What problem does it solve? Users often ask for what they think they should want rather than what they actually need, leading agents to build the wrong artifact from underspecified requests. This Skill closes the gap between the stated ask and the real intent 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 explicit confidence number, then asks one focused question at a time with a guess attached so users can react instead of generating answers from scratch. - Want-vs-Should Detection: Probes sophistication-signaling answers ("scalable", "clean", "best practice") with the question "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 checkable stop condition based on predicting the user's next three reactions. - Use Case: A user says "build me a dashboard for our metrics." Instead of proposing chart libraries, the agent interviews and discovers the real need is a personal experiment tracker list, avoiding building the wrong artifact entirely. ## Quick Start Ask the agent to interview you about your request before starting any planning or coding, for example by saying "interview me before we start building this feature."
