A/B Testing
SkillCommunicationUse when turning a marketing, growth, CRO, pricing, onboarding, email, ad, or acquisition idea into a useful experiment or test plan.
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the A/B Testing skill
What this skill tells your AI
The instructions your AI receives, as published by infinite-labs-ai/infinite-skills in skills/ab-testing/SKILL.md and read by ahel’s review.
Turn a growth idea into a test that can actually change a decision.
Frame The Decision
Start with the decision the experiment should inform:
- Ship, kill, iterate, scale, or investigate.
- Audience or surface being tested.
- Current baseline.
- Primary metric and guardrail metric.
- Minimum effect that would matter.
- Sample size or traffic reality.
- Time window and implementation cost.
If the traffic is too low for an A/B test, recommend a qualitative, sequential, or directional test instead.
Write The Hypothesis
Use this shape:
Because [observed problem], changing [specific thing] for [audience] should improve [primary metric] without hurting [guardrail], shown by [measurement].
Make the variant isolate one main idea. Do not mix headline, price, layout, offer, and audience changes unless the test is explicitly a bundled concept test.
Choose The Test Type
Pick the method based on traffic, risk, and decision cost:
- A/B test: enough traffic and a reversible surface.
- Before/after read: operational change where randomization is impractical.
- Concierge test: validate demand or workflow manually before building.
- Smoke test: test interest before full fulfillment.
- Fake-door test: measure intent when the feature or offer is not ready, with ethical disclosure.
- Qualitative read: use interviews, session reviews, or sales calls when numbers will be too thin.
Add decision economics:
- Cost of shipping the wrong thing.
- Cost of waiting.
- Minimum useful evidence.
Design The Test
Define:
- Control and variant.
- Inclusion and exclusion rules.
- Primary metric.
- Guardrails.
- Instrumentation requirements.
- Decision threshold.
- Stop conditions.
- Rollback plan.
Interpret Carefully
- Do not call a winner before the decision threshold is met.
- Do not ignore novelty effects.
- Segment after the primary read, not until a desired story appears.
- Treat inconclusive results as useful when they eliminate bad ideas.
Output
Experiment brief:
Decision:
Hypothesis:
Audience:
Surface:
Evidence shape:
[A/B / before-after / concierge / smoke / fake-door / qualitative]
Decision economics:
- Cost of wrong ship:
- Cost of waiting:
- Minimum useful evidence:
Control or baseline:
Variant or intervention:
Metrics:
Primary:
Guardrails:
Instrumentation:
Readiness:
- Traffic:
- Baseline:
- Minimum useful lift:
- Runtime:
Decision rules:
- Ship if:
- Iterate if:
- Kill if:
Risks:
- [risk] -> [mitigation]
Signals
- GitHub stars
- 43
- Forks
- 4
- Last commit
- Aug 2026
Advanced
- Catalog kind
- skill
- Gateway key
ab-testing-infinite-labs-ai- Source
- github.com/infinite-labs-ai/infinite-skills