What problem does it solve? Customer conversations often produce misleading data because founders pitch their ideas, ask hypothetical questions, and accept compliments as validation. This Skill applies Rob Fitzpatrick's Mom Test framework to structure interviews around past behavior, concrete facts, and real commitments instead of opinions. ## Core Features & Use Cases - Question Evaluation: Distinguishes good questions (anchored in past behavior) from bad ones (hypotheticals, leading questions, fishing for compliments) with extensive examples. - Bad Data Detection: Identifies and deflects the three types of bad data — compliments, fluff, and unsolicited feature requests — with real-time scoring and recovery phrases. - Commitment Testing: Pushes conversations toward concrete advances using time, reputation, and money commitment currencies to separate real interest from politeness. - Use Case: Before interviewing users about a new SaaS idea, use this Skill to rewrite your question script so every question asks about specific past behavior, then score each completed conversation 0-10 and extract commitment signals. ## Quick Start Review my customer interview questions and rewrite any that violate the Mom Test rules.
