ab-test-plan

Generate A/B test plans for DTC funnels using RMBC principles.

Updated May 19, 2026
One-click install
npx skills add https://github.com/mohammedburqan/es --skill ab-test-plan-mohammedburqan
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: ab-test-plan
Source: https://github.com/mohammedburqan/es/tree/main/skills/ab-test-plan
Command: npx skills add https://github.com/mohammedburqan/es --skill ab-test-plan-mohammedburqan

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill streamlines the process of creating A/B test plans for Direct-to-Consumer funnels, ensuring they are structured, based on RMBC principles, and include a falsifiable hypothesis, control vs variant definition, and success criteria.

Core Features & Use Cases

  • Structured Test Plan Creation: Provides a template for writing A/B test plans, including hypothesis, control, variant, primary metric, sample size, and success criteria.
  • RMBC Grounded Reasoning: Ensures that every element of the test plan connects back to Research, Mechanism, Brief, or Copy phases of the RMBC methodology.
  • Use Case: For a DTC e-commerce funnel, use this Skill to create a comprehensive A/B test plan for the checkout page, outlining the hypothesis, expected outcomes, and primary success criteria.

Quick Start

Generate an A/B test plan for the checkout page using the 'checkout' page type, with a baseline conversion rate of 10%, a hypothesis about reducing checkout friction, and a traffic volume of 1000 daily visitors.

Frequently Asked Questions about ab-test-plan

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I create an A/B test plan for a DTC e-commerce funnel?

An A/B test plan for a DTC funnel validates changes by structuring a falsifiable hypothesis, defining control versus variant, calculating sample size, and setting primary metrics with success criteria based on RMBC principles.

What is the RMBC methodology for conversion optimization?

The RMBC methodology for conversion optimization structures testing around Research, Mechanism, Brief, and Copy phases. It ensures every A/B test plan element connects back to these core principles to validate Direct-to-Consumer funnel changes.

How do I calculate sample size for an A/B test on a checkout page?

Calculating sample size for an A/B test requires your baseline conversion rate, expected effect size, and daily traffic volume. The plan incorporates these metrics to determine statistical significance for your checkout page tests.

Do I need prior A/B testing knowledge to use RMBC principles for DTC funnels?

Yes, you need a foundational understanding of A/B testing and RMBC methodology. The process requires you to input valid baseline conversion rates, page types, and hypotheses to generate accurate test plans for Direct-to-Consumer funnels.

What's the best way to structure a falsifiable hypothesis for conversion optimization?

The best way to structure a falsifiable hypothesis is to link it directly to RMBC phases. The generated plan defines the expected outcome, primary metric, and success criteria to ensure the hypothesis is testable and measurable.