[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill-tavily-vendor-risk-kyc-screening":3,"mdc-ykej9h-key":36,"related-org-tavily-vendor-risk-kyc-screening":742,"related-repo-tavily-vendor-risk-kyc-screening":918},{"slug":4,"name":4,"fn":5,"description":6,"org":7,"tags":12,"stars":26,"repoUrl":27,"updatedAt":28,"license":29,"forks":26,"topics":30,"repo":31,"sourceUrl":34,"mdContent":35},"vendor-risk-kyc-screening","screen vendors for risk and compliance","Screen vendors, merchants, suppliers, counterparties, companies, executives, and related entities for vendor risk, KYC, adverse media, sanctions, regulatory actions, litigation, recalls, supplier risk, cybersecurity incidents, and compliance concerns. 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Use when the user asks for vendor onboarding research, KYC screening, supplier due diligence, merchant case research, or a source-grounded risk brief.\nlicense: MIT\nmetadata:\n  author: tavily\n  version: \"0.1.0\"\n  homepage: https:\u002F\u002Fwww.tavily.com\n  source: https:\u002F\u002Fgithub.com\u002Ftavily-ai\u002Fuse-case-skills\n---\n\n# Vendor Risk KYC Screening\n\n## Workflow\n\nUse Tavily's MCP search and extract tools for source-grounded vendor, merchant, supplier, counterparty, and KYC research; use map or crawl only for known official directories or registries. Treat this as research support, not a final compliance determination.\n\nTreat the guidance below as base guidance; adapt it to the user's request when appropriate.\n\n- Identify the entity, aliases, parent\u002Fsubsidiaries, executives, jurisdictions, products, and risk categories.\n- Break the screen into short subqueries under 400 characters for each alias and risk type: sanctions, enforcement, litigation, regulatory warning, recall, adverse media, cybersecurity incident, supplier risk, and jurisdiction.\n- Use exact-match style queries for legal names, people, product names, and phrases that must appear verbatim.\n- Search first, then filter by domain trust and source type before extracting. Prioritize official regulators, sanctions lists, court records, recall databases, company disclosures, and credible news.\n- Extract selected sources that can support or rule out specific findings.\n- Use site navigation only when a known regulator, registry, or company site needs URL discovery.\n- Collect multiple pages only for scoped official directories, recalls\u002Fadvisories, policy pages, or supplier\u002Fproduct pages.\n\n## Research Budget\n\n- Start with a small focused search set covering entity aliases, sanctions\u002Fregulatory risk, adverse media, and jurisdiction-specific risk.\n- Extract only the strongest official or credible sources before drafting.\n- Add more searches only for named gaps, such as missing alias coverage, missing jurisdiction coverage, or unresolved high-risk findings.\n- Do not use map unless a known regulator, registry, sanctions portal, or company site has specific buried pages to locate.\n- Do not use crawl unless the user asks for scoped collection from an official directory, registry, recalls page, or supplier\u002Fproduct section.\n\n## Capability Guidance\n\n- Use search for most vendor screening, KYC, adverse media, sanctions, enforcement, litigation, recall, and supplier-risk discovery.\n- Use extract on selected official records, regulator pages, sanctions pages, court records, recall databases, company disclosures, and credible news.\n- Use map for official registries, regulator sites, sanctions portals, or company domains with hard-to-find pages.\n- Use crawl for directories or official record sets only after narrowing the paths and risk categories.\n- Use research for full due diligence reports, then verify high-impact claims against original sources.\n\n## Query And Source Guidance\n\n- Query legal names, trade names, aliases, parent\u002Fsubsidiary names, executives, product names, and jurisdiction terms separately.\n- Combine entity terms with specific risk concepts: sanctions, enforcement action, consent order, lawsuit, fraud, recall, breach, warning letter, import alert, debarment, bankruptcy, and adverse media.\n- Prioritize regulators, sanctions databases, court systems, recall databases, official registries, company disclosures, and credible news.\n- Treat unsourced aggregators, copied press releases, and name-match-only hits as low confidence until corroborated.\n- Report failed or inaccessible sources when they affect official registries, sanctions pages, or high-risk findings.\n\n## Output Template\n\nUse this markdown structure and avoid implying clearance:\n\n```markdown\n# Vendor \u002F KYC Risk Brief: \u003Centity>\n\n## Scope\n- Entity and aliases checked:\n- Jurisdictions:\n- Risk categories:\n- Source coverage limits:\n\n## Findings\n### High\n- Finding:\n- Evidence:\n- Confidence:\n\n### Medium\n- Finding:\n- Evidence:\n- Confidence:\n\n### Low \u002F Watch\n- Finding:\n- Evidence:\n- Confidence:\n\n## No Relevant Findings In Searched Sources\n- \u003Crisk category or alias checked>\n\n## Recommended Next Checks\n- \u003Cnext check>\n\n## Sources\n- [Source title](URL) - \u003Cwhat it supports>\n```\n\nSay \"no relevant findings found in searched sources\" instead of \"cleared\" unless the user supplied authoritative internal checks.\n",{"data":37,"body":42},{"name":4,"description":6,"license":29,"metadata":38},{"author":8,"version":39,"homepage":40,"source":41},"0.1.0","https:\u002F\u002Fwww.tavily.com","https:\u002F\u002Fgithub.com\u002Ftavily-ai\u002Fuse-case-skills",{"type":43,"children":44},"root",[45,53,60,66,71,111,117,145,151,179,185,213,219,224,731,736],{"type":46,"tag":47,"props":48,"children":49},"element","h1",{"id":4},[50],{"type":51,"value":52},"text","Vendor Risk KYC Screening",{"type":46,"tag":54,"props":55,"children":57},"h2",{"id":56},"workflow",[58],{"type":51,"value":59},"Workflow",{"type":46,"tag":61,"props":62,"children":63},"p",{},[64],{"type":51,"value":65},"Use Tavily's MCP search and extract tools for source-grounded vendor, merchant, supplier, counterparty, and KYC research; use map or crawl only for known official directories or registries. 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[",{"type":46,"tag":237,"props":682,"children":684},{"style":683},"--shiki-light:#91B859;--shiki-default:#C3E88D;--shiki-dark:#C3E88D",[685],{"type":51,"value":686},"Source title",{"type":46,"tag":237,"props":688,"children":689},{"style":244},[690],{"type":51,"value":691},"](",{"type":46,"tag":237,"props":693,"children":695},{"style":694},"--shiki-light:#E53935;--shiki-light-text-decoration:underline;--shiki-default:#F07178;--shiki-default-text-decoration:underline;--shiki-dark:#F07178;--shiki-dark-text-decoration:underline",[696],{"type":51,"value":697},"URL",{"type":46,"tag":237,"props":699,"children":700},{"style":244},[701],{"type":51,"value":702},")",{"type":46,"tag":237,"props":704,"children":705},{"style":305},[706],{"type":51,"value":707}," - ",{"type":46,"tag":237,"props":709,"children":710},{"style":244},[711],{"type":51,"value":258},{"type":46,"tag":237,"props":713,"children":714},{"style":261},[715],{"type":51,"value":716},"what",{"type":46,"tag":237,"props":718,"children":719},{"style":577},[720],{"type":51,"value":721}," it",{"type":46,"tag":237,"props":723,"children":724},{"style":577},[725],{"type":51,"value":726}," supports",{"type":46,"tag":237,"props":728,"children":729},{"style":244},[730],{"type":51,"value":269},{"type":46,"tag":61,"props":732,"children":733},{},[734],{"type":51,"value":735},"Say \"no relevant findings found in searched sources\" instead of \"cleared\" unless the user supplied authoritative internal checks.",{"type":46,"tag":737,"props":738,"children":739},"style",{},[740],{"type":51,"value":741},"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":743,"total":445},[744,760,775,790,800,813,824,837,850,865,882,899],{"slug":745,"name":745,"fn":746,"description":747,"org":748,"tags":749,"stars":757,"repoUrl":758,"updatedAt":759},"tavily-best-practices","implement Tavily integration best practices","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.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[750,753,756],{"name":751,"slug":752,"type":16},"API Development","api-development",{"name":754,"slug":755,"type":16},"Search","search",{"name":9,"slug":8,"type":16},426,"https:\u002F\u002Fgithub.com\u002Ftavily-ai\u002Fskills","2026-04-06T18:54:03.550741",{"slug":761,"name":761,"fn":762,"description":763,"org":764,"tags":765,"stars":757,"repoUrl":758,"updatedAt":774},"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},[766,769,772,773],{"name":767,"slug":768,"type":16},"CLI","cli",{"name":770,"slug":771,"type":16},"Research","research",{"name":754,"slug":755,"type":16},{"name":9,"slug":8,"type":16},"2026-04-06T18:54:02.065814",{"slug":776,"name":776,"fn":777,"description":778,"org":779,"tags":780,"stars":757,"repoUrl":758,"updatedAt":789},"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},[781,784,785,786],{"name":782,"slug":783,"type":16},"Automation","automation",{"name":767,"slug":768,"type":16},{"name":9,"slug":8,"type":16},{"name":787,"slug":788,"type":16},"Web Scraping","web-scraping","2026-04-06T18:54:09.921446",{"slug":791,"name":791,"fn":792,"description":793,"org":794,"tags":795,"stars":757,"repoUrl":758,"updatedAt":799},"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},[796,797,798],{"name":770,"slug":771,"type":16},{"name":754,"slug":755,"type":16},{"name":9,"slug":8,"type":16},"2026-04-14T04:50:31.944584",{"slug":801,"name":801,"fn":802,"description":803,"org":804,"tags":805,"stars":757,"repoUrl":758,"updatedAt":812},"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},[806,807,810,811],{"name":767,"slug":768,"type":16},{"name":808,"slug":809,"type":16},"Content Creation","content-creation",{"name":9,"slug":8,"type":16},{"name":787,"slug":788,"type":16},"2026-04-06T18:54:08.624436",{"slug":814,"name":814,"fn":815,"description":816,"org":817,"tags":818,"stars":757,"repoUrl":758,"updatedAt":823},"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},[819,820,821,822],{"name":767,"slug":768,"type":16},{"name":754,"slug":755,"type":16},{"name":9,"slug":8,"type":16},{"name":787,"slug":788,"type":16},"2026-04-06T18:54:04.832149",{"slug":825,"name":825,"fn":826,"description":827,"org":828,"tags":829,"stars":757,"repoUrl":758,"updatedAt":836},"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},[830,831,832,835],{"name":767,"slug":768,"type":16},{"name":770,"slug":771,"type":16},{"name":833,"slug":834,"type":16},"Summarization","summarization",{"name":9,"slug":8,"type":16},"2026-04-06T18:54:07.378619",{"slug":838,"name":838,"fn":839,"description":840,"org":841,"tags":842,"stars":757,"repoUrl":758,"updatedAt":849},"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},[843,844,847,848],{"name":767,"slug":768,"type":16},{"name":845,"slug":846,"type":16},"LLM","llm",{"name":754,"slug":755,"type":16},{"name":9,"slug":8,"type":16},"2026-04-06T18:54:06.085596",{"slug":851,"name":851,"fn":852,"description":853,"org":854,"tags":855,"stars":26,"repoUrl":27,"updatedAt":864},"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},[856,859,862,863],{"name":857,"slug":858,"type":16},"Bioinformatics","bioinformatics",{"name":860,"slug":861,"type":16},"Life Sciences","life-sciences",{"name":770,"slug":771,"type":16},{"name":833,"slug":834,"type":16},"2026-07-21T06:07:31.35948",{"slug":866,"name":866,"fn":867,"description":868,"org":869,"tags":870,"stars":26,"repoUrl":27,"updatedAt":881},"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},[871,874,877,880],{"name":872,"slug":873,"type":16},"Finance","finance",{"name":875,"slug":876,"type":16},"Financial Modeling","financial-modeling",{"name":878,"slug":879,"type":16},"Investment Banking","investment-banking",{"name":770,"slug":771,"type":16},"2026-07-21T06:07:30.328538",{"slug":883,"name":883,"fn":884,"description":885,"org":886,"tags":887,"stars":26,"repoUrl":27,"updatedAt":898},"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},[888,891,894,897],{"name":889,"slug":890,"type":16},"Competitive Intelligence","competitive-intelligence",{"name":892,"slug":893,"type":16},"E-commerce","e-commerce",{"name":895,"slug":896,"type":16},"Marketing","marketing",{"name":770,"slug":771,"type":16},"2026-07-21T06:07:34.257311",{"slug":900,"name":900,"fn":901,"description":902,"org":903,"tags":904,"stars":26,"repoUrl":27,"updatedAt":917},"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},[905,908,911,914],{"name":906,"slug":907,"type":16},"CRM","crm",{"name":909,"slug":910,"type":16},"Lead Enrichment","lead-enrichment",{"name":912,"slug":913,"type":16},"Prospecting","prospecting",{"name":915,"slug":916,"type":16},"Sales","sales","2026-07-21T06:07:33.56804",{"items":919,"total":337},[920,927,934,941,948,959,976],{"slug":851,"name":851,"fn":852,"description":853,"org":921,"tags":922,"stars":26,"repoUrl":27,"updatedAt":864},{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[923,924,925,926],{"name":857,"slug":858,"type":16},{"name":860,"slug":861,"type":16},{"name":770,"slug":771,"type":16},{"name":833,"slug":834,"type":16},{"slug":866,"name":866,"fn":867,"description":868,"org":928,"tags":929,"stars":26,"repoUrl":27,"updatedAt":881},{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[930,931,932,933],{"name":872,"slug":873,"type":16},{"name":875,"slug":876,"type":16},{"name":878,"slug":879,"type":16},{"name":770,"slug":771,"type":16},{"slug":883,"name":883,"fn":884,"description":885,"org":935,"tags":936,"stars":26,"repoUrl":27,"updatedAt":898},{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[937,938,939,940],{"name":889,"slug":890,"type":16},{"name":892,"slug":893,"type":16},{"name":895,"slug":896,"type":16},{"name":770,"slug":771,"type":16},{"slug":900,"name":900,"fn":901,"description":902,"org":942,"tags":943,"stars":26,"repoUrl":27,"updatedAt":917},{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[944,945,946,947],{"name":906,"slug":907,"type":16},{"name":909,"slug":910,"type":16},{"name":912,"slug":913,"type":16},{"name":915,"slug":916,"type":16},{"slug":949,"name":949,"fn":950,"description":951,"org":952,"tags":953,"stars":26,"repoUrl":27,"updatedAt":958},"tavily-web","research the web with Tavily","Search and research the web with Tavily. Use for current information, source discovery, webpage extraction, site mapping, documentation crawling, comparisons, and comprehensive research with citations.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[954,955,956,957],{"name":770,"slug":771,"type":16},{"name":754,"slug":755,"type":16},{"name":9,"slug":8,"type":16},{"name":787,"slug":788,"type":16},"2026-07-21T06:07:29.989683",{"slug":960,"name":960,"fn":961,"description":962,"org":963,"tags":964,"stars":26,"repoUrl":27,"updatedAt":975},"threat-intelligence-enrichment","enrich threat intelligence data","Enrich threat intelligence from CVEs, IOCs, malware names, threat actors, vendor advisories, security incidents, exploit reports, vulnerability disclosures, breach news, and mitigation guidance. Use when the user asks to investigate a CVE, enrich indicators, summarize vendor advisories, assess exploit status, collect mitigations, or produce a source-grounded security brief.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[965,968,969,972],{"name":966,"slug":967,"type":16},"Code Analysis","code-analysis",{"name":770,"slug":771,"type":16},{"name":970,"slug":971,"type":16},"Security","security",{"name":973,"slug":974,"type":16},"Threat Modeling","threat-modeling","2026-07-21T06:07:30.67376",{"slug":4,"name":4,"fn":5,"description":6,"org":977,"tags":978,"stars":26,"repoUrl":27,"updatedAt":28},{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[979,980,981,982],{"name":14,"slug":15,"type":16},{"name":18,"slug":19,"type":16},{"name":24,"slug":25,"type":16},{"name":21,"slug":22,"type":16}]