Ab Test Designer

What it does

The ab-test-designer skill enables AI agents to generate comprehensive A/B test plans, including hypotheses, metrics, and statistical parameters. It helps product teams validate features by structuring experiments with clear success criteria and sample size calculations.

When to use it

• You need to design an experiment to compare two versions of a feature before development begins.
• Your team requires a structured hypothesis statement linking specific changes to expected business outcomes.
• You must define statistical significance levels, confidence intervals, or required sample sizes for your test.
• You want to identify key performance indicators (KPIs) that will determine the winner of an A/B test.

Key capabilities

• Generates structured A/B test hypotheses based on user behavior goals.
• Defines primary and secondary metrics for measuring experiment success.
• Calculates necessary sample sizes and duration estimates for valid statistical testing.
• Outlines null and alternative hypotheses to frame experimental logic clearly.

Example prompts

• "Create an A/B test plan for a new checkout button color, including the hypothesis, metrics, and required sample size."
• "Design an experiment to test the impact of adding a live chat widget on user retention rates."
• "Generate a structured A/B test proposal comparing two pricing page layouts with defined success criteria."

Tips & gotchas

Ensure you have access to historical conversion data or traffic estimates, as the skill relies on these inputs to calculate accurate sample sizes. Always validate generated statistical parameters with your data science team before launching live experiments.