Individual skill

a-b-test-design

0upvotes

Design rigorous A/B tests with hypotheses, variants, metrics, and sample size calculations.

product-designers/a-b-test-design/SKILL.md

Read pinned source

Source commit 02cfefb3a213041de8b80bc659ebc5f17b5e746a

What was checked

Matches the observed repository HEAD: Yes
Scanner rules are current: Yes
Repository scan complete: No
Skill scan complete: No
Observed behavior: Instructions only
Last checked: Nov 11, 2026

These are observations at the recorded commit, not a guarantee of safety. Review the source before running it.

Install this skill

Choose an agent in your Stack or copy a command below. Commands fetch the pinned commit and copy only this skill directory. Existing destinations are refused. Copying a command does not install anything.

An install command is unavailable: current rules, complete repository and skill scans, an eligible behavior level and a permitted license are required.

Repository context

Repository stars (not skill ratings): 163
Repository last updated: Jun 28, 2026
License: MIT

Observed agent integration: No specific integration evidence. Standard SKILL.md installation does not imply verified compatibility.

ai-citation Repository topic
ai-visibility Repository topic
claude Repository topic
claude-skills Repository topic
geo Repository topic
skills Repository topic
dev-gtm Repository topic
design Derived category
testing Derived category

Files and excerpt

File metadata and permitted, redacted excerpts come from the recorded scan. Source links are pinned to that commit.

Browse files

SKILL.mdcore · 1,781 bytes

Read excerpt

# 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.