From augments
Use whenever a request, plan, or design is unclear or underspecified — in any phase, before you build on it. Grills you one question at a time, answering from the codebase first and asking only what it genuinely cannot determine, then writes a short alignment brief. Skip for trivial or already-precise requests.
How this skill is triggered — by the user, by Claude, or both
Slash command
/augments:interview-meThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
Close the gap between what was asked and what is actually wanted — *before* you build on it. A cross-cutting clarification technique: grill a goal, a requirement, or a plan — the method is the same. The cheapest bug to fix is the one never built.
Close the gap between what was asked and what is actually wanted — before you build on it. A cross-cutting clarification technique: grill a goal, a requirement, or a plan — the method is the same. The cheapest bug to fix is the one never built.
1. Scan before you ask. Read the request, then explore the codebase and context for what is already decided: conventions, similar features, libraries in use, naming. Never ask what the code already answers.
2. Ask ONE question at a time. For each open decision, in one short message:
Prefer yes/no or a small multiple choice. Wait for the answer before the next question.
3. Use each answer to prune. An answer often settles later questions — drop them. Aim for ~3–6 questions total. If you need more, say why first.
4. Stop when another question would not change the outcome — or when the user says go. Do not gold-plate the interview.
5. Write a short alignment brief (not a spec): goal, decisions + rationale, explicit non-goals, open risks. Keep it tight — see brief-template.md. Save it to the project's briefs location (default .augments/briefs/{{YYYY-MM-DD}}-{{topic}}.md), or inline if tiny.
6. Offer, do not force, the next step: "Turn this into a plan?" → writing-plans.
npx claudepluginhub njoyimpeguy/augments --plugin augmentsGuides creation, editing, and verification of skills for AI coding agents using test-driven development with subagent scenarios. Use when authoring or debugging skills.