model-evaluation-discipline
Skill9 starsChanged 4 months agoLicence not identifiedView on GitHub
Build a model evaluation that can actually fail — a mandatory baseline, a metric that matches the decision the output feeds, an interval instead of a point estimate, a test set treated as a consumable, subgroup breakdowns that expose what the aggregate hides, and negative controls that prove the pipeline is capable of producing a bad score. Use whenever a model is evaluated, compared, promoted, or reported, and whenever a number is used to justify shipping. Trigger on "accuracy", "F1", "AUC", "our model gets X%", "it beats the baseline", "which model should we ship", "the metric improved", "evaluate this model", "benchmark", "is this good", "calibration", "the offline numbers looked fine", "SOTA", or a model comparison with one number per model. Sibling of falsifiable-testing, which governs tests that can fail; this one governs metrics that can fail. It does not tune models and does not chase leaderboard positions.
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The file
We split this skill into the rules it gives an agent
3 rules. About 0 are common, 3 are distinctive, and 0 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈3
Distinctive · estimate
This author's own choices. Worth a look.
0
Very specific
Tied to their project's names, files or tools. Probably skip.
The licence could not be identified — read it at the source. We count its rules but don't quote them. Read them on GitHub.
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
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