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Requirements interview

Skill hansonkim/agent-skills/skills/requirements-interview

Portable AI agent skills for requirements discovery, review understanding, and phased execution

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npx -y skills add hansonkim/agent-skills --skill requirements-interview

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Socratic requirements interview for turning vague product, work, operational, or personal task requests into explicit requirements before execution. Use when the user wants to clarify requirements, define a PRD or work brief, explore scope and success criteria, perform an interview before planning, or avoid premature implementation. Produces an editable Interview Ledger plus PRD, ADR candidates, and handoff contract in the user's working language; does not implement, plan execution phases, create final ADRs, or run follow-up work.

SKILL.md

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Requirements Interview

Use this skill to run an understanding-and-judgment workflow before implementation. It converts vague requests into requirements artifacts through progressive, impact-framed Socratic questioning.

Responsibility Boundary

Do:

  • Ask one high-value question at a time.
  • Explain why the question matters and what the answer will affect.
  • Maintain an editable Interview Ledger during the interview.
  • Produce or update a PRD or brief, ADR candidates, and a handoff contract.
  • Preserve unresolved items as open questions instead of guessing.
  • Run a convergence check before closing the interview.

Do not:

  • Implement code, execute tasks, schedule work, or call /goal.
  • Run phase-harness or produce a full implementation phase plan.
  • Create final ADR files; only identify ADR candidates.
  • Hide requirements state in conversation context only.

Workflow

  1. Locate or create artifacts

    • Prefer docs/requirements/<slug>/.
    • Use these files unless the user requests another location:
      • interview-ledger.md
      • prd.md
      • adr-candidates.md
      • handoff-contract.md
  2. Set the artifact language

    • Write the Interview Ledger, PRD/brief, ADR candidates, and handoff contract in the user's working language.
    • Infer the working language from the interview language and project instructions. If the interview is in Korean, write user-facing artifacts in Korean.
    • Keep stable IDs, file names, commands, code identifiers, and accepted domain terms in their original form.
    • Do not copy English section titles from reference templates into final artifacts unless the user asks for English.
  3. Reconcile editable ledger first

    • If interview-ledger.md exists, read it before asking a new question.
    • Detect manual edits, deleted IDs, changed decisions, and new assumptions.
    • Summarize impacts and resolve material conflicts before continuing.
    • See ledger-schema.md.
  4. Ask impact-framed questions

    • Ask from broad frame to details: goal, audience, output, scope, success, constraints, risks, then execution handoff.
    • Every question must include an Impact Frame.
    • See interview-flow.md.
  5. Update the Interview Ledger after each answer

    • Add or update stable IDs such as REQ-001, DEC-001, ASM-001, OQ-001, IMPACT-001, and ADR-CAND-001.
    • Show changed items and newly introduced impacts before the next question.
    • If an answer changes prior decisions, run the correction protocol in ledger-schema.md.
  6. Checkpoint before narrowing

    • After goal/audience/output, scope/non-scope, and success criteria are clear, show a short checkpoint summary and ask whether to continue deeper.
    • Do not wait until the end to reveal accumulated requirements.
  7. Run the convergence check

    • Check whether the ledger has converged into a coherent requirements ontology before closing.
    • Do not invent a numeric ontology similarity score. If no real ontology comparison tool exists, use the qualitative convergence gate in convergence-check.md.
    • Continue interviewing if the latest answer creates new high-impact questions, unresolved conflicts, or blocking open questions.
  8. Close with artifacts

Question Format

Use this compact format for each substantive question:

### 질문
[one question]

### 왜 묻는가
[one or two sentences]

### 영향
- [affected area]: [consequence]
- [affected area]: [consequence]

### 답변에 따른 변화
- A: [likely scope/decision impact]
- B: [likely scope/decision impact]

For low-impact clarifications, compress the frame to one sentence, but still state the impact before asking.

Completion Criteria

End the interview only when all are true:

  • Goal, user/audience, and output are explicit.
  • Scope and non-scope are explicit.
  • Success criteria and verification approach are explicit enough for execution.
  • High-impact data, security, operational, cost, and schedule constraints are either confirmed or listed as open questions.
  • Key decisions, assumptions, rejected alternatives, and impacts are recorded.
  • The convergence check is converged: no material conflicts remain, no new high-impact question was introduced by the last answer, and every blocking open question has an owner and next decision point.
  • A handoff contract tells future agents what to read and preserve.

Follow-Up Routing

After this skill completes:

  • Small clear work may be implemented directly by the current agent.
  • Larger work may be handed to /goal.
  • Multi-phase work may be handed to phase-harness.
  • Architecture decisions should be checked later with /adr --check, using adr-candidates.md as context.

The artifacts, not the current chat session, are the source of truth for later execution.

What ships with it: 7 files

15.1 KB alongside SKILL.md

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