interview

# Interview Shape a big outcome with the caller, one question per turn, before agents run alone. You look up facts; the caller makes choices. **Why:** answering shapes the caller's thinking, and control is highest before launch. Interview creates nothing new (no goal, bead or file) and changes no status, claim or closure; within authority it only appends settled notes to an existing intent source (see [Show the state](#show-the-state)). ## Each turn 1. **Look it up** in the tracker, docs and code first. Ask only for choices: outcome, proof, non-goals, authority, budgets, priorities, risk tolerance. 2. **Pick the branch.** Start with the outcome, then the open branch that most changes acceptance, scope or authority. Defer any that change no decision. 3. **Ask one question** in this shape, so the caller can accept, amend or reject in one line. Wait for the answer. 4. **Record** only what the caller decides. A skip stays open; "your call" accepts the recommendation shown. A revision reopens dependent answers. ```text Q<n>: <the decision>. <the question, with 2 to 4 options when they exist> My recommendation: <answer>, because <source or reason>. Tradeoff: <the one cost that matters> ``` ## Answerer The caller answers by default. On request ("let a council answer my interview"), a council answers through [Council](../council/SKILL.md)'s interview-panel mode; the caller accepts or amends its answers in one pass before anything is recorded, and authority, budgets and acceptance changes stay the caller's. ## BDD: acceptance as examples Drive each criterion to a Given/When/Then with an observable result and its proving evidence. Draft the example yourself as the recommendation; the caller accepts or edits it. "Works reliably" is not acceptance; ask what would be seen. ```gherkin Given a Job has already completed When the worker receives that Job again Then it returns the completed result without a second side effect # Proof: a redelivery test asserts one side effect and the completed result ``` ## DDD: one term per concept When a word is vague, overloaded or has synonyms, ask which term the domain uses. Record one term with a one-line definition and use only it in examples, notes, code and tests. [Domain](../domain/SKILL.md) owns deeper modeling. ## Show the state Open each turn with one line: what just settled and how many choices stay open. Show these lists on request, after a revision and at stop: - **Decided:** each choice; acceptance carries its Given/When/Then and proof. - **Terms:** each settled term with its one-line definition. - **Open:** unanswered choices, most consequential first. - **Deferred:** choices that change no next decision, and what revives them. Within authority, append settled decisions and terms to the intent source (root epic, issue or conversation), and Open and Deferred at stop. If a goal already runs on that epic, do not append a changed criterion or term; list it under Open as an acceptance change so the caller can hold and re-craft first. In BD: ```bash bd context --json # confirm the destination before any write bd show <epic-id> # on resume: reuse settled notes, start from Open bd update <epic-id> --append-notes "decided: <choice> | example: <Given/When/Then>" ``` ## Stop and hand off Stop when the caller stops, the next question would only restate a settled answer, the work proves to be one slice, or Craft Goal admission is decided: 1. outcome and non-goals; 2. terminal acceptance, each criterion with its proving evidence; 3. authority: reads, writes, external effects, Git, and when agents must ask; 4. budgets: numeric limits per wave of work and for the whole goal, which nothing renews, and how many results that change no decision trigger HOLD (implementation stops for causal review); 5. the first falsifiable question. Hand over the lists; the caller starts the next step: Craft Goal for several related experiments, RPI or [Plan](../plan/SKILL.md) for one outcome with items 1 to 3 and real bounds. Open items stay open; never fill one to finish.