Plan and evaluate product A/B tests with clear hypotheses, success metrics, sample size estimates, and stopping rules. Rank proposed experiments and interpret results using effect sizes and uncertainty to support practical product decisions.
Use when planning product experiments, writing testable hypotheses, estimating sample size, prioritizing tests, or interpreting A/B outcomes with practical statistical rigor.
Review the instructions the agent will receive before you install.
Design, prioritize, and evaluate product experiments with clear hypotheses and defensible decisions.
Use this skill for:
[intervention][metric] will change by [expected direction/magnitude][behavioral mechanism]Use:
python3 scripts/sample_size_calculator.py --baseline-rate 0.12 --mde 0.02 --mde-type absolute
ICE Score = (Impact * Confidence * Ease) / 10
See:
references/experiment-playbook.mdreferences/statistics-reference.mdscripts/sample_size_calculator.pyComputes required sample size (per variant and total) from:
Example:
python3 scripts/sample_size_calculator.py \
--baseline-rate 0.10 \
--mde 0.015 \
--mde-type absolute \
--alpha 0.05 \
--power 0.8
No reviews yet. Tell others what you thought.
No login needed. Share how it worked for you, ask a question, or note where it did not run.
No comments yet. Be the first to write one.
Audit web applications for accessibility issues against WCAG 2.2 Level A and AA, the web accessibility standards. Get framework-specific code fixes, contrast checks, verification steps, and reports, with options for automated checks in development pipelines.
Plan A/B tests that compare page variations, from headlines to pricing layouts. Define a hypothesis, choose success metrics, calculate sample size and duration, and prepare launch checks and results reports to support decisions.
Create paid ad headlines and body copy tailored to each platform's character limits. Use existing performance data to develop variations, explore new messaging angles, and check copy for length, quality, and potential rejection risks.
Review code changes from three critical perspectives: production failures, maintainability, and security. Get a consolidated report that ranks findings by severity and provides merge guidance, helping examine assumptions that an ordinary self-review may overlook.
Audit and improve content intended for citation in AI answers. Review evidence, author information, and structure, generate revised content and structured metadata, and keep a local record of observed citations across AI services.
Create signed records for consequential agent actions such as deployments, deletions, and payments. Decide which actions need evidence, record the agent, action, target, and policy, then verify the resulting certificate offline to detect later changes.