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 - [ ] Sample size calculated - [ ] Test duration estimated - [ ] Segment isolated (no overlap with other running tests) - [ ] Rollback plan defined ## Hypothesis Template > "We believe that [change] will cause [primary metric] to [increase/decrease] by [X%] for [user segment], because [rationale based on data or insight]." Never run a test without a directional hypothesis. "Let's just see what happens" is not a hypothesis. ## Sample Size Calculator Logic Use this formula (provide the output, not the formula, to the user): - **Baseline conversion rate:** Current rate of primary metric - **MDE:** Smallest change worth detecting (recommend 10–20% relative lift for ...