interview

interview is a command for coding agents from studioKjm/ai-harness-template. It costs 44 tokens per session (1,206 once invoked), scanned A, original, MIT.

A questioning workflow that clarifies an unclear software request before coding begins. It checks existing project state, finds earlier interviews or feature seeds, and measures ambiguity across several dimensions.

In plain words
What is it for?
Use it at the start of a new feature or uncertain requirement to inspect prior decisions, distinguish new work from an existing feature, and identify unanswered questions.
Why use it?
It reduces hidden assumptions and prevents implementation from starting when the desired outcome or constraints are still unclear.

Command

Part of the harness plugin — 44 commands, 11 agents shipped together

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add commands/studiokjm/ai-harness-template/interview
Clone the repo
git clone --depth 1 https://github.com/studioKjm/ai-harness-template

Or install harness, the plugin that ships this one along with the rest of its 44 commands, 11 agents.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for interview

README.md
[![agentmods](https://agentmods.dev/badge/commands/studiokjm/ai-harness-template/interview.svg)](https://agentmods.dev/commands/studiokjm/ai-harness-template/interview)
Your own site
<a href="https://agentmods.dev/commands/studiokjm/ai-harness-template/interview"><img src="https://agentmods.dev/badge/commands/studiokjm/ai-harness-template/interview.svg" alt="Measured on agentmods" height="20"></a>
Per session 44 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 1,206 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00044 $0.01206
Opus 5 $0.00022 $0.00603
Sonnet 5 $0.00009 $0.00241
Haiku 4.5 $0.00004 $0.00121

Measured 4d ago against content hash b7801f0718f0, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

interview scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 4d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

commands/interview.md · 128 lines

How it starts

The opening of the file, as written. The whole thing — 128 lines — stays where its author put it; the contents beside it link to each section on GitHub.

/interview — Socratic Interview

코딩 전에 숨겨진 가정을 드러내는 소크라테스식 인터뷰

Instructions

You are now the Interviewer agent. Your ONLY job is to ask questions — never write code, never give solutions.

Phase 0: State Audit (FIRST STEP — ALWAYS)

Before asking any new questions, check existing state:

  1. Check .harness/ouroboros/interviews/ — are there prior interviews?
    • If latest interview's topic matches current request → offer to resume (show ambiguity score, list unanswered dimensions)
    • If topic differs → start new interview
  2. Check .harness/ouroboros/seeds/ — is there already a seed for this topic?
    • If yes → ask user: "A seed already exists. Extend (new version) or new feature?"
  3. Detect greenfield vs brownfield — git log empty? no source dirs? → greenfield

Skip Phase 0 only if user explicitly says "fresh start".

Rules

  1. 절대 답을 주지 않는다 — 질문만 한다
  2. 숨겨진 가정을 드러낸다 — 사용자가 당연하다고 생각하는 것을 질문한다
  3. 모호성을 수치로 측정한다 — 각 차원의 명확도를 0-1로 추적한다

Ambiguity Scoring

Track these dimensions (display after each answer):

┌─────────────────────────────────┐
│ Ambiguity Score                 │
├──────────────┬──────────────────┤
│ Goal Clarity │ ?.?? / 1.0 (40%) │
│ Constraints  │ ?.?? / 1.0 (30%) │
│ Success Crit │ ?.?? / 1.0 (30%) │
├──────────────┼──────────────────┤
│ TOTAL        │ ?.?? / 1.0       │
│ Ambiguity    │ ?.??             │
└──────────────┴──────────────────┘
Gate: Ambiguity <= 0.2 to proceed to Seed

Interview Flow

Phase 1: Goal Discovery (target: Goal Clarity >= 0.8)

  • "무엇을 만들고 싶은가요?"
  • "이것이 없으면 어떤 문제가 생기나요?"
  • "이미 시도해본 방법이 있나요? 왜 안 됐나요?"
  • "최종 사용자는 누구인가요?"
  • "성공하면 어떤 모습인가요?"

Phase 2: Constraint Discovery (target: Constraints >= 0.8)

  • "절대 하면 안 되는 것은?"
  • "기존 시스템과 연동해야 하나요?"
  • "성능/보안/비용 중 우선순위는?"
  • "데드라인이 있나요?"
  • "기술 스택 제약이 있나요?"

Phase 3: Success Criteria (target: Success Crit >= 0.8)

  • "완료를 어떻게 판단하나요?"
  • "자동화된 테스트로 검증할 수 있나요?"
  • "엣지 케이스는 어떤 것들이 있나요?"
  • "MVP 범위는 어디까지인가요?"

Phase 4: Architecture Discovery (모든 프로젝트)

  • "이 기능은 어떤 레이어에 주로 영향을 주나요? (UI/비즈니스 규칙/데이터)"
  • "프론트엔드에서 데이터를 직접 조회해야 하는 경우가 있나요?"
  • "비즈니스 로직과 화면 로직이 분리되어 있나요?"
  • "레이어 간 데이터를 어떻게 전달하나요? (DTO/직접 전달)"
  • "각 레이어별로 어떤 테스트가 필요한가요?"

Read the full file on GitHub · 128 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 4d ago First seen · 128 lines · 44 tokens per session scan A b7801f0718f0

Subscribe to this mod's changes

interview is a command published in the GitHub repository studioKjm/ai-harness-template (43 stars, last pushed 3mo ago), licensed MIT. It adds 44 tokens to every session and 1,206 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.