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model-assessment

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Use when validating or evaluating a trained medical-imaging model. Audits split leakage and validation design, computes task-correct held-out metrics (Dice + HD95, AUROC + AUPRC, FROC, calibration), and covers uncertainty/OOD and Grad-CAM explainability, each with a gate.

skills/model-assessment/SKILL.md

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# Model-Assessment Skill

Assess a trained medical-imaging model (in-house, vendor or open-weights; segmentation,
classification or detection) in the order the evidence is built: **Part A** designs and audits the
validation study, **Part B** computes task-correct held-out metrics, **Part C** adds the
uncertainty / OOD / abstention layer a deployment claim needs, and **Part D** makes an
explainability analysis survive review. Run the parts the request needs (a Grad-CAM question starts
at Part D), but no metric headline is reported before the Part A split gate is green. Each part ends
in a stdlib gate whose verdict is reproduced from a file — never report a pass without running it.

Numbers come only from code executed on the supplied predictions, split table, or the researcher's
executed UQ/XAI code; if predictions or ground truth are missing, say so and stop. Integrate
MONAI / nnU-Net, MAPIE, captum, pytorch-grad-cam and pretrained OOD scorers by reference — never
reimplement them, never build or train the model, never run a model on real patient data.
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