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, locking in the wrong solution before any plan, spec, or code exists. ## 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 of what the user would want if they 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 skill interviews and discovers the real need is a personal experiment tracker list, avoiding building the wrong artifact. ## Quick Start Say "interview me before we start" or "stress-test my thinking" when your request is underspecified, and answer the agent's questions one at a time until it restates your intent for confirmation.
