ab-test-setup

Name: ab-test-setup
Availability: InStock
Author: henriquedecamposjunior

Plan statistically valid A/B tests with sample-size estimation and guardrail metrics.

Updated Mar 16, 2026

Search Tags:#metrics #conversion #hypothesis #experimentation #sample-size #ab-test #a/b-testing

Ready to run

npx skills add https://github.com/HenriquedeCamposJunior/marketingskills2 --skill ab-test-setup-henriquedecamposjunior

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: ab-test-setup
Source: https://github.com/HenriquedeCamposJunior/marketingskills2/tree/main/skills/ab-test-setup
Command: npx skills add https://github.com/HenriquedeCamposJunior/marketingskills2 --skill ab-test-setup-henriquedecamposjunior

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Plan and run statistically valid A/B tests to help marketing teams learn what actually drives conversion and engagement.

Core Features & Use Cases

Hypothesis-driven test design: define clear observations, changes, and expected outcomes.
End-to-end test planning: choose test types (A/B, A/B/n, MVT), estimate sample size, set duration, and select primary and secondary metrics.
Template-backed execution: leverage test templates and references to ensure rigor and repeatability.

Quick Start

Draft a complete A/B test plan for a homepage headline that defines the hypothesis, primary metric, sample size, duration, and decision criteria.

Frequently Asked Questions about ab-test-setup

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

FAQPage Schema

How do I plan an A/B test with the right sample size and duration?▼

A valid A/B test plan requires defining a hypothesis, selecting primary metrics, estimating sample size, and setting a pre-determined duration to ensure statistical significance and prevent premature conclusions.

What is the difference between A/B, A/B/n, and MVT test types?▼

A/B tests compare two variants, A/B/n tests compare multiple variants against a control, and MVT (Multivariate Testing) evaluates multiple variable combinations simultaneously to identify the best overall interaction effects.

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

Writing a test hypothesis involves defining clear observations, stating the specific changes you plan to test, and predicting the expected outcomes. This structured format ensures your conversion experiments are measurable and grounded in data.

Can I use this A/B testing approach for marketing pages and funnels?▼

Yes, this A/B testing approach is specifically designed for marketing pages and funnels. It guides metric selection, variant design, and analysis to help marketing teams learn what actually drives conversion and engagement.

Why do I need guardrail metrics when running experiments?▼

Guardrail metrics are necessary to protect your business from unintended negative consequences during experiments. They act as safeguards alongside your primary metrics, ensuring changes do not harm other critical areas of your funnel.