ExpLint

It evaluates A/B test ideas and experiment backlogs, flagging vague hypotheses, missing or unclear success metrics, weak evidence, absent guardrails, rollout risks, and other testing anti-patterns, then suggests fixes before experiments ship. Designed for growth teams, product managers, researchers, and experiment owners using AI assistants, it helps make tests more measurable and reliable, reduce wasted development and analysis, and improve decisions through real-time linting and cross-assistant workflow support.

ניתן למצוא את ExpLint ב Feature Flags & Experimentation קטגוריות.