About a-b-test-design
a-b-test-design is a skill for teams testing product or design changes with rigorous A/B experiments. It helps define hypotheses, variants, metrics, sample sizes, and duration, while noting common pitfalls and cases where testing is unsuitable.
Key features
- 01Form testable hypotheses
- 02Set control and single-variable treatment
- 03Choose primary and supporting metrics
- 04Estimate sample size from effect and statistical inputs
- 05Plan duration and flag common pitfalls
Use cases
- 01Test the impact of a single design change
- 02Set metrics and sample size for conversion experiments
- 03Plan tests that cover full weekly cycles
- 04Decide whether low-traffic cases suit A/B testing
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