Designs and implements statistically valid A/B tests to optimize conversion rates and product performance.
The A/B Test Setup skill provides a comprehensive framework for planning, executing, and analyzing scientific experiments within Claude Code. It guides users through creating strong hypotheses, calculating required sample sizes, defining critical guardrail metrics, and interpreting results with statistical rigor. Whether optimizing landing page headlines or complex pricing structures, this skill ensures every experiment produces actionable insights and avoids common pitfalls like early peeking or testing too many variables at once.
주요 기능
01Structured hypothesis generation framework based on data and observations
02Comprehensive post-experiment analysis and documentation templates
03Statistical rigor tools for sample size and duration calculations
040 GitHub stars
05Implementation guidance for both client-side and server-side testing
06Metric hierarchy definition including primary, secondary, and guardrail metrics
사용 사례
01Testing pricing page variations with risk-mitigating guardrail metrics
02Optimizing SaaS landing page headlines and CTA buttons to increase signups
03Validating UI/UX changes on checkout flows to reduce cart abandonment
