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A/B Test Planner

🟢 Production-Ready🕐 updated 2026-07-14 ✅ 4.8/5 🔷 SkillSpec L3 pm-delivery

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.

📚 Based on Controlled experiments — Kohavi, Tang & Xu, Trustworthy Online Controlled Experiments

▶ Run it free — no key needed 📝 Grade your existing draft View SKILL.md ↗

What to give it

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

✅ The bar it holds itself to

Every skill in this library self-verifies — these are this skill's own quality checks, straight from its definition.

Hypothesis is directional (predicts a specific direction and magnitude, not "let's see")
Primary metric is singular (guardrail metrics are secondary)
Sample size is calculated from actual MDE and baseline (not guessed)
Test duration accounts for weekly seasonality (minimum 2 weeks)
Guardrail metrics are defined (at least one to protect revenue or core engagement)
Rollback trigger is specified with a concrete threshold

⚠️ What it refuses to do

Do not run a test without a directional hypothesis — "let's see what happens" produces uninterpretable results
Do not declare a winner before reaching the pre-planned sample size — peeking at results inflates false positive rates
Do not test multiple independent changes in a single variant — you won't know which change caused the result
Do not use engagement metrics (clicks, time-on-page) as the primary metric when the goal is revenue or retention — proxy metrics mislead
Do not ignore guardrail metrics — a conversion lift that causes a support ticket spike is not a win

Install

npx pm-claude-skills add --agent claude   # or codex · cursor · gemini · hermes
# or one-line MCP (every skill, any client):
claude mcp add pm-skills -- npx -y pm-claude-skills-mcp

Example output

Input: Test whether moving the signup CTA above the fold on the pricing page increases free-trial starts. Current trial-start rate 8%, ~20k weekly visitors. We want to detect a 10% relative lift. · generated by claude-sonnet-4-6

Start with

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<script src="https://mohitagw15856.github.io/pm-claude-skills/embed.js" async></script>

💬 Discussion

A/B Test Planner is one of 1078 open-source professional AI agent skills — all SkillSpec L3. Try them all in the browser · ⭐ Star on GitHub · Browse the full catalog