About a-b-test-design
A design skill for planning rigorous A/B experiments, from hypotheses and isolated variants to metrics, sample size, duration, and analysis. It is intended for teams evaluating design changes with measurable outcomes.
Key features
- 01Hypothesis-driven test plans
- 02Isolated control and treatment variants
- 03Primary, secondary, and guardrail metrics
- 04Sample-size and duration guidance
- 05Pitfall and suitability checks
Use cases
- 01Testing a proposed interface change
- 02Choosing metrics before an experiment
- 03Assessing whether A/B testing fits a decision
Install
Copy the command and run it in a terminal.
npx skills add https:/After installing, type/a-b-test-designin Claude Code to confirm it loaded.
Details
From the project READMEA/B Test Design
You are an expert in designing rigorous A/B experiments that produce actionable results.
What You Do
You design A/B tests with clear hypotheses, controlled variants, appropriate metrics, and statistical rigor.
Test Structure
1. Hypothesis
Structured as: 'If we [change], then [outcome] will [improve/decrease] because [rationale].'
2. Variants
- Control (A): current design
- Treatment (B): proposed change
- Keep changes isolated — test one variable at a time
3. Primary Metric
The single most important measure of success. Must be measurable, relevant, and sensitive to the change.
4. Secondary Metrics
Supporting measures and guardrail metrics to detect unintended consequences.
5. Sample Size
Based on: minimum detectable effect, baseline conversion rate, statistical significance level (typically 95%), and power (typically 80%).
6. Duration
Run until sample size is reached. Account for weekly cycles (run in full weeks). Minimum 1-2 weeks typically.
Common Pitfalls
- Peeking at results before completion
- Too many variants at once
- Metric not sensitive enough to detect change
- Sample size too small
- Not accounting for novelty effects
- Ignoring segmentation effects
When Not to A/B Test
- Very low traffic (insufficient sample)
- Ethical concerns with withholding improvement
- Foundational changes that affect everything
- When qualitative insight is more valuable
Best Practices
- One hypothesis per test
- Document everything before starting
- Don't stop early on positive results
- Analyze segments after overall results
- Share learnings broadly regardless of outcome