What problem does it solve? Ambiguous product questions like "why is growth slow" or "why is churn rising" are hard to act on without structure. This Skill decomposes vague concerns into a MECE hypothesis tree so teams can prioritize and test explanations systematically instead of guessing. ## Core Features & Use Cases - Structured Decomposition: Converts vague concerns into specific, measurable questions and breaks them into mutually exclusive, collectively exhaustive hypothesis branches. - Prioritization Framework: Scores hypotheses by available evidence, test effort, and impact to decide what to test first. - Output Templates: Provides ready-to-use hypothesis tree diagrams, testing plans, and evidence summary tables. - Use Case: Investigating why signup conversion dropped below 30% by branching into awareness, ability, motivation, and technical hypotheses, then ranking which to validate first. ## Quick Start Ask the AI to build a hypothesis tree for why your feature adoption is below target and produce a prioritized testing plan.