[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M7132D57WGJBJ7RHA1JCZP4V":3,"skill-same:01M7132D57WGJBJ7RHA1JCZP4V":85},{"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":29,"publishedAt":47,"linkOnly":48,"markdownContent":49,"markdownBody":50,"frontMatter":51,"refName":52,"commitSha":53,"skillDirectory":54,"forksCount":55,"repositoryHtmlUrl":56,"repositoryHomepageUrl":6,"ownerHtmlUrl":57,"ownerBlogUrl":58,"ownerDescription":59,"updatedAt":60,"install":61,"resources":74},"01M7132D57WGJBJ7RHA1JCZP4V","email-batch-test",null,"Use when Invoice-Downloader work involves mailbox batch tests, truth audit, regression validation, P0\u002FP1\u002FP2 decisions, packaged frontend acceptance, or clean reruns.","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\u002Femail-batch-test\u002Ffdf8193fab63b418\u002Femail-batch-test\u002FSKILL.md",[18,23,26],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000005","邮件与沟通","SCENARIO","使用场景",{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":27,"categoryName":28,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",[30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46],"test","email batch test","email","batch","acceptance","audit","validation","regression","work","decisions","use","clean","mailbox","tests","invoice","when","frontend","2026-11-10 10:12:30",false,"---\nname: email-batch-test\ndescription: Use when Invoice-Downloader work involves mailbox batch tests, truth audit, regression validation, P0\u002FP1\u002FP2 decisions, packaged frontend acceptance, or clean reruns.\n---\n\n# Email Batch Test\n\n## Current Authority\n\nRead `AGENTS.md`, then read both files under `references\u002F`.\n\nThe canonical QQ full-range truth manifest is:\n\n`test_dataset\u002Fqq_20251125_20260614_rebuilt_20260614_1035\u002Ftruth_manifest.json`\n\nIts required contract is:\n\n- `included_count = 215`\n- `pending_review_count = 0`\n- `finalized = true`\n- date range `2025-11-25` through `2026-06-14`\n\nDo not substitute an older manifest or describe a non-final truth set as authoritative.\n\n## Required Gate\n\n1. Build the Windows package from the exact candidate revision.\n2. Create a fresh child of `manual_frontend_runs` and clear only its output-scoped state.\n3. Preserve `%APPDATA%\\InvoiceFlowAI\\user_settings.json` and its encrypted credentials.\n4. Launch the packaged EXE visibly through `scripts\u002Ffrontend_truth_acceptance.py`.\n5. Require finalized run evidence and a fresh `batch_validation.py` audit.\n6. Accept only an explicit result with P0, P1, P2, and manual counts all zero.\n\nNo result may be reported as probable, suspected, or unconfirmed. Missing authority is a failed gate.\n\n## Secondary Mailboxes\n\nRun another mailbox only when its local manifest is present, finalized, has zero pending review, and the task explicitly requires that scope. Never depend on a stale or missing worktree path.\n","# Email Batch Test\n\n## Current Authority\n\nRead `AGENTS.md`, then read both files under `references\u002F`.\n\nThe canonical QQ full-range truth manifest is:\n\n`test_dataset\u002Fqq_20251125_20260614_rebuilt_20260614_1035\u002Ftruth_manifest.json`\n\nIts required contract is:\n\n- `included_count = 215`\n- `pending_review_count = 0`\n- `finalized = true`\n- date range `2025-11-25` through `2026-06-14`\n\nDo not substitute an older manifest or describe a non-final truth set as authoritative.\n\n## Required Gate\n\n1. Build the Windows package from the exact candidate revision.\n2. Create a fresh child of `manual_frontend_runs` and clear only its output-scoped state.\n3. Preserve `%APPDATA%\\InvoiceFlowAI\\user_settings.json` and its encrypted credentials.\n4. Launch the packaged EXE visibly through `scripts\u002Ffrontend_truth_acceptance.py`.\n5. Require finalized run evidence and a fresh `batch_validation.py` audit.\n6. Accept only an explicit result with P0, P1, P2, and manual counts all zero.\n\nNo result may be reported as probable, suspected, or unconfirmed. Missing authority is a failed gate.\n\n## Secondary Mailboxes\n\nRun another mailbox only when its local manifest is present, finalized, has zero pending review, and the task explicitly requires that scope. Never depend on a stale or missing worktree path.\n",{"name":5,"description":7},"mine","1e7390741dffd3d5041fca4ebc056071ee3ba137","archives\u002Fuser-installed-skills\u002Femail-batch-test\u002Ffdf8193fab63b418\u002Femail-batch-test",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":62,"prompt":72,"zipAvailable":73},[63,66,69],{"tool":64,"command":65},"npx","npx skills add EthanYoQ\u002FSkill-hub --skill email-batch-test",{"tool":67,"command":68},"bunx","bunx skills add EthanYoQ\u002FSkill-hub --skill email-batch-test",{"tool":70,"command":71},"pnpm","pnpm dlx skills add EthanYoQ\u002FSkill-hub --skill email-batch-test","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FEthanYoQ\u002FSkill-hub\u002Fblob\u002Fmine\u002Farchives\u002Fuser-installed-skills\u002Femail-batch-test\u002Ffdf8193fab63b418\u002Femail-batch-test\u002FSKILL.md",true,[75,81],{"path":76,"resourceType":77,"resourceTypeDesc":78,"sizeBytes":79,"sourceHtmlUrl":80},"references\u002Fregression-gate.md","REFERENCE","参考文档",1236,"https:\u002F\u002Fgithub.com\u002FEthanYoQ\u002FSkill-hub\u002Fblob\u002Fmine\u002Farchives\u002Fuser-installed-skills\u002Femail-batch-test\u002Ffdf8193fab63b418\u002Femail-batch-test\u002Freferences\u002Fregression-gate.md",{"path":82,"resourceType":77,"resourceTypeDesc":78,"sizeBytes":83,"sourceHtmlUrl":84},"references\u002Ftest-standards.md",974,"https:\u002F\u002Fgithub.com\u002FEthanYoQ\u002FSkill-hub\u002Fblob\u002Fmine\u002Farchives\u002Fuser-installed-skills\u002Femail-batch-test\u002Ffdf8193fab63b418\u002Femail-batch-test\u002Freferences\u002Ftest-standards.md",[86,112,142,164,182,197],{"skillNo":87,"name":88,"nameZh":6,"description":89,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":90,"categories":91,"keywords":93,"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",[92],{"categoryNo":27,"categoryName":28,"taxonomyType":21,"taxonomyTypeDesc":22},[94,95,96,97,98,99,45,100,33,101,102,103,104,105,106,107,108,109,110,111],"social","xhs pharma social listening","social listening","xhs","medical","counting","collection","analyze","notes","run","opencli","fallback","captcha","agent reach","evidence","for","collect","produce",{"skillNo":113,"name":114,"nameZh":6,"description":115,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":116,"categories":117,"keywords":124,"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",[118,119],{"categoryNo":27,"categoryName":28,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":120,"categoryName":121,"taxonomyType":122,"taxonomyTypeDesc":123},"01JBSEEDCAT000000000000037","律师与法务","PROFESSION","适用职业",[125,126,127,128,129,130,131,132,45,133,134,135,35,136,137,138,109,139,140,141],"healthcare","phi","compliance","healthcare phi compliance","pii","compliance patterns","information","access","encryption","code","common","data","patterns","Access Control","system","applications","Auditing Access Control",{"skillNo":143,"name":144,"nameZh":6,"description":145,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":146,"categories":147,"keywords":149,"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",[148],{"categoryNo":27,"categoryName":28,"taxonomyType":21,"taxonomyTypeDesc":22},[125,150,151,152,153,154,155,45,156,157,158,159,126,109,160,161,43,162,40,163],"eval","harness","healthcare eval harness","eval harness","clinical","patient","workflow","Evaluation","Safety","integrity","deployment","accuracy","deployments","super",{"skillNo":165,"name":166,"nameZh":6,"description":167,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":168,"categories":169,"keywords":171,"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",[170],{"categoryNo":27,"categoryName":28,"taxonomyType":21,"taxonomyTypeDesc":22},[125,172,137,173,174,98,154,175,45,176,177,178,158,136,179,180,181,109,140,40],"EMR","healthcare emr patterns","decision","workflows","support","clinical decision support","features","ui","as","development",{"skillNo":183,"name":184,"nameZh":6,"description":185,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":186,"categories":187,"keywords":189,"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",[188],{"categoryNo":27,"categoryName":28,"taxonomyType":21,"taxonomyTypeDesc":22},[125,137,190,174,154,172,175,45,191,192,176,177,193,181,139,194,40,195,163,196],"healthcare cdss patterns","scoring","interaction","severity","classification","alert","decision support",{"skillNo":198,"name":199,"nameZh":6,"description":200,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":201,"categories":202,"keywords":204,"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",[203],{"categoryNo":27,"categoryName":28,"taxonomyType":21,"taxonomyTypeDesc":22},[205,206,207,208,209,210,211,212],"clarify","dxy task clarify","task","pptx","整理","翻译","数据","总结"]