@pmprompt/ab-test-designer

Design robust A/B test experiments. Use when testing a new feature, validating a hypothesis, or optimizing conversion rates.

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SKILL.md
nameab-test-designer
descriptionDesign robust A/B test experiments. Use when testing a new feature, validating a hypothesis, or optimizing conversion rates.
argument-hint[feature/change to test]

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
  2. Test Design

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

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

    • Potential negative impacts
    • Guardrail metrics
    • When to stop the test early
  5. 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...

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