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To Questionnaire

Community
FeatherHunter
to-questionnaire

把一项你自己无法完全回答的决定,变成一份交给别人填写的问卷。

Overview

PublisherFeatherHunter
Repositorydsh-mattpocock-skills-deck
Skill nameto-questionnaire
Stars
263
Forks
22
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by FeatherHunter on GitHub. Read the source before you install it.

Installation

Install the To Questionnaire AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/FeatherHunter/dsh-mattpocock-skills-deck/tree/main/package/bundled-skills-zh/to-questionnaire
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/FeatherHunter/dsh-mattpocock-skills-deck.git /tmp/dsh-mattpocock-skills-deck
mkdir -p .claude/skills
cp -r /tmp/dsh-mattpocock-skills-deck/package/bundled-skills-zh/to-questionnaire .claude/skills/to-questionnaire
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable To Questionnaire in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads To Questionnaire on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so To Questionnaire is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

把用户一个人答不上来的事情,变成一份问卷:一份 Markdown 文档,可以交给某一个人异步填写,也可以在会议上一起填完。对方掌握着用户缺少的知识,问卷的作用就是把这份知识从对方那里问出来。

盘问的是「怎么发出去」,不是主题本身。 只就_发出_这件事访谈用户——这件事他们总能回答:这份问卷发给谁,以及需要从对方那里拿回什么。文档里的问题则对准对方所知与用户所需之间的那个缺口。

  1. 这份问卷发给谁? 用一次交流问清对方的角色、专长,以及与用户的关系。这决定问卷的语气,以及它必须自带多少背景信息。当你知道对方是谁、以及对方知道哪些用户不知道的东西时,就算完成。

  2. 你需要拿回什么? 用一次交流问清用户一个人决定不了、需要从这个人这里得到的具体决定或事实。当手上有一份明确的清单、写明用户问完之后必须能够做出或决定什么时,就算完成。

  3. 把问卷写出来。 按照下面的「文档结构」,起草对准第 1–2 步找出的那个缺口的问题。写到当前目录下的 to-questionnaire-<slug>.md(slug 取自主题),并报出路径。当文件已经写好、且用户在第 2 步点名的每一项都被某个问题覆盖到时,就算完成。

文档结构

把文档定位成一份发现式问卷:用户缺上下文,对方有上下文。问题按最重要的排在最前面,因为异步意味着你可能只有一次机会;一旦问题超过几个,就用 ## 标题按主题分组。用下面的模板来写。

<问卷标题>

目的: 这份问卷为什么存在,以及压在它上面的那个决定是什么。

来自: <用户>,发给: <接收者>,你的回答会被怎么用: <它们会用到哪里去>

背景

用一段话让没参与过用户思考过程的接收者快速了解背景。够他把问题答好即可,不要写成一整页。

如何作答

截止时间和大致的投入。部分作答和「我不知道」都有用:拿不准的地方标出来,而不是跳过。

<主题标题>

每个主题一个 ## 小节。每个小节下面放它的问题,最重要的在最前面。每个问题只讲一个意思,绝不复合,紧跟一个作答占位,只有在问题可能被误读、或者容易招来敷衍回答的地方,才加一行_为什么这重要_。

为什么这重要:这决定我们现在就为突发流量做容量准备,还是往后推。

还有别的吗?

一个收尾的兜底问题:有没有什么我们没问、但我们应该知道的?

Frequently asked questions

What does the To Questionnaire AI skill do?

把一项你自己无法完全回答的决定,变成一份交给别人填写的问卷。

Why use To Questionnaire on TypingMind?

Because you install it once and use it with any model. To Questionnaire is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install To Questionnaire in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/FeatherHunter/dsh-mattpocock-skills-deck/tree/main/package/bundled-skills-zh/to-questionnaire. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use To Questionnaire?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with To Questionnaire?

As many as you like. As long as a model supports skills, you can use To Questionnaire with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the To Questionnaire AI skill free?

Yes. It is published on GitHub by FeatherHunter under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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