AbTestSetup

Design statistically valid A/B tests with hypothesis, sample size, and metrics planning.

2|Updated Mar 5, 2026
One-click install
npx skills add https://github.com/phuaky/pai-skills --skill abtestsetup
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: AbTestSetup
Source: https://github.com/phuaky/pai-skills/tree/main/skills/AbTestSetup
Command: npx skills add https://github.com/phuaky/pai-skills --skill abtestsetup

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Design and analyze experiments to produce reliable, actionable results from A/B tests and other controlled experiments.

Core Features & Use Cases

  • Hypothesis-driven test design: structure experiments with clear, testable predictions.
  • Comprehensive test types: supports A/B, A/B/n, Multivariate Test (MVT), and split URL testing for various scenarios.
  • Metrics planning and sample size: guidance on primary/secondary metrics and statistical power calculations to determine needed sample sizes.
  • Run and interpret results: pre-launch checklists, significance assessment, and actionable interpretation for decision-making.

Quick Start

Define your hypothesis and choose a test type to start planning your A/B test.

Frequently Asked Questions about AbTestSetup

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

FAQPage Schema
How do I calculate sample size for an A/B test?

Design A/B tests by defining a clear hypothesis and selecting a test type to structure your experiment. This skill enforces methodical framing with testable predictions, supporting A/B, A/B/n, Multivariate Test (MVT), and split URL testing for various optimization scenarios.

When should I use a multivariate test instead of an A/B test?

Assess A/B test results using significance assessment and pre-launch checklists to ensure statistical validity. This skill provides actionable interpretation for decision-making, helping you determine if your landing page changes or feature rollouts produced reliable outcomes.

What metrics do I need to plan for statistical significance in experiments?

Use A/B testing for product optimization contexts like landing page changes, feature rollouts, and messaging experiments. This skill enforces methodical framing and adherence to best practices to ensure rigorous results in these scenarios.

How do I structure a hypothesis for a controlled experiment?

Structure a controlled experiment hypothesis by creating clear, testable predictions about your product changes. This skill enforces hypothesis-driven test design to ensure your experiments are methodically framed for reliable, actionable results.