Organize A/B tests with hypotheses, variants, metrics, and conclusions.
npx skills add https://github.com/ttorres33/project-docs --skill ab-test-ttorres33
Please help me install this Agent Skill. Skill: ab-test Source: https://github.com/ttorres33/project-docs/tree/main/skills/ab-test Command: npx skills add https://github.com/ttorres33/project-docs --skill ab-test-ttorres33
Documents A/B tests with variants, hypothesis, measurement plan, and conclusions. Use when the user mentions running an A/B test, comparing two variants, or wanting to measure/test something systematically.
Use concise prompts to document an A/B test by specifying a test-name and details for each section.
High-intent search queries and answers about installing and using this skill.
Document A/B test hypotheses and measurement plans by recording test names, dates, variants, group assignments, metrics, and conclusions in a structured scaffold. This preserves learning and enables experiment governance for analytics teams.
The best way to structure A/B test documentation for marketing experiments is to use a standardized scaffold capturing test name, dates, hypothesis, variants, metrics, results, and conclusions to ensure consistent governance across teams.
Yes, you can use this to compare two variants systematically for product analysis. It provides a structured framework to record group assignments, measure outcomes, and document conclusions for product experiments.
When running an A/B test, track metrics defined in your measurement plan. Documenting these metrics alongside variants and hypotheses ensures outcomes are measured systematically and conclusions are preserved for future analysis.
No specific tools or dependencies are required to document A/B tests. You can record hypotheses, variants, group assignments, metrics, and conclusions using the structured scaffold without needing additional components or frameworks.
You should not use structured A/B test documentation when comparing more than two variants or when experiments lack defined hypotheses and measurement plans, as the scaffold is designed for two-variant comparisons with systematic outcome measurement.
ryzamediaDesign and evaluate statistically rigorous A/B and multivariate experiments.
product-on-purposeDesign end-to-end experiment plans with hypotheses, variants, metrics, and guardrails.
jokken79Plan statistically valid A/B tests with sample-size calculations and test-type selection.
sumilee-pcuCoordinate QA subagents for strategy, test case generation, and bug reporting.
