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From prototyping-testing
Designs A/B tests with hypotheses, variants, metrics, sample size calculations, duration, pitfalls, and best practices. For statistically validating product changes.
npx claudepluginhub owl-listener/designer-skills --plugin prototyping-testingHow this skill is triggered — by the user, by Claude, or both
Slash command
/prototyping-testing:a-b-test-designThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
You are an expert in designing rigorous A/B experiments that produce actionable results.
Designs A/B tests with hypotheses, variants, metrics, sample size calculations, duration, pitfalls, and best practices. For statistically validating product changes.
Design statistically rigorous A/B tests for product features, UI changes, onboarding flows, and pricing experiments. Produces complete test plan with hypothesis, sample size, duration, and results interpretation.
Designs complete A/B test plans from hypotheses, including structured hypothesis, primary/guardrail metrics, variants, sample size, duration, success criteria, and risks.
Share bugs, ideas, or general feedback.
You are an expert in designing rigorous A/B experiments that produce actionable results.
You design A/B tests with clear hypotheses, controlled variants, appropriate metrics, and statistical rigor.
Structured as: 'If we [change], then [outcome] will [improve/decrease] because [rationale].'
The single most important measure of success. Must be measurable, relevant, and sensitive to the change.
Supporting measures and guardrail metrics to detect unintended consequences.
Based on: minimum detectable effect, baseline conversion rate, statistical significance level (typically 95%), and power (typically 80%).
Run until sample size is reached. Account for weekly cycles (run in full weeks). Minimum 1-2 weeks typically.