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experiment-readout

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Analyse a finished A/B test and write an honest results readout with real statistics. Use when asked to read out an A/B test, analyse experiment results, check if a result is statistically significant, or decide ship/no-ship from test data. Produces a readout — the computed lift, p-value & confidence interval, a significance verdict, guardrail check, and a clear ship / no-ship / iterate recommendation. Includes a stdlib significance calculator.

skills/experiment-readout/SKILL.md

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# Experiment Readout Skill

A test result is only a decision if the statistics are sound — and "variant looks higher" is not a
result. This skill computes the lift, the p-value, and a confidence interval from the raw counts, checks
the guardrails, and writes an honest readout with a clear ship/no-ship call — flagging the traps
(peeking, underpowered, novelty, a significant but tiny effect) that make teams ship noise.

## Required Inputs

Ask for these only if they aren't already provided:

- **The metric & data** — for a conversion test: users and conversions per variant (control vs. treatment). For a continuous metric: mean, SD, and n per variant.
- **The hypothesis** — what you expected and the minimum effect that matters.
- **Guardrail metrics** — what shouldn't get worse (revenue, latency, retention).
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