What problem does it solve? Users often ask for what they think they should want rather than what they actually need, and agents silently fill in ambiguous requirements with assumptions. This Skill closes that gap before any plan, spec, or code exists, when switching costs are still zero. ## Core Features & Use Cases - Hypothesis-driven interviewing: States a one-sentence hypothesis with an honest confidence number, then asks one focused question at a time with a guess attached so the user can react instead of generating answers from scratch. - Want-vs-should detection: Probes convention-signaling answers ("scalable", "best practice") with questions like "what would you actually want if you didn't have to justify it?" - Confirmed intent output: Produces a structured restatement (Outcome / User / Why now / Success / Constraint / Out of scope) gated on an explicit yes, with a 95% confidence stop test. - Use Case: A user says "build me a dashboard for our metrics." Instead of proposing chart libraries, the Skill interviews and discovers the real ask is a personal experiment tracker list — a completely different artifact. ## Quick Start Ask the agent to interview you about your next underspecified request, for example by saying "interview me before we start building the reporting feature."
