name: ab-test-designer description: "Design robust A/B test experiments. Use when testing a new feature, validating a hypothesis, or optimizing conversion rates." argument-hint: "[feature/change to test]" metadata: version: "1.0.1"

Domain Context

This skill implements a proven product management framework. The approach combines best practices from industry leaders and is designed for practical application in day-to-day PM work.

Input Requirements

• Context about your product, feature, or problem
• Relevant data, research, or constraints (recommended but optional)
• Clear articulation of what you're trying to achieve

A/B Test Designer

When to Use

• Testing a new feature or design variation
• Validating a hypothesis before full rollout
• Optimizing conversion rates or key metrics
• Choosing between multiple design approaches
• Need to make a data-driven decision on a change

What This Skill Does

Helps you design rigorous A/B tests with clear hypotheses, success metrics, sample size calculations, and analysis plans.

Instructions

Help me design an A/B test for [feature/change]. Include:

1. Hypothesis

• Current situation and metrics
• Proposed change
• Expected impact and why

1. Test Design

• Primary success metric
• Secondary metrics
• Sample size needed
• Test duration
• User segments to include/exclude

1. Variants

• Control (A): current experience
• Variant (B): new experience
• Any additional variants (C, D, etc.)

1. Risks and Controls

• Potential negative impacts
• Guardrail metrics
• When to stop the test early

1. Analysis Plan

• Statistical significance threshold
• How to handle edge cases
• Decision criteria

Feature context: [Add context about the change you want to test]

Best Practices

• Start with a clear, falsifiable hypothesis
• Choose one primary metric to avoid multiple comparison issues
• Calculate sample size upfront based on expected effect size
• Run tests for full weekly cycles to account for day-of-week effects
• Set a minimum test duration (usually 1-2 weeks)
• Define success criteria before running the test
• Monitor guardrail metrics (revenue, errors, performance)

Example

Input: Testing new onboarding flow vs current 3-step process Output: Hypothesis (new 1-step flow will increase co...