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.
- License 许可
- MIT
- 最近更新
- 2026-10-30
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01 前言 Front Matter
- name
- write-imaging-factsheet
- description
- 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.
- argument-hint
- image_path [prior_image_path]
02 资源文件 1
- 参考文档template/factsheet.md1.8 KB
03 SKILL.md 正文
Write Imaging Factsheet
- For each image (current and prior, if supplied), call the
medimageparseMCP tools (VS Code may list them asmcp_medimageparse_<tool>):list_models, thensegmentwith modelmip3d-mri-brain-lesionand itstumor-components.promptsverbatim, in order. If the tools are not available or if the run fails, stop and tell the user to check themedimageparseserver 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. - Call
clean_segmentationon each returnedmask_path. Keep the original masks, clean masks, lesion-instance masks, and sidecars. - Call
describe_lesionswith the currentclean_path, passingprior_clean_pathwhen available. Keep all descriptor JSON files. - Fill template/factsheet.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
inputsandconventions; 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<mask-stem>.factsheet.mdbeside<mask-stem>.descriptors.json(replace.descriptors.jsonwith.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 ifdescribe_lesionsreturned none for it. Reply with the factsheet, then end with each scan's audit-log folder (audit_dirfromsegment), current first; the current folder holds the factsheet and images.
04 同仓库更多 Skills
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.
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.
write-report-with-ehr
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).
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.
lookup-patient-history
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.
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.