When the user wants to plan, design, or implement an A/B test or experiment, or build a growth experimentation program. Also use when the user mentions "A/B test," "split test," "experiment," "test this change," "variant copy," "multivariate test," "hypothesis," "should I test this,"...
SKILL.md
A/B Test Setup
When to Use
Use this skill when you need when the user wants to plan, design, or implement an A/B test or experiment, or build a growth experimentation program. Also use when the user mentions "A/B test," "split test," "experiment," "test this change," "variant copy," "multivariate test," "hypothesis," "should I test this,"...
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 .agents/product-marketing.md exists (or .claude/product-marketing.md, or the legacy product-marketing-context.md filename, in older setups), 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:
Test Context - What are you trying to improve? What change are you considering?
Current State - Baseline conversion rate? Current traffic volume?
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."
Looking at results before reaching sample size and stopping early leads to false positives and wrong decisions. Pre-commit to sample size and trust the process.
Individual tests are valuable. A continuous experimentation program is a compounding asset. This section covers how to run experiments as an ongoing growth engine, not just one-off tests.
The Experiment Loop
1. Generate hypotheses (from data, research, competitors, customer feedback)
2. Prioritize with ICE scoring
3. Design and run the test
4. Analyze results with statistical rigor
5. Promote winners to a playbook
6. Generate new hypotheses from learnings
→ Repeat
Hypothesis Generation
Feed your experiment backlog from multiple sources:
Features, messaging, or UX patterns they use that you don't
Support tickets
Recurring questions or complaints about conversion flows
Heatmaps/recordings
Where users hesitate, rage-click, or abandon
Past experiments
"Significant loser" tests often reveal new angles to try
ICE Prioritization
Score each hypothesis 1-10 on three dimensions:
Dimension
Question
Impact
If this works, how much will it move the primary metric?
Confidence
How sure are we this will work? (Based on data, not gut.)
Ease
How fast and cheap can we ship and measure this?
ICE Score = (Impact + Confidence + Ease) / 3
Run highest-scoring experiments first. Re-score monthly as context changes.
Experiment Velocity
Track your experimentation rate as a leading indicator of growth:
Metric
Target
Experiments launched per month
4-8 for most teams
Win rate
20-30% is common for mature programs (sustained higher rates may indicate conservative hypotheses)
Average test duration
2-4 weeks
Backlog depth
20+ hypotheses queued
Cumulative lift
Compound gains from all winners
The Experiment Playbook
When a test wins, don't just implement it — document the pattern:
## [Experiment Name]
**Date**: [date]
**Hypothesis**: [the hypothesis]
**Sample size**: [n per variant]
**Result**: [winner/loser/inconclusive] — [primary metric] changed by [X%] (95% CI: [range], p=[value])
**Guardrails**: [any guardrail metrics and their outcomes]
**Segment deltas**: [notable differences by device, segment, or cohort]
**Why it worked/failed**: [analysis]
**Pattern**: [the reusable insight — e.g., "social proof near pricing CTAs increases plan selection"]
**Apply to**: [other pages/flows where this pattern might work]
**Status**: [implemented / parked / needs follow-up test]
Over time, your playbook becomes a library of proven growth patterns specific to your product and audience.
Experiment Cadence
Weekly (30 min): Review running experiments for technical issues and guardrail metrics. Don't call winners early — but do stop tests where guardrails are significantly negative.
Bi-weekly: Conclude completed experiments. Analyze results, update playbook, launch next experiment from backlog.
Quarterly: Audit the playbook. Which patterns have been applied broadly? Which winning patterns haven't been scaled yet? What areas of the funnel are under-tested?
Common Mistakes
Test Design
Testing too small a change (undetectable)
Testing too many things (can't isolate)
No clear hypothesis
Execution
Stopping early
Changing things mid-test
Not checking implementation
Analysis
Ignoring confidence intervals
Cherry-picking segments
Over-interpreting inconclusive results
Task-Specific Questions
What's your current conversion rate?
How much traffic does this page get?
What change are you considering and why?
What's the smallest improvement worth detecting?
What tools do you have for testing?
Have you tested this area before?
Related Skills
cro: For generating test ideas based on CRO principles
analytics: For setting up test measurement
copywriting: For creating variant copy
Limitations
Use this skill only when the task clearly matches its upstream source and local project context.
Verify commands, generated code, dependencies, credentials, and external service behavior before applying changes.
Do not treat examples as a substitute for environment-specific tests, security review, or user approval for destructive or costly actions.
