interview-me

interview-me is a skill for Claude Code from GreatMark/fable-field-guide-skills. It costs 162 tokens per session (1,357 once invoked), scanned A, original, MIT.

A question-by-question process for turning unclear requirements into explicit decisions before implementation. It starts with choices that could change the software’s architecture, data model, scope, or behavior.

In plain words
What is it for?
Use it when requirements contain unresolved choices or when you want clarification before coding. It can help decide system structure, stored data, behavior, user experience, and visual details, in that order of impact.
Why use it?
It reduces the risk of building the wrong thing because important decisions were left implicit. Asking one focused question at a time makes trade-offs easier to understand and answer.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin. Also seen: names the AskUserQuestion tool; mentions Claude Code.

Part of the fable-field-guide-skills plugin — 9 skills, 2 hooks shipped together

Good fit Use it when requirements contain unresolved choices or when you want clarification before coding. It can help decide system structure, stored data, behavior, user experience, and visual details, in that order of impact.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/greatmark/fable-field-guide-skills/interview-me
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.

Any agent
npx skills add GreatMark/fable-field-guide-skills --skill interview-me
Clone the repo
git clone --depth 1 https://github.com/GreatMark/fable-field-guide-skills

Made for: Claude Code.

Or install fable-field-guide-skills, the plugin that ships this one along with the rest of its 9 skills, 2 hooks.

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-me

README.md
[![agentmods](https://agentmods.dev/badge/skills/greatmark/fable-field-guide-skills/interview-me/github.svg)](https://agentmods.dev/skills/greatmark/fable-field-guide-skills/interview-me)
Your own site
<a href="https://agentmods.dev/skills/greatmark/fable-field-guide-skills/interview-me"><img src="https://agentmods.dev/badge/skills/greatmark/fable-field-guide-skills/interview-me/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for interview-me

Your own site · 80×15
<a href="https://agentmods.dev/skills/greatmark/fable-field-guide-skills/interview-me"><img src="https://agentmods.dev/badge/skills/greatmark/fable-field-guide-skills/interview-me.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 162 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,357 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
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 5.1 $0.00162 $0.01357
Opus 5 $0.00081 $0.00678
Sonnet 5 $0.00032 $0.00271
Haiku 4.5 $0.00016 $0.00136

Measured 9d ago against content hash 3fae8d9b37d6, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, from the pricing page.

Security

Grade A, and why

interview-me 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 9d 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.

skills/interview-me/SKILL.md · 98 lines

How it starts

The opening of the file, as written. The whole thing — 98 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Interview Me

Resolve the user's known unknowns by interviewing them, one question at a time, before any implementation.

Workflow

  1. Build the question list silently. From the conversation, any blindspot-pass findings if that ran first, plus a quick scan of relevant code (for greenfield work with no code yet, the conversation alone), list the ambiguities in the task. For each, note which layer the answer would change: architecture/data model > scope/behavior > UX details > cosmetics. Sort by that impact; when one answer gates other questions, ask the gating question first. If the list holds 10+ architecture-level ambiguities, propose splitting the task instead of interviewing through it. Open by telling the user how many questions you plan to ask, architecture first.
  2. Ask one question at a time. Use the structured question tool when available (in Cursor, the AskQuestion option card; in Claude Code, the AskUserQuestion tool). Never ask a bare open-ended "what do you want?". For decision questions:
    • Present 2-4 concrete options, recommended option first, marked "(recommended)" in its label.
    • Say why the answer matters (what it would change) and the trade-offs in the question body; keep option labels short.
    • Treat a free-text answer outside the listed options as a first-class answer. For fact questions (who the users are, how much data), ask openly — options, if offered, are "e.g." examples, never a forced choice.
  3. Re-plan after every answer. The question list is a living document: drop questions the answer resolved, rewrite options it invalidated, add ambiguities it exposed. If an answer does not address the question, restate it once — that is not re-asking. If it introduces a new requirement, do not expand it that turn: batch-ask which of the new items are in or out of scope; after three consecutive turns of new requirements, pause the interview and re-scope the task. If the user changes a requirement mid-interview, say which earlier decisions it affects, update those, and rebuild the remaining list.
  4. Stop early. End the interview once remaining unknowns would not change the approach — typically 3-7 questions. At question 7, if approach-changing unknowns still remain, stop asking one-by-one: lay out the remaining list in a single message, let the user pick what to settle now, and record the rest as assumptions. Never pad with questions you could answer yourself from the code; go read the code instead.
  5. Deliver the decision record. Summarize in chat, in the language of the conversation, as a compact decision record ("known knowns") with three parts: decisions made; assumptions (defaults chosen in place of an answer); deliberately deferred unknowns, each with the assumption chosen for it. Classify by who chose: the user explicitly picked = decision; the user explicitly postponed = deferred; the agent defaulted = assumption. Write each decision on one line: chosen — why it matters — alternatives shown — depends on (which earlier decisions, if any). Before delivering, re-read the record for contradictions; the record reflects the latest state — when the user reverses a decision, rewrite its entry in place, never append a conflicting one. Keep it ready to paste into an implementation prompt or plan. If planning or implementation will happen in a later session (or the user asks), also write it to interview-decisions.md in the project root. Do not commit it unless the user asks; in a git repo, exclude it locally: n=interview-decisions.md; p="$(git rev-parse --git-path info/exclude 2>/dev/null)" && { mkdir -p "$(dirname "$p")" && { grep -qxF "$n" "$p" || echo "$n" >> "$p"; }; } 2>/dev/null || echo "note: could not exclude $n — leaving it untracked, never staging it" If planning comes next, hand this decision record to implementation-plan as its input.

Read the full file on GitHub · 98 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. 9d ago First seen · 98 lines · 162 tokens per session scan A 3fae8d9b37d6

Subscribe to this mod's changes

interview-me is a skill published in the GitHub repository GreatMark/fable-field-guide-skills (9 stars, last pushed 1mo ago), licensed MIT. It adds 162 tokens to every session and 1,357 once invoked, about $0.0008 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 2026-08-31.

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