to-questionnaire
Turn a decision you can't fully answer into a questionnaire for someone else to fill in.
- License 许可
- MIT
- 最近更新
- 2026-11-10
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01 前言 Front Matter
- name
- to-questionnaire
- description
- Turn a decision you can't fully answer into a questionnaire for someone else to fill in.
- disable-model-invocation
- true
02 资源文件 1
- 参考文档agents/openai.yaml166 B
03 SKILL.md 正文
Turn something the user can't answer alone into a questionnaire: a Markdown document they hand to one person to fill in async, or fill out together over a meeting. The recipient holds knowledge the user lacks; the questionnaire pulls it out of them.
Grill the send, not the subject. Interview the user only about the send, which they can always answer: who it goes to, and what they need back. The questions in the document then target the gap between what the recipient knows and what the user needs.
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Who is it going to? Ask, in one exchange, the recipient's role, expertise, and relationship to the user. This fixes the questionnaire's tone and how much context it must carry. Done when you know who the recipient is and what they know that the user doesn't.
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What do you need back? Ask, in one exchange, the specific decisions or facts the user can't resolve alone and needs from this person. Done when you have a concrete list of what the user must walk away able to do or decide.
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Write the questionnaire. Draft questions aimed at the gap from steps 1–2, following the Document structure below. Write it to
to-questionnaire-<slug>.mdin the current directory (slug from the topic) and report the path. Done when the file exists and every item the user named in step 2 is covered by a question.
Document structure
Frame the document as a discovery questionnaire: the user lacks context, the recipient holds it. Order questions most-important-first, since async means you may only get one pass, and group them under ## headings by theme once there are more than a handful. Write it using the template below.
<questionnaire-template>
<Questionnaire title>
Purpose: why this questionnaire exists and the decision riding on it.
From: <the user>, To: <the recipient>, How your answers will be used: <where they go>
Context
One paragraph orienting a recipient who wasn't in the user's head. Enough to answer well, not a page.
How to answer
Deadline and rough effort. Partial answers and "I don't know" are useful: flag anything you're unsure of rather than skipping it.
<Theme heading>
One ## section per theme. Under each, its questions, most-important-first. Every question is one idea, never compound, with an answer stub directly beneath, and a one-line why this matters only where the question could be misread or invite a throwaway answer.
<question-example>
What load is the system expected to handle at launch?
Why this matters: it decides whether we provision for burst traffic now or defer it.
</question-example>
Anything else?
A closing catch-all: anything we didn't ask that we should know?
</questionnaire-template>
04 同仓库更多 Skills
improve
暂无简介
create-verification-skill
Generate a project-local verification skill that drives your app the way a user does — any language, framework, or platform. Use for /create-verification-skill, "make a control skill for this repo", or when a project has no scripted way to prove UI/CLI/service behavior.
tdd
Test-driven development. Use when the user wants to build features or fix bugs test-first, mentions "red-green-refactor", or wants integration tests.
maintain-verification-skill
Periodic pass that keeps a project's verification skill and feature map honest: parallel source readers per feature, one live session driving every feature, at most one PR of proven corrections. Use for /maintain-verification-skill or "audit the verify skill".
verdict
The Reviewer's Verdict on a PR: re-run the repo's verification skill at the PR's head, hold the PR's Evidence against it, and record pass or fail with nico-agent.
research
Investigate a question against high-trust primary sources and capture the findings as a Markdown file in the repo. Use when the user wants a topic researched, docs or API facts gathered, or reading legwork delegated to a background agent.