From sanity
A/B testing and content experimentation methodology for data-driven content optimization. Use when implementing experiments, analyzing results, or building experimentation infrastructure.
npx claudepluginhub jadecli/jadecli-claude-pluginsThis skill uses the workspace's default tool permissions.
Principles and patterns for running effective content experiments to improve conversion rates, engagement, and user experience.
Guides content A/B testing and experimentation: design hypotheses, metrics, sample size, stats, CMS variants, pitfalls. For planning, setup, analysis in CMS/frontend stacks.
Guides planning, designing, and implementing A/B tests, split tests, multivariate experiments. Covers hypotheses, sample sizes, test types, statistical principles.
Designs A/B tests and experiments with hypothesis frameworks, sample sizes, test types, metrics, and statistical principles for valid, actionable growth results.
Share bugs, ideas, or general feedback.
Principles and patterns for running effective content experiments to improve conversion rates, engagement, and user experience.
Reference these guidelines when:
Comparing two variants (A vs B) to determine which performs better.
Testing multiple variables simultaneously to find optimal combinations.
The confidence level that results aren't due to random chance.
Making decisions based on data rather than opinions (HiPPO avoidance).
See resources/ for detailed guidance: