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 honest confidence number, then asks one focused question at a time with a guess attached. - Want vs. Should-Want Detection: Probes convention-signaling answers ("scalable", "best practice") to surface what the user actually wants. - Confirmed Statement of Intent: Produces a structured restate (Outcome, User, Why now, Success, Constraint, Out of scope) gated on an explicit yes. - Use Case: A user says "build me a dashboard for our metrics." Instead of proposing chart libraries, the skill interviews them 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 feels underspecified, and answer the agent's questions one at a time until it restates your intent for confirmation.