optimization.experiment_brief

A/B test design requires defining your hypothesis, primary metric, audience, and variants before launch. This Skill generates a structured experiment brief that outlines your test design, sample size estimates, run duration, and instrumentation so results are measurable and actionable.

An experiment brief for product optimization includes your hypothesis, audience definition, primary and secondary metrics, variant details, sample size calculations, timeline, and an operational checklist. This Skill produces all these components from your core inputs to ensure experiments are well-scoped and aligned with user experience.

Poorly scoped experiments lack clear hypotheses, undefined metrics, or misaligned audiences. This Skill solves this by consuming your hypothesis, metrics, and audience to generate a complete experiment brief with rationale, test design, sample size estimates, and timelines before you launch.

Yes. Experiment briefs apply across product design, product management, analytics, and engineering contexts. This Skill helps teams in any of these disciplines plan experiments, design variants, estimate sample sizes, and set timelines using consistent methodology.

Your experiment brief should define a primary metric directly tied to your hypothesis and optional secondary metrics to capture broader impact. This Skill consumes both primary and secondary metrics to produce a comprehensive overview and rationale that ensures your measurement strategy is complete and aligned with your test goals.

Run duration depends on your sample size, audience size, and metric variability. This Skill estimates sample size requirements and calculates appropriate run duration as part of the experiment brief, so you know how long to collect data before making a decision.