[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M69BEWRG2WTN2V1E6MCVB78V":3,"skill-same:01M69BEWRG2WTN2V1E6MCVB78V":69},{"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":23,"publishedAt":27,"linkOnly":28,"markdownContent":29,"markdownBody":30,"frontMatter":31,"refName":33,"commitSha":34,"skillDirectory":35,"forksCount":14,"repositoryHtmlUrl":36,"repositoryHomepageUrl":6,"ownerHtmlUrl":37,"ownerBlogUrl":38,"ownerDescription":39,"updatedAt":40,"install":41,"resources":54},"01M69BEWRG2WTN2V1E6MCVB78V","write-imaging-factsheet",null,"Generate a lesion-only imaging factsheet from a post-contrast T1 MRI, optionally comparing a prior image. Use when asked for a lesion factsheet or a descriptor-based imaging summary.","microsoft\u002FLAB730-ignite-2026-healthcare-agents","microsoft","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F6154722?v=4","01M698VYDXZ6JW5WH3CRV650R8","Microsoft","01M69BEN2JF8ERT1WJ3PE24FJ6",0,"MIT","https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002FLAB730-ignite-2026-healthcare-agents\u002Fblob\u002Fmain\u002Fchapters\u002Fchapter2-foundation-models\u002Fstarter\u002F.github\u002Fskills\u002Fwrite-imaging-factsheet\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000016","图像与多媒体","SCENARIO","使用场景",[24,25,26],"write imaging factsheet","image","generate","2026-11-10 10:12:30",false,"---\nname: write-imaging-factsheet\ndescription: Generate a lesion-only imaging factsheet from a post-contrast T1 MRI, optionally comparing a prior image. Use when asked for a lesion factsheet or a descriptor-based imaging summary.\nargument-hint: image_path [prior_image_path]\n---\n\n# Write Imaging Factsheet\n\n1. For each image (current and prior, if supplied), call the `medimageparse` MCP tools (VS Code may list them as `mcp_medimageparse_\u003Ctool>`): `list_models`, then `segment` with model `mip3d-mri-brain-lesion` and its `tumor-components.prompts` verbatim, in order. If the tools are not available or if the run fails, stop and tell the user to check the `medimageparse` server in the tool picker. Do not run the server's Python code, write your own segmentation or measurements, or write the factsheet with a script.\n2. Call `clean_segmentation` on each returned `mask_path`. Keep the original masks, clean masks, lesion-instance masks, and sidecars.\n3. Call `describe_lesions` with the current `clean_path`, passing `prior_clean_path` when available. Keep all descriptor JSON files.\n4. Fill [template\u002Ffactsheet.md](template\u002Ffactsheet.md) from the returned descriptors only. Replace ID placeholders with actual IDs; repeat lesion, pair, total, change, and active-limitation lines as needed. Copy numbers, units, categories, booleans, and reasons verbatim. Cite descriptor IDs in brackets on every fact line. Copy frontmatter from `inputs` and `conventions`; use null for a missing prior. Omit sections with no descriptors. For unavailable values, copy null and the reason; never invent a value. Do not compute, round, diagnose, assign anatomy or side, or imply lesion matching. Save `\u003Cmask-stem>.factsheet.md` beside `\u003Cmask-stem>.descriptors.json` (replace `.descriptors.json` with `.factsheet.md`). In the Images section, link the current scan's review images by file name (they sit beside the factsheet); drop the measurements line if `describe_lesions` returned none for it. Reply with the factsheet, then end with each scan's audit-log folder (`audit_dir` from `segment`), current first; the current folder holds the factsheet and images.","# Write Imaging Factsheet\n\n1. For each image (current and prior, if supplied), call the `medimageparse` MCP tools (VS Code may list them as `mcp_medimageparse_\u003Ctool>`): `list_models`, then `segment` with model `mip3d-mri-brain-lesion` and its `tumor-components.prompts` verbatim, in order. If the tools are not available or if the run fails, stop and tell the user to check the `medimageparse` server in the tool picker. Do not run the server's Python code, write your own segmentation or measurements, or write the factsheet with a script.\n2. Call `clean_segmentation` on each returned `mask_path`. Keep the original masks, clean masks, lesion-instance masks, and sidecars.\n3. Call `describe_lesions` with the current `clean_path`, passing `prior_clean_path` when available. Keep all descriptor JSON files.\n4. Fill [template\u002Ffactsheet.md](template\u002Ffactsheet.md) from the returned descriptors only. Replace ID placeholders with actual IDs; repeat lesion, pair, total, change, and active-limitation lines as needed. Copy numbers, units, categories, booleans, and reasons verbatim. Cite descriptor IDs in brackets on every fact line. Copy frontmatter from `inputs` and `conventions`; use null for a missing prior. Omit sections with no descriptors. For unavailable values, copy null and the reason; never invent a value. Do not compute, round, diagnose, assign anatomy or side, or imply lesion matching. Save `\u003Cmask-stem>.factsheet.md` beside `\u003Cmask-stem>.descriptors.json` (replace `.descriptors.json` with `.factsheet.md`). In the Images section, link the current scan's review images by file name (they sit beside the factsheet); drop the measurements line if `describe_lesions` returned none for it. Reply with the factsheet, then end with each scan's audit-log folder (`audit_dir` from `segment`), current first; the current folder holds the factsheet and images.",{"name":5,"description":7,"argument-hint":32},"image_path [prior_image_path]","main","30a1709ed305ae7f43775c5cb86f9006a03720f8","chapters\u002Fchapter2-foundation-models\u002Fstarter\u002F.github\u002Fskills\u002Fwrite-imaging-factsheet","https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002FLAB730-ignite-2026-healthcare-agents","https:\u002F\u002Fgithub.com\u002Fmicrosoft","https:\u002F\u002Fopensource.microsoft.com","Open source projects and samples from Microsoft","2026-10-30 02:36:25",{"source":8,"skillName":5,"commands":42,"prompt":52,"zipAvailable":53},[43,46,49],{"tool":44,"command":45},"npx","npx skills add microsoft\u002FLAB730-ignite-2026-healthcare-agents --skill write-imaging-factsheet",{"tool":47,"command":48},"bunx","bunx skills add microsoft\u002FLAB730-ignite-2026-healthcare-agents --skill write-imaging-factsheet",{"tool":50,"command":51},"pnpm","pnpm dlx skills add microsoft\u002FLAB730-ignite-2026-healthcare-agents --skill write-imaging-factsheet","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002FLAB730-ignite-2026-healthcare-agents\u002Fblob\u002Fmain\u002Fchapters\u002Fchapter2-foundation-models\u002Fstarter\u002F.github\u002Fskills\u002Fwrite-imaging-factsheet\u002FSKILL.md",true,[55,61,65],{"path":56,"resourceType":57,"resourceTypeDesc":58,"sizeBytes":59,"sourceHtmlUrl":60},"merge-templates.md","REFERENCE","参考文档",6928,"https:\u002F\u002Fgithub.com\u002Fapache\u002Fmagpie\u002Fblob\u002Fmain\u002Fplugins\u002Fmagpie-security\u002Fskills\u002Fissue-deduplicate\u002Fmerge-templates.md",{"path":62,"resourceType":57,"resourceTypeDesc":58,"sizeBytes":63,"sourceHtmlUrl":64},"template\u002Ffactsheet.md",1792,"https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002FLAB730-ignite-2026-healthcare-agents\u002Fblob\u002Fmain\u002Fchapters\u002Fchapter2-foundation-models\u002Fstarter\u002F.github\u002Fskills\u002Fwrite-imaging-factsheet\u002Ftemplate\u002Ffactsheet.md",{"path":66,"resourceType":57,"resourceTypeDesc":58,"sizeBytes":67,"sourceHtmlUrl":68},"tracker-backend.md",2778,"https:\u002F\u002Fgithub.com\u002Fapache\u002Fmagpie\u002Fblob\u002Fmain\u002Fplugins\u002Fmagpie-security\u002Fskills\u002Fissue-deduplicate\u002Ftracker-backend.md",[70,85,94,102,110,116],{"skillNo":71,"name":72,"nameZh":6,"description":73,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":74,"categories":75,"keywords":82,"publishedAt":27},"01M69BF6AY2E31N5HC0HM7S7YK","write-report-with-ehr","Write a context-aware brain MRI follow-up report from the patient's record, tool-computed lesion measurements, and change from the most recent prior image. Use when asked for a report that uses the patient's history, chart, or EHR.","https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002FLAB730-ignite-2026-healthcare-agents\u002Fblob\u002Fmain\u002Fchapters\u002Fchapter3-knowledge-sources\u002Fanswer-key\u002F.github\u002Fskills\u002Fwrite-report-with-ehr\u002FSKILL.md",[76,77],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":78,"categoryName":79,"taxonomyType":80,"taxonomyTypeDesc":81},"01JBSEEDCAT000000000000034","行政与办公人员","PROFESSION","适用职业",[83,25,84],"write report with ehr","change",{"skillNo":86,"name":87,"nameZh":6,"description":88,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":89,"categories":90,"keywords":92,"publishedAt":27},"01M69BF4TR0Z36MNW16DZVZ9YE","lookup-patient-history","Load a synthetic patient EHR record (demographics, diagnosis, treatments, medications, expected post-treatment findings, prior imaging studies and reports, pending orders) for a given brain MRI image. Invoke as the first step of any context-aware reporting flow that needs patient history. No model calls.","https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002FLAB730-ignite-2026-healthcare-agents\u002Fblob\u002Fmain\u002Fchapters\u002Fchapter3-knowledge-sources\u002Fanswer-key\u002F.github\u002Fskills\u002Flookup-patient-history\u002FSKILL.md",[91],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[93,25],"lookup patient history",{"skillNo":95,"name":72,"nameZh":6,"description":96,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":97,"categories":98,"keywords":100,"publishedAt":27},"01M69BF2XGK9Z7FCTPSP67TP2C","Write a context-aware brain MRI report using the patient chart and mandatory tool-computed linear and volumetric measurements. Use for EHR reporting with Radiology Assistant (EHR).","https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002FLAB730-ignite-2026-healthcare-agents\u002Fblob\u002Fmain\u002Fchapters\u002Fchapter3-knowledge-sources\u002Fanswer-key-improved-compliance\u002F.github\u002Fskills\u002Fwrite-report-with-ehr\u002FSKILL.md",[99],{"categoryNo":78,"categoryName":79,"taxonomyType":80,"taxonomyTypeDesc":81},[25,101],"assistant",{"skillNo":103,"name":104,"nameZh":6,"description":105,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":106,"categories":107,"keywords":108,"publishedAt":27},"01M69BF14X7RFX39K47N9DAXD9","write-imaging-factsheet-ehr","Produce an EHR imaging factsheet with mandatory segmentation, cleanup, linear dimensions, and volumes. Use when write-report-with-ehr requests imaging evidence or when explicitly asked for this factsheet.","https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002FLAB730-ignite-2026-healthcare-agents\u002Fblob\u002Fmain\u002Fchapters\u002Fchapter3-knowledge-sources\u002Fanswer-key-improved-compliance\u002F.github\u002Fskills\u002Fwrite-imaging-factsheet-ehr\u002FSKILL.md",[],[24,25,109],"cleanup",{"skillNo":111,"name":87,"nameZh":6,"description":112,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":113,"categories":114,"keywords":115,"publishedAt":27},"01M69BEZPTQH6XF1025SGE3TRF","Load the synthetic patient chart and verbatim prior reports for EHR reporting. Use before measuring the selected current and prior images; this lookup does not perform segmentation or measurements.","https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002FLAB730-ignite-2026-healthcare-agents\u002Fblob\u002Fmain\u002Fchapters\u002Fchapter3-knowledge-sources\u002Fanswer-key-improved-compliance\u002F.github\u002Fskills\u002Flookup-patient-history\u002FSKILL.md",[],[93,25],{"skillNo":117,"name":118,"nameZh":6,"description":119,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":120,"categories":121,"keywords":124,"publishedAt":27},"01M69BEY7Q35J8DCZWZYPCN92M","write-report-measured","Write a brain MRI report with tool-computed lesion measurements and, when a prior image is supplied, change from that prior. Use for brain MRI reports when the medimageparse `segment`, `clean_segmentation`, and `describe_lesions` tools are available.","https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002FLAB730-ignite-2026-healthcare-agents\u002Fblob\u002Fmain\u002Fchapters\u002Fchapter2-foundation-models\u002Fstarter\u002F.github\u002Fskills\u002Fwrite-report-measured\u002FSKILL.md",[122,123],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":78,"categoryName":79,"taxonomyType":80,"taxonomyTypeDesc":81},[125,25,84],"write report measured"]