[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M69BEVT3N1AXH8BNV7K31W31":3,"skill-same:01M69BEVT3N1AXH8BNV7K31W31":61},{"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":28,"publishedAt":32,"linkOnly":33,"markdownContent":34,"markdownBody":35,"frontMatter":36,"refName":38,"commitSha":39,"skillDirectory":40,"forksCount":41,"repositoryHtmlUrl":42,"repositoryHomepageUrl":6,"ownerHtmlUrl":43,"ownerBlogUrl":44,"ownerDescription":45,"updatedAt":46,"install":47,"resources":60},"01M69BEVT3N1AXH8BNV7K31W31","write-report-measured",null,"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; preferred over write-report-segmentation.","microsoft\u002Fhealthcare-models-and-agents-workshop","microsoft","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F6154722?v=4","01M698VYDXZ6JW5WH3CRV650R8","Microsoft","01M69BEFF0TQHSY4MP1845AB1N",5,"MIT","https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fhealthcare-models-and-agents-workshop\u002Fblob\u002Fmain\u002Fchapters\u002Fchapter2-foundation-models\u002Fstarter\u002F.github\u002Fskills\u002Fwrite-report-measured\u002FSKILL.md",[18,23],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000016","图像与多媒体","SCENARIO","使用场景",{"categoryNo":24,"categoryName":25,"taxonomyType":26,"taxonomyTypeDesc":27},"01JBSEEDCAT000000000000034","行政与办公人员","PROFESSION","适用职业",[29,30,31],"write report measured","image","change","2026-11-10 10:12:30",false,"---\nname: write-report-measured\ndescription: 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; preferred over write-report-segmentation.\nargument-hint: image_path [prior_image_path]\n---\n\n# Write Measured Report\n\nBuild a descriptor factsheet, then let `write-report-simple` write the report from it.\n\n1. Invoke `write-imaging-factsheet` with the image and any prior image. Keep its factsheet and each scan's `audit_dir`.\n2. Invoke `write-report-simple` with:\n   - `findings`: each tumor component `present` when its total volume in the factsheet is above 0, otherwise `absent`.\n   - `measurements`, copied verbatim with units from the factsheet: lesion count; each lesion's diameters and tumor-core volume; total edema volume; and, with a prior, each change line (prior, current, percent change).\n   - With a prior, the prior exam as the comparison, using its date if the path shows one.\n3. Return the `write-report-simple` output unchanged, then end with the factsheet path and each scan's `audit_dir`.\n\n## Rules\n\n- Numbers come from the factsheet only. Do not compute, round, or convert them.\n- Lesion IDs are ranked separately for each scan. Do not match lesions across scans or attribute change to one lesion; change lines compare totals.\n- The descriptors assign no side or anatomy. Use your own read of the image, such as the overlay film grids (patient right on screen left), only for location wording.\n","# Write Measured Report\n\nBuild a descriptor factsheet, then let `write-report-simple` write the report from it.\n\n1. Invoke `write-imaging-factsheet` with the image and any prior image. Keep its factsheet and each scan's `audit_dir`.\n2. Invoke `write-report-simple` with:\n   - `findings`: each tumor component `present` when its total volume in the factsheet is above 0, otherwise `absent`.\n   - `measurements`, copied verbatim with units from the factsheet: lesion count; each lesion's diameters and tumor-core volume; total edema volume; and, with a prior, each change line (prior, current, percent change).\n   - With a prior, the prior exam as the comparison, using its date if the path shows one.\n3. Return the `write-report-simple` output unchanged, then end with the factsheet path and each scan's `audit_dir`.\n\n## Rules\n\n- Numbers come from the factsheet only. Do not compute, round, or convert them.\n- Lesion IDs are ranked separately for each scan. Do not match lesions across scans or attribute change to one lesion; change lines compare totals.\n- The descriptors assign no side or anatomy. Use your own read of the image, such as the overlay film grids (patient right on screen left), only for location wording.\n",{"name":5,"description":7,"argument-hint":37},"image_path [prior_image_path]","main","61aa7607e74cda657fcd824aa8261c91b0eba446","chapters\u002Fchapter2-foundation-models\u002Fstarter\u002F.github\u002Fskills\u002Fwrite-report-measured",2,"https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fhealthcare-models-and-agents-workshop","https:\u002F\u002Fgithub.com\u002Fmicrosoft","https:\u002F\u002Fopensource.microsoft.com","Open source projects and samples from Microsoft","2026-11-03 00:53:01",{"source":8,"skillName":5,"commands":48,"prompt":58,"zipAvailable":59},[49,52,55],{"tool":50,"command":51},"npx","npx skills add microsoft\u002Fhealthcare-models-and-agents-workshop --skill write-report-measured",{"tool":53,"command":54},"bunx","bunx skills add microsoft\u002Fhealthcare-models-and-agents-workshop --skill write-report-measured",{"tool":56,"command":57},"pnpm","pnpm dlx skills add microsoft\u002Fhealthcare-models-and-agents-workshop --skill write-report-measured","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fhealthcare-models-and-agents-workshop\u002Fblob\u002Fmain\u002Fchapters\u002Fchapter2-foundation-models\u002Fstarter\u002F.github\u002Fskills\u002Fwrite-report-measured\u002FSKILL.md",true,[],[62,72,81,89,99,105],{"skillNo":63,"name":64,"nameZh":6,"description":65,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":66,"categories":67,"keywords":70,"publishedAt":32},"01M69BF5DFRTGPQX0E6BYQNH0E","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\u002Fhealthcare-models-and-agents-workshop\u002Fblob\u002Fmain\u002Fchapters\u002Fchapter3-knowledge-sources\u002Fanswer-key\u002F.github\u002Fskills\u002Fwrite-report-with-ehr\u002FSKILL.md",[68,69],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":24,"categoryName":25,"taxonomyType":26,"taxonomyTypeDesc":27},[71,30,31],"write report with ehr",{"skillNo":73,"name":74,"nameZh":6,"description":75,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":76,"categories":77,"keywords":79,"publishedAt":32},"01M69BF3Z4EYEC327GH4D2E48A","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\u002Fhealthcare-models-and-agents-workshop\u002Fblob\u002Fmain\u002Fchapters\u002Fchapter3-knowledge-sources\u002Fanswer-key\u002F.github\u002Fskills\u002Flookup-patient-history\u002FSKILL.md",[78],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[80,30],"lookup patient history",{"skillNo":82,"name":64,"nameZh":6,"description":83,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":84,"categories":85,"keywords":87,"publishedAt":32},"01M69BF22K2HHE17TTJWEXRXVA","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\u002Fhealthcare-models-and-agents-workshop\u002Fblob\u002Fmain\u002Fchapters\u002Fchapter3-knowledge-sources\u002Fanswer-key-improved-compliance\u002F.github\u002Fskills\u002Fwrite-report-with-ehr\u002FSKILL.md",[86],{"categoryNo":24,"categoryName":25,"taxonomyType":26,"taxonomyTypeDesc":27},[71,30,88],"assistant",{"skillNo":90,"name":91,"nameZh":6,"description":92,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":93,"categories":94,"keywords":95,"publishedAt":32},"01M69BF052F87M2X96G7NBR4YH","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\u002Fhealthcare-models-and-agents-workshop\u002Fblob\u002Fmain\u002Fchapters\u002Fchapter3-knowledge-sources\u002Fanswer-key-improved-compliance\u002F.github\u002Fskills\u002Fwrite-imaging-factsheet-ehr\u002FSKILL.md",[],[96,97,30,98],"write imaging factsheet","write imaging factsheet ehr","cleanup",{"skillNo":100,"name":74,"nameZh":6,"description":101,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":102,"categories":103,"keywords":104,"publishedAt":32},"01M69BEYPBQANFHDN71X44GF98","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\u002Fhealthcare-models-and-agents-workshop\u002Fblob\u002Fmain\u002Fchapters\u002Fchapter3-knowledge-sources\u002Fanswer-key-improved-compliance\u002F.github\u002Fskills\u002Flookup-patient-history\u002FSKILL.md",[],[80,30],{"skillNo":106,"name":107,"nameZh":6,"description":108,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":109,"categories":110,"keywords":112,"publishedAt":32},"01M69BEXDBN6Y3VXNMBWZRFA5K","write-report-segmentation","Write a brain MRI report grounded in MedImageParse 3D tumor segmentation. Use for brain MRI reports when the medimageparse `segment` tool is available but `clean_segmentation` or `describe_lesions` is not.","https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fhealthcare-models-and-agents-workshop\u002Fblob\u002Fmain\u002Fchapters\u002Fchapter2-foundation-models\u002Fstarter\u002F.github\u002Fskills\u002Fwrite-report-segmentation\u002FSKILL.md",[111],{"categoryNo":24,"categoryName":25,"taxonomyType":26,"taxonomyTypeDesc":27},[113,30],"write report segmentation"]