[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M70DP15VEYBYMP7SEEKX9K8X":3,"skill-same:01M70DP15VEYBYMP7SEEKX9K8X":77},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":26,"publishedAt":47,"linkOnly":48,"markdownContent":49,"markdownBody":50,"frontMatter":51,"refName":54,"commitSha":55,"skillDirectory":56,"forksCount":57,"repositoryHtmlUrl":58,"repositoryHomepageUrl":6,"ownerHtmlUrl":59,"ownerBlogUrl":60,"ownerDescription":61,"updatedAt":62,"install":63,"resources":76},"01M70DP15VEYBYMP7SEEKX9K8X","tavily-research",null,"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.","EthanYoQ\u002FSkill-hub","EthanYoQ","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F257484394?v=4","01M70C9QKWR1YS0S7V26DNB18H","Ethan","01M70C9QMER047R5ANZZA6KC2P",11,"MIT","https:\u002F\u002Fgithub.com\u002FEthanYoQ\u002FSkill-hub\u002Fblob\u002Fmine\u002Farchives\u002Fuser-installed-skills\u002Ftavily-research\u002F069725235368fdcd\u002Ftavily-research\u002FSKILL.md",[18,23],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000021","资料检索与研究","SCENARIO","使用场景",{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",[27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46],"tavily research","research","tavily","returns","tavily cli","source","cli","literature","analyze","review","analysis","Bash","when","search","market analysis","deep research","quick","deep","sources","Citations","2026-11-10 10:12:30",false,"---\nname: tavily-research\ndescription: |\n  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.\nallowed-tools: Bash(tvly *)\n---\n\n# tavily research\n\nAI-powered deep research that gathers sources, analyzes them, and produces a cited report. Takes 30-120 seconds.\n\n## Before running any command\n\nIf `tvly` is not found on PATH, install it first:\n\n```bash\ncurl -fsSL https:\u002F\u002Fcli.tavily.com\u002Finstall.sh | bash && tvly login\n```\n\nDo not skip this step or fall back to other tools.\n\nSee [tavily-cli](..\u002Ftavily-cli\u002FSKILL.md) for alternative install methods and auth options.\n\n## When to use\n\n- You need comprehensive, multi-source analysis\n- The user wants a comparison, market report, or literature review\n- Quick searches aren't enough — you need synthesis with citations\n- Step 5 in the [workflow](..\u002Ftavily-cli\u002FSKILL.md): search → extract → map → crawl → **research**\n\n## Quick start\n\n```bash\n# Basic research (waits for completion)\ntvly research \"competitive landscape of AI code assistants\"\n\n# Pro model for comprehensive analysis\ntvly research \"electric vehicle market analysis\" --model pro\n\n# Stream results in real-time\ntvly research \"AI agent frameworks comparison\" --stream\n\n# Save report to file\ntvly research \"fintech trends 2025\" --model pro -o fintech-report.md\n\n# JSON output for agents\ntvly research \"quantum computing breakthroughs\" --json\n```\n\n## Options\n\n| Option | Description |\n|--------|-------------|\n| `--model` | `mini`, `pro`, or `auto` (default) |\n| `--stream` | Stream results in real-time |\n| `--no-wait` | Return request_id immediately (async) |\n| `--output-schema` | Path to JSON schema for structured output |\n| `--citation-format` | `numbered`, `mla`, `apa`, `chicago` |\n| `--poll-interval` | Seconds between checks (default: 10) |\n| `--timeout` | Max wait seconds (default: 600) |\n| `-o, --output` | Save output to file |\n| `--json` | Structured JSON output |\n\n## Model selection\n\n| Model | Use for | Speed |\n|-------|---------|-------|\n| `mini` | Single-topic, targeted research | ~30s |\n| `pro` | Comprehensive multi-angle analysis | ~60-120s |\n| `auto` | API chooses based on complexity | Varies |\n\n**Rule of thumb:** \"What does X do?\" → mini. \"X vs Y vs Z\" or \"best way to...\" → pro.\n\n## Async workflow\n\nFor long-running research, you can start and poll separately:\n\n```bash\n# Start without waiting\ntvly research \"topic\" --no-wait --json    # returns request_id\n\n# Check status\ntvly research status \u003Crequest_id> --json\n\n# Wait for completion\ntvly research poll \u003Crequest_id> --json -o result.json\n```\n\n## Tips\n\n- **Research takes 30-120 seconds** — use `--stream` to see progress in real-time.\n- **Use `--model pro`** for complex comparisons or multi-faceted topics.\n- **Use `--output-schema`** to get structured JSON output matching a custom schema.\n- **For quick facts**, use `tvly search` instead — research is for deep synthesis.\n- Read from stdin: `echo \"query\" | tvly research - --json`\n\n## See also\n\n- [tavily-search](..\u002Ftavily-search\u002FSKILL.md) — quick web search for simple lookups\n- [tavily-crawl](..\u002Ftavily-crawl\u002FSKILL.md) — bulk extract from a site for your own analysis\n","# tavily research\n\nAI-powered deep research that gathers sources, analyzes them, and produces a cited report. Takes 30-120 seconds.\n\n## Before running any command\n\nIf `tvly` is not found on PATH, install it first:\n\n```bash\ncurl -fsSL https:\u002F\u002Fcli.tavily.com\u002Finstall.sh | bash && tvly login\n```\n\nDo not skip this step or fall back to other tools.\n\nSee [tavily-cli](..\u002Ftavily-cli\u002FSKILL.md) for alternative install methods and auth options.\n\n## When to use\n\n- You need comprehensive, multi-source analysis\n- The user wants a comparison, market report, or literature review\n- Quick searches aren't enough — you need synthesis with citations\n- Step 5 in the [workflow](..\u002Ftavily-cli\u002FSKILL.md): search → extract → map → crawl → **research**\n\n## Quick start\n\n```bash\n# Basic research (waits for completion)\ntvly research \"competitive landscape of AI code assistants\"\n\n# Pro model for comprehensive analysis\ntvly research \"electric vehicle market analysis\" --model pro\n\n# Stream results in real-time\ntvly research \"AI agent frameworks comparison\" --stream\n\n# Save report to file\ntvly research \"fintech trends 2025\" --model pro -o fintech-report.md\n\n# JSON output for agents\ntvly research \"quantum computing breakthroughs\" --json\n```\n\n## Options\n\n| Option | Description |\n|--------|-------------|\n| `--model` | `mini`, `pro`, or `auto` (default) |\n| `--stream` | Stream results in real-time |\n| `--no-wait` | Return request_id immediately (async) |\n| `--output-schema` | Path to JSON schema for structured output |\n| `--citation-format` | `numbered`, `mla`, `apa`, `chicago` |\n| `--poll-interval` | Seconds between checks (default: 10) |\n| `--timeout` | Max wait seconds (default: 600) |\n| `-o, --output` | Save output to file |\n| `--json` | Structured JSON output |\n\n## Model selection\n\n| Model | Use for | Speed |\n|-------|---------|-------|\n| `mini` | Single-topic, targeted research | ~30s |\n| `pro` | Comprehensive multi-angle analysis | ~60-120s |\n| `auto` | API chooses based on complexity | Varies |\n\n**Rule of thumb:** \"What does X do?\" → mini. \"X vs Y vs Z\" or \"best way to...\" → pro.\n\n## Async workflow\n\nFor long-running research, you can start and poll separately:\n\n```bash\n# Start without waiting\ntvly research \"topic\" --no-wait --json    # returns request_id\n\n# Check status\ntvly research status \u003Crequest_id> --json\n\n# Wait for completion\ntvly research poll \u003Crequest_id> --json -o result.json\n```\n\n## Tips\n\n- **Research takes 30-120 seconds** — use `--stream` to see progress in real-time.\n- **Use `--model pro`** for complex comparisons or multi-faceted topics.\n- **Use `--output-schema`** to get structured JSON output matching a custom schema.\n- **For quick facts**, use `tvly search` instead — research is for deep synthesis.\n- Read from stdin: `echo \"query\" | tvly research - --json`\n\n## See also\n\n- [tavily-search](..\u002Ftavily-search\u002FSKILL.md) — quick web search for simple lookups\n- [tavily-crawl](..\u002Ftavily-crawl\u002FSKILL.md) — bulk extract from a site for your own analysis\n",{"name":5,"description":52,"allowed-tools":53},"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","Bash(tvly *)","mine","1e7390741dffd3d5041fca4ebc056071ee3ba137","archives\u002Fuser-installed-skills\u002Ftavily-research\u002F069725235368fdcd\u002Ftavily-research",5,"https:\u002F\u002Fgithub.com\u002FEthanYoQ\u002FSkill-hub","https:\u002F\u002Fgithub.com\u002FEthanYoQ","www.orz.md","Part Marketer, Part AI Explorer.","2026-11-07 14:52:29",{"source":8,"skillName":5,"commands":64,"prompt":74,"zipAvailable":75},[65,68,71],{"tool":66,"command":67},"npx","npx skills add EthanYoQ\u002FSkill-hub --skill tavily-research",{"tool":69,"command":70},"bunx","bunx skills add EthanYoQ\u002FSkill-hub --skill tavily-research",{"tool":72,"command":73},"pnpm","pnpm dlx skills add EthanYoQ\u002FSkill-hub --skill tavily-research","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FEthanYoQ\u002FSkill-hub\u002Fblob\u002Fmine\u002Farchives\u002Fuser-installed-skills\u002Ftavily-research\u002F069725235368fdcd\u002Ftavily-research\u002FSKILL.md",true,[],[78,104,135,159,177,192],{"skillNo":79,"name":80,"nameZh":6,"description":81,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":82,"categories":83,"keywords":85,"publishedAt":47},"01M713CBDSYARG4PXFXK2R2NNN","xhs-pharma-social-listening","Run evidence-disciplined Xiaohongshu social listening for foreign pharma employee pain points. Use when the user asks to collect and analyze notes\u002Fcomments about pharma companies, aliases, roles, compliance, medical affairs, or AI topics, then produce a cited and scored sampled-insight report. Includes independent-evidence counting, MediaCrawler batch collection, and CAPTCHA\u002F461 fallback to Agent Reach\u002FOpenCLI.","https:\u002F\u002Fgithub.com\u002FEthanYoQ\u002FSkill-hub\u002Fblob\u002Fmine\u002Fskills\u002F10-business-industry\u002Fxhs-pharma-social-listening\u002FSKILL.md",[84],{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},[86,87,88,89,90,91,39,92,93,35,94,95,96,97,98,99,100,101,102,103],"social","xhs pharma social listening","social listening","xhs","medical","counting","collection","batch","notes","run","opencli","fallback","captcha","agent reach","evidence","for","collect","produce",{"skillNo":105,"name":106,"nameZh":6,"description":107,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":108,"categories":109,"keywords":116,"publishedAt":47},"01M713C8Q76ZP0WXXBRAFKS6EE","healthcare-phi-compliance","Protected Health Information (PHI) and Personally Identifiable Information (PII) compliance patterns for healthcare applications. Covers data classification, access control, audit trails, encryption, and common leak vectors. Use when code touches PHI or PII in a healthcare system, or when auditing access control, audit trails, or leak vectors.","https:\u002F\u002Fgithub.com\u002FEthanYoQ\u002FSkill-hub\u002Fblob\u002Fmine\u002Fskills\u002F10-business-industry\u002Fhealthcare-phi-compliance\u002FSKILL.md",[110,111],{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":112,"categoryName":113,"taxonomyType":114,"taxonomyTypeDesc":115},"01JBSEEDCAT000000000000037","律师与法务","PROFESSION","适用职业",[117,118,119,120,121,122,123,124,39,125,126,127,128,129,130,131,101,132,133,134],"healthcare","phi","compliance","healthcare phi compliance","pii","compliance patterns","information","access","encryption","code","common","audit","data","patterns","Access Control","system","applications","Auditing Access Control",{"skillNo":136,"name":137,"nameZh":6,"description":138,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":139,"categories":140,"keywords":142,"publishedAt":47},"01M713C5R2GE43X45T2K75TB76","healthcare-eval-harness","Patient safety evaluation harness for healthcare application deployments. Automated test suites for CDSS accuracy, PHI exposure, clinical workflow integrity, and integration compliance. Blocks deployments on safety failures. Use when a healthcare deployment must be gated on patient-safety tests for CDSS accuracy, PHI exposure, and workflow integrity.","https:\u002F\u002Fgithub.com\u002FEthanYoQ\u002FSkill-hub\u002Fblob\u002Fmine\u002Fskills\u002F10-business-industry\u002Fhealthcare-eval-harness\u002FSKILL.md",[141],{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},[117,143,144,145,146,147,148,39,149,150,151,152,118,101,153,154,155,156,157,158],"eval","harness","healthcare eval harness","eval harness","clinical","patient","workflow","Evaluation","Safety","integrity","deployment","accuracy","tests","deployments","use","super",{"skillNo":160,"name":161,"nameZh":6,"description":162,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":163,"categories":164,"keywords":166,"publishedAt":47},"01M713C2J17S91VYR7A8M696VR","healthcare-emr-patterns","EMR\u002FEHR development patterns for healthcare applications. Clinical safety, encounter workflows, prescription generation, clinical decision support integration, and accessibility-first UI for medical data entry. Use when building EMR or EHR features such as encounter workflows, prescription generation, or clinical data entry UI.","https:\u002F\u002Fgithub.com\u002FEthanYoQ\u002FSkill-hub\u002Fblob\u002Fmine\u002Fskills\u002F10-business-industry\u002Fhealthcare-emr-patterns\u002FSKILL.md",[165],{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},[117,167,130,168,169,90,147,170,39,171,172,173,151,129,174,175,176,101,133,157],"EMR","healthcare emr patterns","decision","workflows","support","clinical decision support","features","ui","as","development",{"skillNo":178,"name":179,"nameZh":6,"description":180,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":181,"categories":182,"keywords":184,"publishedAt":47},"01M713BZERTXV0N8X6SW9DDVWZ","healthcare-cdss-patterns","Clinical Decision Support System (CDSS) development patterns. Drug interaction checking, dose validation, clinical scoring (NEWS2, qSOFA), alert severity classification, and integration into EMR workflows. Use when building clinical decision support — drug interaction checks, dose validation, clinical scoring, or alert severity.","https:\u002F\u002Fgithub.com\u002FEthanYoQ\u002FSkill-hub\u002Fblob\u002Fmine\u002Fskills\u002F10-business-industry\u002Fhealthcare-cdss-patterns\u002FSKILL.md",[183],{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},[117,130,185,169,147,167,170,39,186,187,171,172,188,176,132,189,157,190,158,191],"healthcare cdss patterns","scoring","interaction","severity","classification","alert","decision support",{"skillNo":193,"name":194,"nameZh":6,"description":195,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":196,"categories":197,"keywords":199,"publishedAt":47},"01M713BVK3PQR7V4ZKYGMWMXJD","dxy-task-clarify","用户的请求含糊、缺背景、缺资料说明、缺读者或用途时使用。先用少量带选项的追问把任务问清楚，写成任务卡，用户确认后再执行。尤其适用于医生、科研人员和药企各岗位的工作，例如「帮我分析一下这些数据」「帮我准备明天的拜访」「写个总结」「做个 PPT」「回一下这封邮件」「整理一下会议纪要」「整理一下这些文献」「帮我准备下周的讲课」「写个病例汇报」「帮我写个提示词」。用户说「先问我」「帮我理清需求」「我不知道怎么描述」「我不会问 AI」时也使用。用途、资料、读者都已说清的请求，纯翻译，以及写代码、改代码，不使用。","https:\u002F\u002Fgithub.com\u002FEthanYoQ\u002FSkill-hub\u002Fblob\u002Fmine\u002Fskills\u002F10-business-industry\u002Fdxy-task-clarify\u002FSKILL.md",[198],{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},[200,201,202,203,204,205,206,207],"clarify","dxy task clarify","task","pptx","整理","翻译","数据","总结"]