ab-test-plan
ab-test-plan is an Experimentation & statistics skill for AI agents, published by indranilbanerjee in the digital-marketing-pro repository on GitHub. Skillspace rates ab-test-plan D (68/100) and it passed the safety scan; the rating is based on static analysis only.
Design a statistically rigorous A/B or multivariate test plan — If/Then/Because hypothesis, control and variant specs, required sample size per variant (absolute vs relative MDE via sample-size-calculator.py), test duration, guardrail metrics, stopping rules, and go/no-go decision criteria. Triggers on \"/digital-marketing-pro:ab-test-plan\", \"set up an A/B test\", \"how long should my test run\", \"calculate sample size for an experiment\", \"is this test result significant\". Reads the brand profile and past campaign-tracker results to avoid re-testing validated hypotheses; finished tests are evaluated with significance-tester.py by the cro-specialist agent.
- Grade
- D
- Quality
- 68 / 100
- Safety
- Passed
- GitHub stars
- 815
Unproven: rated by static analysis only, not yet compared head-to-head.
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Our review
- Architecture
- 100 / 100
- Maturity
- Basic: a single SKILL.md
- Security scan
- No issues found
- License
- Not declared. Check the repository before reusing it.
Not checked yet
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- Not judged on the rubric yet (not a contender in its category)
- No blind task eval yet
- Scope (portable / vendor / repo-internal) not assessed; only the top of each category is read for scope
- Sibling files (scripts, references) not fetched; security scan covers SKILL.md only
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