ab-test-setup

What This Skill Does

The ab-test-setup skill is designed to guide users through the complex lifecycle of conversion rate optimization (CRO) and experimentation. It acts as an expert consultant, ensuring that any A/B test, split test, or multivariate experiment is grounded in sound scientific methodology. The skill helps users transition from vague ideas to structured, statistically significant experiments by enforcing a robust hypothesis framework, defining appropriate primary and guardrail metrics, and calculating necessary sample sizes. It prevents common pitfalls like 'peeking' at results, under-powered testing, and changing too many variables simultaneously.

Installation

To install this skill in your OpenClaw environment, execute the following command in your terminal: clawhub install openclaw/skills/skills/alirezarezvani/cs-ab-test-setup

Use Cases

• Conversion Rate Optimization: Testing changes to landing pages, checkout flows, or sign-up forms to improve conversion metrics.
• Feature Validation: Validating whether a new UI element or copy change actually drives user behavior as expected before a full-scale deployment.
• Hypothesis Refinement: Turning vague goals like 'let's test the button color' into rigorous 'Because/We believe/We'll know' statements.
• Experimental Planning: Determining if you have enough traffic to run a test or if you should use an A/B vs. a Multivariate Test (MVT) strategy.

Example Prompts

1. "I want to test if changing the headline on our landing page increases demo requests. Can you help me write a strong hypothesis?"
2. "We have about 5,000 visitors a month and a current 2% conversion rate. Is a split test feasible for a 10% lift, or do we need more traffic?"
3. "Help me design an experiment for our pricing page. I want to test a new layout, but I'm worried about hurting our current retention rates. What guardrail metrics should I track?"

Tips & Limitations

• Context is Key: Always ensure .claude/product-marketing-context.md is populated. The skill performs best when it understands your specific product constraints and baseline data.
• One Variable Only: The skill strictly enforces the 'test one thing' rule. If you attempt to change multiple disparate elements, it will advise you to simplify your design to maintain statistical validity.
• Not for Analytics Implementation: This skill focuses on the design and strategy of experiments. For the technical implementation of tracking tags, refer to the analytics-tracking skill.
• Avoid Peeking: Remember that the skill's primary goal is integrity; it will advise against drawing conclusions before reaching the pre-calculated sample size, regardless of how promising early data looks.