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ab-test-plan

Create A/B test plans with RMBC-aligned hypotheses, metrics, and sample sizes.

13|5|Updated Mar 20, 2026
Search Tags:#data-driven#ab-test#conversion-rate#experiment-design#a/b-testing#funnel-optimization#rmbc
One-click install
npx skills add https://github.com/coleschaffer/dtc-copywriting-skills --skill ab-test-plan-coleschaffer
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ab-test-plan
Source: https://github.com/coleschaffer/dtc-copywriting-skills/tree/main/skills/ab-test-plan
Command: npx skills add https://github.com/coleschaffer/dtc-copywriting-skills --skill ab-test-plan-coleschaffer

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Design rigorous, RMBC-aligned A/B test plans that translate hypotheses into measurable, data-driven experiments, reducing guesswork and optimizing funnel performance.

Core Features & Use Cases

  • Falsifiable RMBC-aligned hypothesis: Crafts a test statement rooted in RMBC phases to ensure test relevance and accountability.
  • One-variable control vs variant: Defines exactly what changes, preventing confounded results.
  • Sample size, duration, and metrics: Computes required sample size, estimated duration, and selects a primary metric with secondary metrics and clear success criteria.
  • End-to-end test plan: Produces a complete plan for pages like landing pages, order forms, emails, and ads, with risk and success criteria clearly outlined.

Quick Start

Provide inputs such as page_type, current_metric, hypothesis, traffic_volume, and test_element to generate a complete A/B test plan.

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 a data-driven A/B test plan for my DTC funnel?▼

Generate an A/B test plan by providing page type, current metric, hypothesis, traffic volume, and test element to output a complete RMBC-aligned experiment design with sample size and success criteria.

What is the RMBC framework for A/B testing?▼

The RMBC framework structures A/B testing by aligning falsifiable hypotheses with specific funnel stages, ensuring experiment relevance and accountability through rigorous, data-driven deployment.

How do I calculate sample size and test duration for an A/B test?▼

Calculate sample size and test duration by providing your current metric and traffic volume, which the planner uses to compute the statistical requirements for a valid conversion rate experiment.

Can I run A/B tests on checkout flows and landing pages?▼

Yes, you can test checkout flows, landing pages, emails, and ads by specifying the page type, allowing the planner to tailor the control vs variant design to that specific funnel stage.

How do I write a falsifiable hypothesis for conversion rate optimization?▼

Write a falsifiable hypothesis by defining a single-variable change between control and variant, ensuring your conversion rate optimization test prevents confounded results and measures one specific element.

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