model-evaluation

model-evaluation is a Model training & evaluation skill for AI agents, published by Aperivue in the medsci-skills repository on GitHub. Skillspace rates model-evaluation C (72/100) and it passed the safety scan; the rating is based on static analysis only.

Compute and report task-correct held-out metrics for a trained medical-imaging model — segmentation (Dice plus a boundary metric such as HD95 or NSD, per structure), classification (AUROC plus AUPRC and sensitivity/specificity with bootstrap CIs at the deployment prevalence), detection (FROC or mAP with a stated IoU criterion), interactive/promptable segmentation (the interaction-count, convergence, and per-case-time axes a static Dice omits), or generative/synthesis image evaluation (similarity plus the downstream-task efficacy similarity alone cannot establish) — plus calibration and subgroup slices. Emits a per-case results table that analyze-stats turns into publication tables, and gates the metric choice against Metrics Reloaded, CLAIM 2024, and Park et al. 2024 (no pixel accuracy for segmentation, no bare accuracy under imbalance, no static Dice for an interactive method, no similarity-only claim for a generative model). Numbers come only from executed code, never hand-typed.

Grade

C

Quality

72 / 100

Safety

Passed

GitHub stars

201

Unproven: rated by static analysis only, not yet compared head-to-head.

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