What common experimentation mistakes does this skill help avoid?

The skill provides specific warnings against 'peeking' at results early, testing too many variables at once, and ignoring guardrail metrics that protect the user experience.

How does this skill help with sample size calculations?

It provides quick reference tables and methodology for determining the traffic needed to detect specific conversion lifts, ensuring your results are statistically significant.

Can it help me write a better test hypothesis?

Yes, it includes a structured 'Because-Believe-Will Cause' framework to turn vague ideas into specific, measurable predictions based on existing observations.

What is the A/B Test Setup skill for Claude Code?

It is a specialized capability that helps developers and marketers plan, design, and implement statistically sound A/B and multivariate tests within their workflow.

Does this skill provide implementation advice?

It offers guidance on both client-side and server-side implementations, including checklists for tracking verification and quality assurance across all variants.

A/B Test Setup & Experimentation

Name: A/B Test Setup & Experimentation
Author: simplysmartai

bysimplysmartai

0•

マーケティングオートメーション

Designs and implements statistically valid A/B tests and multivariate experiments to optimize conversion rates and user experience.

This skill empowers Claude to act as an experimentation expert, guiding users through the end-to-end process of setting up A/B tests. It provides frameworks for crafting strong hypotheses, calculating required sample sizes, selecting meaningful primary and guardrail metrics, and choosing between client-side or server-side implementations. Whether you are testing marketing copy or complex UI changes, this skill ensures your experiments are statistically rigorous, avoid common pitfalls like 'peeking,' and produce actionable business insights based on reliable data.

主な機能

01Structured framework for generating data-driven hypotheses

02Strategic selection of primary, secondary, and guardrail metrics

03Statistical significance analysis and result interpretation checklists

04Guidance on traffic allocation and variant design best practices

05Sample size and duration calculation guidelines based on baseline conversion

060 GitHub stars

ユースケース

01Calculating the necessary sample size for a new pricing model experiment

02Designing a split test for a landing page to increase signup conversions

03Analyzing experiment results to determine if a variant is a statistically significant winner

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add simplysmartai/5cypressautomation ab-test-setup

For use in Claude.ai and ChatGPT

Download Skill