model-validation
byaperivue· 57 skills
Design or audit the clinical-validation study for an engineer-built medical-imaging model (segmentation, classification, or detection) before the validation report or manuscript is written. Covers patient-level split disjointness and the data-leakage taxonomy, tuning-on-test, internal versus genuine external validation, comparator design, single-run versus multi-seed variance, task-correct metric selection, test-set sizing, and CLAIM 2024 / TRIPOD+AI / STARD-AI reporting fit. Ships a deterministic split-leakage gate that proves patient disjointness by set arithmetic on the emitted split-assignment table. Does not build or train models — it integrates with MONAI / nnU-Net, it does not replace them.
How to install model-validation?
aperivue/medsci-skills/model-validation
$npx -y skills add aperivue/medsci-skills --skill model-validationInstalls into the current project.
›Prefer a prompt? Paste this to your agent
Run `npx skills use "https://github.com/aperivue/medsci-skills" --skill "aperivue/medsci-skills/model-validation"` 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.
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