Agent skill · marketing growth · owl-listener
a-b-test-design
Design an A/B experiment — hypothesis, variants, primary metric, and sample size. Use when a change can be measured quantitatively at scale. For observing behaviour qualitatively, use `test-scenario`.
Why this skill is useful
Provides structured guidelines for designing A/B tests, including metrics and sample size calculations that the AI wouldn't reliably generate on its own.
What it needs
About 1k tokens when loaded. Last updated 2026-06-14. 1,982 stars on the source repository.
What this skill does
A/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
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills owl-listener/a-b-test-design