"2026-04-06T18:54:06.085596",{"slug":1213,"name":1213,"fn":1214,"description":1215,"org":1216,"tags":1217,"stars":1226,"repoUrl":1227,"updatedAt":1228},"academic-scientific-research","synthesize academic and scientific literature","Find, screen, and synthesize academic papers, scientific literature, technical reports, preprints, clinical or biomedical publications, and evidence around a research question. Use when the user asks to find papers, summarize literature, compare methods, identify seminal or recent work, extract claims from studies, or build a source-grounded academic\u002Fscientific evidence brief.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[1218,1221,1224,1225],{"name":1219,"slug":1220,"type":14},"Bioinformatics","bioinformatics",{"name":1222,"slug":1223,"type":14},"Life Sciences","life-sciences",{"name":1132,"slug":1133,"type":14},{"name":1195,"slug":1196,"type":14},0,"https:\u002F\u002Fgithub.com\u002Ftavily-ai\u002Ftavily-grok-plugin","2026-07-21T06:07:31.35948",{"slug":1230,"name":1230,"fn":1231,"description":1232,"org":1233,"tags":1234,"stars":1226,"repoUrl":1227,"updatedAt":1245},"investment-research-briefs","create investment research briefs","Create concise investment research briefs, company memos, sector snapshots, portfolio monitoring updates, earnings\u002Fnews summaries, risk and catalyst briefs, and market thesis support. Use when the user asks for an investor-focused brief on a company, sector, public\u002Fprivate market, comparable set, or portfolio theme.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[1235,1238,1241,1244],{"name":1236,"slug":1237,"type":14},"Finance","finance",{"name":1239,"slug":1240,"type":14},"Financial Modeling","financial-modeling",{"name":1242,"slug":1243,"type":14},"Investment Banking","investment-banking",{"name":1132,"slug":1133,"type":14},"2026-07-21T06:07:30.328538",{"slug":1247,"name":1247,"fn":1248,"description":1249,"org":1250,"tags":1251,"stars":1226,"repoUrl":1227,"updatedAt":1262},"product-competitor-intelligence","research competitor products and market data","Research competitor products, SKUs, pricing, packaging, positioning, feature comparisons, product catalogs, category pages, retailer listings, marketplace data, and market intelligence. Use when the user asks for competitor SKU discovery, product data enrichment, pricing\u002Fspec extraction, product comparison, market intelligence, or crawl\u002Fmap-driven product research.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[1252,1255,1258,1261],{"name":1253,"slug":1254,"type":14},"Competitive Intelligence","competitive-intelligence",{"name":1256,"slug":1257,"type":14},"E-commerce","e-commerce",{"name":1259,"slug":1260,"type":14},"Marketing","marketing",{"name":1132,"slug":1133,"type":14},"2026-07-21T06:07:34.257311",{"slug":1264,"name":1264,"fn":1265,"description":1266,"org":1267,"tags":1268,"stars":1226,"repoUrl":1227,"updatedAt":1281},"sales-account-intelligence","build sales account intelligence","Build sales-ready account intelligence for companies, prospects, customers, partners, and target buyers. Use when the user asks for sales prep, account brief, meeting prep, buyer research, expansion signals, customer intelligence, trigger events, executive context, outreach angles, or market\u002Faccount context for GTM teams.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[1269,1272,1275,1278],{"name":1270,"slug":1271,"type":14},"CRM","crm",{"name":1273,"slug":1274,"type":14},"Lead Enrichment","lead-enrichment",{"name":1276,"slug":1277,"type":14},"Prospecting","prospecting",{"name":1279,"slug":1280,"type":14},"Sales","sales","2026-07-21T06:07:33.56804",{"items":1283,"total":227},[1284,1290,1297,1304,1310,1317,1324],{"slug":4,"name":4,"fn":5,"description":6,"org":1285,"tags":1286,"stars":21,"repoUrl":22,"updatedAt":23},{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[1287,1288,1289],{"name":19,"slug":20,"type":14},{"name":16,"slug":17,"type":14},{"name":9,"slug":8,"type":14},{"slug":1123,"name":1123,"fn":1124,"description":1125,"org":1291,"tags":1292,"stars":21,"repoUrl":22,"updatedAt":1136},{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[1293,1294,1295,1296],{"name":1129,"slug":1130,"type":14},{"name":1132,"slug":1133,"type":14},{"name":16,"slug":17,"type":14},{"name":9,"slug":8,"type":14},{"slug":1138,"name":1138,"fn":1139,"description":1140,"org":1298,"tags":1299,"stars":21,"repoUrl":22,"updatedAt":1151},{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[1300,1301,1302,1303],{"name":1144,"slug":1145,"type":14},{"name":1129,"slug":1130,"type":14},{"name":9,"slug":8,"type":14},{"name":1149,"slug":1150,"type":14},{"slug":1153,"name":1153,"fn":1154,"description":1155,"org":1305,"tags":1306,"stars":21,"repoUrl":22,"updatedAt":1161},{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[1307,1308,1309],{"name":1132,"slug":1133,"type":14},{"name":16,"slug":17,"type":14},{"name":9,"slug":8,"type":14},{"slug":1163,"name":1163,"fn":1164,"description":1165,"org":1311,"tags":1312,"stars":21,"repoUrl":22,"updatedAt":1174},{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[1313,1314,1315,1316],{"name":1129,"slug":1130,"type":14},{"name":1170,"slug":1171,"type":14},{"name":9,"slug":8,"type":14},{"name":1149,"slug":1150,"type":14},{"slug":1176,"name":1176,"fn":1177,"description":1178,"org":1318,"tags":1319,"stars":21,"repoUrl":22,"updatedAt":1185},{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[1320,1321,1322,1323],{"name":1129,"slug":1130,"type":14},{"name":16,"slug":17,"type":14},{"name":9,"slug":8,"type":14},{"name":1149,"slug":1150,"type":14},{"slug":1187,"name":1187,"fn":1188,"description":1189,"org":1325,"tags":1326,"stars":21,"repoUrl":22,"updatedAt":1198},{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[1327,1328,1329,1330],{"name":1129,"slug":1130,"type":14},{"name":1132,"slug":1133,"type":14},{"name":1195,"slug":1196,"type":14},{"name":9,"slug":8,"type":14}]