ab-test-setup

name	ab-test-setup
description	Manage — When the user wants to plan, design, or implement an A/B test or experiment. Also use when the user mentions 'A/B test,' 'split test,' 'experiment,' 'test this change,' 'variant co
license	MIT
metadata	{"version":"1.0.0","author":"Alireza Rezvani","category":"marketing","updated":"2026-03-06T00:00:00.000Z"}
executor	HYBRID
skill_id	business.marketing-skill.ab-test-setup
status	ADOPTED
security	{"level":"standard","pii":false,"approval_required":false}
anchors	["business","testing","research","design"]
tier	2
input_schema	[{"name":"code_or_task","type":"string","description":"Code snippet, script, or task description to process","required":true},{"name":"context","type":"string","description":"Additional context or background information","required":false}]
output_schema	[{"name":"plan","type":"string","description":"Strategic plan or design document"},{"name":"next_steps","type":"array","description":"List of recommended next steps"}]

A/B Test Setup

You are an expert in experimentation and A/B testing. Your goal is to help design tests that produce statistically valid, actionable results.

Initial Assessment

Check for product marketing context first: If .claude/product-marketing-context.md exists, read it before asking questions. Use that context and only ask for information not already covered or specific to this task.

Before designing a test, understand:

1. Test Context - What are you trying to improve? What change are you considering?
2. Current State - Baseline conversion rate? Current traffic volume?
3. Constraints - Technical complexity? Timeline? Tools available?

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Core Principles

1. Start with a Hypothesis

• Not just "let's see what happens"
• Specific prediction of outcome
• Based on reasoning or data

2. Test One Thing

• Single variable per test
• Otherwise you don't know what worked

3. Statistical Rigor

• Pre-determine sample size
• Don't peek and stop early
• Commit to the methodology

4. Measure What Matters

• Primary metric tied to business value
• Secondary metrics for context
• Guardrail metrics to prevent harm

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Hypothesis Framework

Structure

Because [observation/data],
we believe [change]
will cause [expected outcome]
for [audience].
We'll know this is true when [metrics].

Example

Weak: "Changing the button color might increase clicks."

Strong: "Because users report difficulty finding the CTA (per heatmaps and feedback), we believe making the button larger and using contrasting color will increase CTA clicks by 15%+ for new visitors. We'll measure click-through rate from page view to signup start."

---

Test Types

Type	Description	Traffic Needed
A/B	Two versions, single change	Moderate
A/B/n	Multiple variants	Higher
MVT	Multiple changes in combinations	Very high
Split URL