ab-test-planner
Design statistically rigorous A/B tests for product features, UI changes, onboarding flows, and pricing experiments. Use when asked to set up an experiment, design an A/B test, calculate sample size, or interpret test results. Produces a complete test plan with hypothesis, variant definitions, sample size, duration estimate, guardrail metrics, and a results interpretation guide.
npx skills add mohitagw15856/pm-claude-skills --skill ab-test-planner --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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'
- Required Inputs
- Experiment Design Checklist
- Hypothesis Template
- Sample Size Calculator Logic
- Test Duration Guidance
- Output Format
- A/B Test Plan — [Test Name] — [Date]
- Guidelines
- Anti-Patterns
- Deeper Materials
- Scoring Rubric (0–40)
- Quality Checks
What does the ab-test-planner skill do?
Design statistically rigorous A/B tests for product features, UI changes, onboarding flows, and pricing experiments. Use when asked to set up an experiment, design an A/B test, calculate sample size, or interpret test results. Produces a complete test plan with hypothesis, variant definitions, sample size, duration estimate, guardrail metrics, and a results interpretation guide.
How do I install it?
Run `npx skills add mohitagw15856/pm-claude-skills --skill ab-test-planner --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From mohitagw15856/pm-claude-skills, a repository with 1,255 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.