Agent Skills: reporting-derived-metrics

Compute statistics, scores, and flags from samples that may be too small to support them — undefined dispersion returned as 0.0 and tripping a minimum threshold, sentinel choice (None vs 0 vs NaN), threshold blocks gated on "was this measured", reports that narrate findings from absent data, nullability as a public API change, `is None` vs truthiness, broad excepts that turn a metric bug into a normal-shaped result, and heavy-tailed samples where the mean points the opposite way to the median. Use when writing or reviewing a scoring/analysis pipeline, a z-score or outlier check, a quality or anomaly flag, a metrics rollup, a benchmark or cohort comparison, or any function that reduces a list of observations to one number a threshold or a report reads.