ab-test-setup

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

Plan A/B tests with sample size, metrics, and duration guidance.

Updated Apr 11, 2026

Search Tags:#growth #hypothesis #experimentation #ab-testing #conversion-rate #statistical-significance

Ready to run

npx skills add https://github.com/krmorantte/marketing --skill ab-test-setup-krmorantte

💬 Prompt for ChatGPT / Claude Chat▼

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

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

A/B testing and growth experimentation can be complex and data-driven. This Skill provides a structured framework to design, run, and analyze experiments to optimize marketing outcomes with statistical rigor.

Core Features & Use Cases

Hypothesis-driven testing using A/B, A/B/n, MVT, and split URL variants.
Sample size planning, duration estimation, and power analysis.
Primary / secondary / guardrail metrics specification and segment analysis.
Documentation templates and playbooks to scale experimentation across teams.
Guidance on interpretation, decision rules, and rollout.

Quick Start

Define a test goal, select a primary metric, and calculate the required sample size for a statistically valid result.

Frequently Asked Questions about ab-test-setup

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

FAQPage Schema

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

To calculate sample size and duration for an A/B test, you need to define a test goal, select a primary metric, and perform a power analysis to ensure statistically valid results for your traffic volume.

What is the best way to structure a hypothesis for growth experimentation?▼

Structuring a hypothesis for growth experimentation requires defining the expected change, the target metric, and the rationale. A rigorous hypothesis structure specifies test variants, primary metrics, and significance thresholds before launch.

How do I set up guardrail metrics for conversion rate optimization?▼

Setting up guardrail metrics for conversion rate optimization involves specifying secondary metrics that monitor unexpected negative impacts. You define these alongside primary metrics to apply decision rules and ensure safe rollout.

Can I use this A/B testing framework for mid-traffic landing pages?▼

Yes, you can use this A/B testing framework for mid-traffic landing pages. It provides sample size planning and duration estimation specifically designed to achieve statistical significance across high-traffic and mid-traffic contexts.

What types of marketing experiments does this approach support?▼

This approach supports multiple marketing experiment types including A/B, A/B/n, MVT, and split URL variants. It applies to website pages, pricing, messaging, and feature tests to optimize conversion rates.