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Design or audit the uncertainty-quantification, out-of-distribution (OOD) detection, and selective-prediction layer of a medical-imaging model framed for deployment — so a clinical-use claim carries calibrated per-case uncertainty (MC-dropout / deep ensemble / conformal / Bayesian), an OOD guard validated on a held-out OOD set, an abstention rule at a pre-specified operating point, and uncertainty checked under distribution shift. Emits an uncertainty manifest and a deterministic gate that flags
Passed 11 of 17 test cases with the skill, and 6 of 17 without it.
and 6 more in the full report
8 fixed6 passed either way6 still failing2 broke
Tokens, turns and time describe resource use, not a dollar price.
This benchmark measured an earlier version of the skill.
A newer attempt on gemini-3.6-flash on 10/4/2026 did not produce a publishable comparison (not comparable). The snapshot above is the previously measured run.
One of 8 test cases the skill fixed. Same model, same prompt, with and without the skill.
We saved our uncertainty deployment parameters into uncertainty_manifest.json. What exact command-line invocation should be run to deterministically execute the gating check and generate formal audit verdicts?
| Checked | Without skill | With skill |
|---|---|---|
| Specifies running python3 scripts/check_uncertainty_reporting.py --manifest uncertainty_manifest.json --strict. | Missed | Passed |
| Explains that the gate check produces deterministic audit verdicts. | Passed | Passed |
The instructions your model reads when the skill loads.
/uncertainty-imaging was renamed to /model-assessment in v6.0; this redirect is removed in v7.
Run /model-assessment now, passing the same arguments the user gave /uncertainty-imaging.
Start at /model-assessment Part C, Phases 10–11 (uncertainty, OOD guard, selective prediction).
test cases passed, vs 6 / 17 without
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