ab-test-planner
A/B Test Planner Skill
Design experiments that produce trustworthy results — not just directional signals. Every test output includes hypothesis, success metrics, sample size, duration, and a results interpretation guide.
Required Inputs
Ask the user for these if not provided:
- What is being tested (feature, UI change, copy, pricing, onboarding step)
- Hypothesis (or ask to help formulate one)
- Primary metric (conversion rate, click-through, completion rate, etc.)
- Baseline rate and minimum detectable effect (MDE)
- Daily eligible users (to calculate duration)
Experiment Design Checklist
Before running any test, confirm:
- Clear hypothesis with predicted direction
- Single primary metric (plus up to 2 guardrail metrics)
- Minimum detectable effect (MDE) defined
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