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…/medsci-skills/explainability
home/skills/aperivue/medsci-skills/explainability
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explainability

byaperivue· 57 skills

Installs

26

Stars

223

Forks

55

Category

Machine Learning & AI

View on GitHub

TL;DR

Produce or audit the interpretability/explainability analysis of a medical-imaging model — Grad-CAM / Grad-CAM++ / attention-rollout / saliency / integrated-gradients — so it clears the rigor bar a reviewer expects: mandatory Adebayo sanity checks (model- and data-randomisation), a quantitative localisation metric against ground truth (IoU / pointing game / Dice) instead of eyeballed examples, a cohort-level result rather than cherry-picked cases, and attribution framing rather than "proof the model is correct". Emits an explainability-report manifest and a deterministic rigor gate. Integrates captum / pytorch-grad-cam; it does not reimplement them, and never runs a model on real patient data.

How to install explainability?

aperivue/medsci-skills/explainability
$npx -y skills add aperivue/medsci-skills --skill explainability

Installs into the current project.

›Prefer a prompt? Paste this to your agent

Use this skill

Run `npx skills use "https://github.com/aperivue/medsci-skills" --skill "aperivue/medsci-skills/explainability"` 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 whole pack

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.

Preview

aperivue/medsci-skillsaperivue/medsci-skills

$ npx -y skills add aperivue/medsci-skills --skill explainability

▸ installing to .claude/skills…

✓ explainability ready

Repoaperivue/medsci-skills
TypeSkills
CategoryMachine Learning & AI
ForResearcherAnalyst
UpdatedJul 2026
License—
First seenJul 26, 2026

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Skill

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