ab-test-setup
🎯SkillA Claude Code skill for planning and designing A/B tests with structured hypothesis frameworks, sample size calculations, metrics selection, and traffic allocation strategies.
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Overview
A/B Test Setup is a Claude Code skill for planning, designing, and implementing A/B tests and experiments. It provides a structured methodology covering hypothesis creation, sample size calculation, metrics selection, variant design, and traffic allocation. The skill reads from for project-specific context before making recommendations.
Key Features
- Hypothesis Framework: Provides a structured template for writing strong hypotheses that include observation/data, expected change, predicted outcome, target audience, and measurable success criteria
- Sample Size Reference Tables: Includes quick-reference tables for required sample sizes across different baseline conversion rates and expected lifts, plus links to industry calculators
- Metrics Selection System: Guides users through choosing primary metrics (decision criteria), secondary metrics (context), and guardrail metrics (safety checks) for each test
- Test Type Guidance: Covers A/B, A/B/n, multivariate, and split URL tests with traffic requirements and guidance on when to use each approach
Who is this for?
- Product managers and growth teams who need a structured framework for designing statistically valid experiments with clear success criteria
- Data analysts and CRO specialists looking for sample size calculations, metrics frameworks, and best practices for running reliable A/B tests
- Marketing and engineering teams implementing split tests who want guidance on hypothesis creation, variant design, and traffic allocation strategies
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