Agent skill · data analytics · borghei
statistical-analyst
Applied statistics for business and product questions — test selection, assumption checks, power planning, effect sizes with intervals, multiplicity correction. Use when interpreting an experiment, sizing a study, or vetting a claim.
Why this skill is useful
Provides domain-specific knowledge on statistical test selection, assumptions, and power analysis that the AI wouldn't reliably generate on its own.
What it needs
About 8k tokens when loaded. Last updated 2026-07-21. 462 stars on the source repository.
What this skill does
Statistical Analyst Most bad statistics in business are not arithmetic errors. They are the wrong test on the right data, a null result reported as "no difference," a p-value mistaken for an effect size, or twenty comparisons run and the one that cleared 0.05 written up. This skill covers the applied path: pick the test the data shape actually calls for, check the assumptions that carry weight, size the study before running it, report effects with intervals rather than bare p-values, and say what you found to people who do not want a statistics lecture. Everything here runs on the Python standard library — the t, chi-square, and normal distributions are implemented directly, so there is no scipy dependency between a question and an answer. Because those implementations are hand-rolled, statscore.py --selftest verifies all of them against published reference values; run it once before trusting any result. When to use this skill An A/B test finished and someone needs to know whether to ship A study is being designed and nobody has computed how many observations it needs A stakeholder is quoting a p-value as if it were an effect size A "no difference" result is about to be reported from a study that was underpowered Several variants, metrics, or segments were compared and no correction was applied The data are skewed or outlier-heavy and the default t test is about to be run anyway Inputs the skill expects The business question and what decision it will change The outcome variable and its type (binary, continuous, count, ordinal, categorical) Group structure — how many groups, independent or paired, randomized or observational Observation counts per group, and the unit of measurement (user, session, order) The baseline value and the smallest effect worth acting on How many comparisons are in the family, and which metric was named primary before the data arrived Clarify First Before analyzing, confirm these inputs. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills borghei/statistical-analyst