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. 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 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-Want Detection: Probes past 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 condition. - 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 tracking list — a completely different artifact. ## Quick Start Ask the agent to interview you about an underspecified request, for example: "Interview me before we start — I think I want a reporting dashboard for the team."
