Design and run statistically valid A/B tests with hypothesis frameworks and metric definitions.
npx skills add https://github.com/SlevoDev/s-tag --skill ab-test-setup-slevodev
Please help me install this Agent Skill. Skill: ab-test-setup Source: https://github.com/SlevoDev/s-tag/tree/main/.claude/skills/ab-test-setup Command: npx skills add https://github.com/SlevoDev/s-tag --skill ab-test-setup-slevodev
Plan, design, and execute statistically valid A/B tests and growth experiments to determine what drives measurable improvements.
Create a hypothesis-driven test plan with clearly defined metrics, sample size, duration, and variant mapping for a page or feature.
High-intent search queries and answers about installing and using this skill.
To set up an A/B test with statistical significance, define a clear hypothesis, establish primary, secondary, and guardrail metrics, and calculate the required sample size and test duration to ensure reliable decision making.
The best way to plan growth experiments is using a hypothesis-driven framework that maps variant designs to specific metric definitions, ensuring your primary, secondary, and guardrail metrics are established before execution begins.
Calculating sample size and duration for split testing involves estimating the required audience size based on your expected effect size and metric variance, ensuring the test runs long enough to achieve statistical significance.
Yes, this approach applies when planning, designing, or implementing A/B tests, split tests, A/B/n experiments, and multivariate tests to compare two or more approaches effectively.
Guardrail metrics are predefined measurements used to monitor and prevent unintended negative consequences during experimentation, ensuring that optimizing primary metrics does not harm overall system health or user experience.
Yes, this method provides reusable templates and playbooks for test plans, results tracking, backlog management, and ongoing experimentation to drive continuous growth learning.
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