Design A/B tests with proper methodology, sample sizes, and success criteria.
/experiment-designer2-3 hrs → 15 min
Compared to doing it manually
/experiment-designerType this in Claude to run the skill
Shipping changes without testing is risky. Poorly designed tests produce invalid results.
Agent workflows chain multiple skills into one command.
/welcome/experiment-designer in Claude to run the skill/metric-framework-builderBuild comprehensive metrics frameworks using the AARRR pirate metrics or input/output methodology.
/funnel-analyzerDiagnose conversion funnel problems and generate data-backed improvement hypotheses.
/ab-test-analyzerInterpret experiment results with statistical rigor and clear ship/no-ship recommendations.
/ab-test-designerDesign statistically sound experiments with clear hypotheses and sample size calculations.
A/B tests compare two versions of the same thing. Experiments are broader — they can test hypotheses, validate assumptions, or explore new directions. All A/B tests are experiments, but not all experiments are A/B tests.
Start with a hypothesis ("We believe X will cause Y"). Define success metrics before you start. Minimize variables to isolate cause and effect. Set a timeline and commit to acting on results.
Failed experiments are successful learning. Document what you learned, update your assumptions, and decide: iterate, pivot, or move on. The only failed experiment is one you don't learn from.
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