byaperivue· 57 skills
Design or audit the data-preparation stage of a medical-imaging model — DICOM/NIfTI intake, resampling and intensity normalisation, and the augmentation plan — so the pipeline is leakage-safe before model-scaffold builds the training repo. Emits a declarative preprocessing manifest and a deterministic data-stage leakage gate that catches the leaks a split table cannot see: a dataset-level normaliser fit on non-train data, any data-fitted transform run before the split, and the same patient's slices crossing splits. Integrates MONAI / TorchIO transforms; it does not reimplement them, and it never runs preprocessing on real patient data.
$npx -y skills add aperivue/medsci-skills --skill preprocess-imagingInstalls into the current project.
Run `npx skills use "https://github.com/aperivue/medsci-skills" --skill "aperivue/medsci-skills/preprocess-imaging"` and follow the generated skill instructions now. Read its complete output, redirecting it to a temporary file first if necessary. Resolve relative paths from the supporting-files directory it provides.
Use the skills in "https://github.com/aperivue/medsci-skills" that are relevant to the current task. Run `npx skills add "https://github.com/aperivue/medsci-skills"` and select the relevant skills, then follow their instructions.