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.
npx skills add GreatMark/fable-field-guide-skills --skill interview-megit clone --depth 1 https://github.com/GreatMark/fable-field-guide-skillsWrote 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.
[](https://agentmods.dev/skills/greatmark/fable-field-guide-skills/interview-me)<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.
<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>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.
| Model | Per session | Once 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 |
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.
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
- 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.
- 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.
- 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.
- 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.
- 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.mdin 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.
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.
- 9d ago First seen · 98 lines · 162 tokens per session scan A 3fae8d9b37d6
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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