Why does the skill insist on guardrail metrics?

Guardrail metrics are essential to ensure that optimizing one metric (like click-through rate) doesn't accidentally degrade a more important one (like total revenue or long-term retention).

Can this skill calculate statistical significance for live results?

The skill provides the logic and pre-calculated estimates for planning and interpreting results, though it recommends using specialized tools like Evan Miller or Statsig for final precision during the analysis phase.

What happens if my product traffic is too low for an A/B test?

If traffic is insufficient to reach statistical significance within 8 weeks, the skill identifies this risk and recommends qualitative alternatives like moderated user testing or 5-second tests.

How does it handle testing more than two variants?

When testing multiple variants, the skill provides guidance on the multiple comparisons problem and recommends statistical adjustments like the Bonferroni correction to maintain accuracy.

A/B Test Planner

Name: A/B Test Planner
Author: mohitagw15856

bymohitagw15856

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295

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分析と監視

Designs statistically rigorous A/B tests and experiment plans for product features, UI changes, and pricing strategies.

The A/B Test Planner skill transforms Claude into a specialized product experimentation consultant, helping you move beyond 'directional signals' toward trustworthy, data-driven decisions. It automates the complex setup of experiments by generating directional hypotheses, calculating required sample sizes and test durations, and establishing essential guardrail metrics to protect core business KPIs. Whether you are testing a new onboarding flow or a major UI overhaul, this skill provides a structured framework to ensure your results are statistically significant and actionable.

主な機能

01Primary and guardrail metric definition to prevent negative business impact

02Detailed results interpretation guides for ship, iterate, or reject scenarios

03Low-traffic detection with recommendations for qualitative research alternatives

04Statistical sample size and duration estimation based on MDE and baseline rates

05295 GitHub stars

06Directional hypothesis generation with predicted impact and rationale

ユースケース

01Calculating the required traffic and duration for a new feature rollout

02Designing a conversion rate optimization experiment for a checkout flow

03Setting up an experiment plan to test a new pricing model without hurting retention

主な機能

01Primary and guardrail metric definition to prevent negative business impact

02Detailed results interpretation guides for ship, iterate, or reject scenarios

03Low-traffic detection with recommendations for qualitative research alternatives

04Statistical sample size and duration estimation based on MDE and baseline rates

05295 GitHub stars

06Directional hypothesis generation with predicted impact and rationale

ユースケース

01Calculating the required traffic and duration for a new feature rollout

02Designing a conversion rate optimization experiment for a checkout flow

03Setting up an experiment plan to test a new pricing model without hurting retention