Design and run A/B tests
ab-test-setupskillsetup L1★0
Sheshiyer/skill-clusters ↗What it does
Design and run A/B test experiments
Best for
Validating product hypotheses with statistical rigor and measuring impact on business metrics.
Inputs
- · live page URL
- · test hypothesis statement
Outputs
- · prioritized violation report
- · execution log or transcript
- · refactoring plan
Requires
- · AST-Grep
Preconditions
Baseline conversion metric known; test platform or tracking code installed
Failure modes
- · Sample size too small for significance
- · External traffic or traffic source added mid-test
- · Variant implementation differs from specification
Trust signals
- · Uses statistical significance and pre-commit sample size
- · Includes regression test safety gates
- · Leverages LSP and AST-based code analysis