[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M69BF14X7RFX39K47N9DAXD9":3,"skill-same:01M69BF14X7RFX39K47N9DAXD9":56},{"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":18,"publishedAt":22,"linkOnly":23,"markdownContent":24,"markdownBody":25,"frontMatter":26,"refName":28,"commitSha":29,"skillDirectory":30,"forksCount":14,"repositoryHtmlUrl":31,"repositoryHomepageUrl":6,"ownerHtmlUrl":32,"ownerBlogUrl":33,"ownerDescription":34,"updatedAt":35,"install":36,"resources":49},"01M69BF14X7RFX39K47N9DAXD9","write-imaging-factsheet-ehr",null,"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.","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\u002Fchapter3-knowledge-sources\u002Fanswer-key-improved-compliance\u002F.github\u002Fskills\u002Fwrite-imaging-factsheet-ehr\u002FSKILL.md",[],[19,20,21],"write imaging factsheet","image","cleanup","2026-11-10 10:12:30",false,"---\nname: write-imaging-factsheet-ehr\ndescription: 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.\nargument-hint: image_path [prior_image_path]\n---\n\n# Write Imaging Factsheet for EHR\n\nUse the four `medimageparse` MCP tools; VS Code may display them as `mcp_medimageparse_\u003Ctool>`.\n\n## Required tool sequence\n\nPerform these calls for the supplied images in this request. Loading this skill or reading an existing audit folder is not a substitute.\n\n1. Resolve `image_path` and any `prior_image_path` to absolute paths.\n2. Call `list_models` once. Select model `mip3d-mri-brain-lesion` and its `views[\"tumor-components\"].prompts`. Pass the full prompts verbatim, in their returned order.\n3. Call `segment` for the current image with that model and those prompts. Keep its `audit_dir`, `mask_path`, and review-image paths. Call `clean_segmentation` with that returned `mask_path`, using the default cleanup parameters; keep the returned `clean_path`.\n4. If a prior image was supplied, call `segment` and then `clean_segmentation` for it in the same way, with the same model, prompts, and cleanup parameters. Keep the prior paths separately; never swap them with the current paths.\n5. Call `describe_lesions` once with the current `clean_path`. Also pass `prior_clean_path` when a prior was supplied; omit it otherwise. Keep `descriptors_path`, `inputs`, `conventions`, `descriptors`, and the returned review-image paths.\n6. Fill [template\u002Ffactsheet.md](.\u002Ftemplate\u002Ffactsheet.md) from the returned current descriptors and aggregate change descriptors. Save it beside `descriptors_path`, replacing `.descriptors.json` with `.factsheet.md`. Preserve all masks, sidecars, and descriptor JSON files.\n\nIf any required tool is unavailable or fails, stop and identify the step, image, and error; tell the user to check the `medimageparse` server or the reported input problem. Do not return a success-shaped factsheet or allow the parent to emit `\u003Creport>`. Do not run the server's Python code, write scripts, compute your own measurements, or substitute an earlier run.\n\n## Filling and checking the factsheet\n\n- Replace placeholders with actual descriptor IDs and values. Repeat the lesion, pair, volume, change, and active-limitation lines for all applicable descriptors. Cite an ID on every fact line.\n- For every current lesion, copy **both `diameters_cm` entries** from `L*.bidimensional` and both `value.AP` and `value.TR` from `L*.extent`. The bidimensional `value` and `unit` describe the product in cm2, not a linear dimension. Never use `unrounded_diameters_cm`.\n- Include the enhancing, necrotic, and tumor-core volumes for every current lesion, all returned total volumes, the lesion count, and sum of products. With a prior, include every `change.*` line with its prior\u002Fcurrent values, units, percent change, and any reason.\n- Include returned diameters even when `measurable` is false. That flag is not permission to omit dimensions.\n- Copy numbers, units, categories, booleans, and reasons verbatim. For explicit unavailable values, preserve `null` and the reason. Never fill a missing field with zero or infer a reason.\n- A successful zero-lesion result has no per-lesion descriptors: omit those sections but keep the zero count, totals, change lines when present, and limitations. It is not a tool failure.\n- Do not compute, round, convert, diagnose, assign anatomy or side, or match lesion IDs across scans. Do not copy historical report sizes into this factsheet.\n- Copy frontmatter from `inputs` and `conventions`, using valid YAML quoting for paths and literal `null` for a missing prior. Prior lesion descriptors are saved at `inputs.prior_descriptors_path`; the returned lesion inventory is the current scan's, not a matched current\u002Fprior inventory.\n- Link the current scan's review images by filename in the Images section. Omit its measurements-image line if no measurement image was returned for the current scan, even if an image was returned for the prior.\n- Before returning, check every current lesion has its diameter pair, AP\u002FTR pair, and component volumes, and that required totals and applicable changes are present. Repair omissions from the same descriptor result or its saved JSON, not by rerunning segmentation. If a required source field is missing, stop with an explicit error naming it.\n\n## Handoff\n\n- **When called by `write-report-with-ehr`:** retain the factsheet and pass its contents, saved path, `descriptors_path`, and each scan's `audit_dir` back to the parent workflow, current first. Continue to the parent's report-writing step. Do not send the factsheet as the final response or end the task with audit folders.\n- **When explicitly requested on its own:** reply with the completed factsheet, then its saved path and each scan's audit-log folder, current first.\n","# Write Imaging Factsheet for EHR\n\nUse the four `medimageparse` MCP tools; VS Code may display them as `mcp_medimageparse_\u003Ctool>`.\n\n## Required tool sequence\n\nPerform these calls for the supplied images in this request. Loading this skill or reading an existing audit folder is not a substitute.\n\n1. Resolve `image_path` and any `prior_image_path` to absolute paths.\n2. Call `list_models` once. Select model `mip3d-mri-brain-lesion` and its `views[\"tumor-components\"].prompts`. Pass the full prompts verbatim, in their returned order.\n3. Call `segment` for the current image with that model and those prompts. Keep its `audit_dir`, `mask_path`, and review-image paths. Call `clean_segmentation` with that returned `mask_path`, using the default cleanup parameters; keep the returned `clean_path`.\n4. If a prior image was supplied, call `segment` and then `clean_segmentation` for it in the same way, with the same model, prompts, and cleanup parameters. Keep the prior paths separately; never swap them with the current paths.\n5. Call `describe_lesions` once with the current `clean_path`. Also pass `prior_clean_path` when a prior was supplied; omit it otherwise. Keep `descriptors_path`, `inputs`, `conventions`, `descriptors`, and the returned review-image paths.\n6. Fill [template\u002Ffactsheet.md](.\u002Ftemplate\u002Ffactsheet.md) from the returned current descriptors and aggregate change descriptors. Save it beside `descriptors_path`, replacing `.descriptors.json` with `.factsheet.md`. Preserve all masks, sidecars, and descriptor JSON files.\n\nIf any required tool is unavailable or fails, stop and identify the step, image, and error; tell the user to check the `medimageparse` server or the reported input problem. Do not return a success-shaped factsheet or allow the parent to emit `\u003Creport>`. Do not run the server's Python code, write scripts, compute your own measurements, or substitute an earlier run.\n\n## Filling and checking the factsheet\n\n- Replace placeholders with actual descriptor IDs and values. Repeat the lesion, pair, volume, change, and active-limitation lines for all applicable descriptors. Cite an ID on every fact line.\n- For every current lesion, copy **both `diameters_cm` entries** from `L*.bidimensional` and both `value.AP` and `value.TR` from `L*.extent`. The bidimensional `value` and `unit` describe the product in cm2, not a linear dimension. Never use `unrounded_diameters_cm`.\n- Include the enhancing, necrotic, and tumor-core volumes for every current lesion, all returned total volumes, the lesion count, and sum of products. With a prior, include every `change.*` line with its prior\u002Fcurrent values, units, percent change, and any reason.\n- Include returned diameters even when `measurable` is false. That flag is not permission to omit dimensions.\n- Copy numbers, units, categories, booleans, and reasons verbatim. For explicit unavailable values, preserve `null` and the reason. Never fill a missing field with zero or infer a reason.\n- A successful zero-lesion result has no per-lesion descriptors: omit those sections but keep the zero count, totals, change lines when present, and limitations. It is not a tool failure.\n- Do not compute, round, convert, diagnose, assign anatomy or side, or match lesion IDs across scans. Do not copy historical report sizes into this factsheet.\n- Copy frontmatter from `inputs` and `conventions`, using valid YAML quoting for paths and literal `null` for a missing prior. Prior lesion descriptors are saved at `inputs.prior_descriptors_path`; the returned lesion inventory is the current scan's, not a matched current\u002Fprior inventory.\n- Link the current scan's review images by filename in the Images section. Omit its measurements-image line if no measurement image was returned for the current scan, even if an image was returned for the prior.\n- Before returning, check every current lesion has its diameter pair, AP\u002FTR pair, and component volumes, and that required totals and applicable changes are present. Repair omissions from the same descriptor result or its saved JSON, not by rerunning segmentation. If a required source field is missing, stop with an explicit error naming it.\n\n## Handoff\n\n- **When called by `write-report-with-ehr`:** retain the factsheet and pass its contents, saved path, `descriptors_path`, and each scan's `audit_dir` back to the parent workflow, current first. Continue to the parent's report-writing step. Do not send the factsheet as the final response or end the task with audit folders.\n- **When explicitly requested on its own:** reply with the completed factsheet, then its saved path and each scan's audit-log folder, current first.\n",{"name":5,"description":7,"argument-hint":27},"image_path [prior_image_path]","main","30a1709ed305ae7f43775c5cb86f9006a03720f8","chapters\u002Fchapter3-knowledge-sources\u002Fanswer-key-improved-compliance\u002F.github\u002Fskills\u002Fwrite-imaging-factsheet-ehr","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":37,"prompt":47,"zipAvailable":48},[38,41,44],{"tool":39,"command":40},"npx","npx skills add microsoft\u002FLAB730-ignite-2026-healthcare-agents --skill write-imaging-factsheet-ehr",{"tool":42,"command":43},"bunx","bunx skills add microsoft\u002FLAB730-ignite-2026-healthcare-agents --skill write-imaging-factsheet-ehr",{"tool":45,"command":46},"pnpm","pnpm dlx skills add microsoft\u002FLAB730-ignite-2026-healthcare-agents --skill write-imaging-factsheet-ehr","安装技能，技能地址：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",true,[50],{"path":51,"resourceType":52,"resourceTypeDesc":53,"sizeBytes":54,"sourceHtmlUrl":55},"template\u002Ffactsheet.md","REFERENCE","参考文档",1823,"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\u002Ftemplate\u002Ffactsheet.md",[57,76,85,93,99,109],{"skillNo":58,"name":59,"nameZh":6,"description":60,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":61,"categories":62,"keywords":73,"publishedAt":22},"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",[63,68],{"categoryNo":64,"categoryName":65,"taxonomyType":66,"taxonomyTypeDesc":67},"01JBSEEDCAT000000000000016","图像与多媒体","SCENARIO","使用场景",{"categoryNo":69,"categoryName":70,"taxonomyType":71,"taxonomyTypeDesc":72},"01JBSEEDCAT000000000000034","行政与办公人员","PROFESSION","适用职业",[74,20,75],"write report with ehr","change",{"skillNo":77,"name":78,"nameZh":6,"description":79,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":80,"categories":81,"keywords":83,"publishedAt":22},"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",[82],{"categoryNo":64,"categoryName":65,"taxonomyType":66,"taxonomyTypeDesc":67},[84,20],"lookup patient history",{"skillNo":86,"name":59,"nameZh":6,"description":87,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":88,"categories":89,"keywords":91,"publishedAt":22},"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",[90],{"categoryNo":69,"categoryName":70,"taxonomyType":71,"taxonomyTypeDesc":72},[20,92],"assistant",{"skillNo":94,"name":78,"nameZh":6,"description":95,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":96,"categories":97,"keywords":98,"publishedAt":22},"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",[],[84,20],{"skillNo":100,"name":101,"nameZh":6,"description":102,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":103,"categories":104,"keywords":107,"publishedAt":22},"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",[105,106],{"categoryNo":64,"categoryName":65,"taxonomyType":66,"taxonomyTypeDesc":67},{"categoryNo":69,"categoryName":70,"taxonomyType":71,"taxonomyTypeDesc":72},[108,20,75],"write report measured",{"skillNo":110,"name":111,"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":116,"publishedAt":22},"01M69BEWRG2WTN2V1E6MCVB78V","write-imaging-factsheet","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.","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",[115],{"categoryNo":64,"categoryName":65,"taxonomyType":66,"taxonomyTypeDesc":67},[19,20,117],"generate"]