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Prototype

Community
FeatherHunter
prototype

构建一个用完即弃的原型,用来回答某个设计问题。当用户想先验证某种状态模型或逻辑是否顺手,或者想探索界面应该长什么样时使用。

Overview

PublisherFeatherHunter
Repositorydsh-mattpocock-skills-deck
Skill nameprototype
Stars
263
Forks
22
Bundled files
2
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.

  • 2 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

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

Installation

Install the Prototype 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/prototype
  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/prototype .claude/skills/prototype
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Prototype 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 Prototype 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 Prototype 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.

原型

原型是用来回答某个问题的用完即弃的代码。问题决定原型的形态。

选择分支

先判断当前要回答的是哪个问题。可以看用户的提示词,看周围的代码,如果用户就在旁边,也可以直接问:

  • 「这套逻辑/状态模型顺不顺手?」 → LOGIC.md。做一个可以分享的单个 HTML 文件(自由操作按钮,加上分标签页的引导式走查),让状态机走一遍那些在纸上很难推演的情形,并且让非开发人员也能自己操作。
  • 「这东西应该长什么样?」 → UI.md。在同一条路由上生成若干套截然不同的界面变体,用一个 URL 查询参数加一条悬浮底栏来切换。

两条分支产出的东西差别极大,选错就会把整个原型白白浪费掉。如果问题确实模棱两可,又联系不上用户,就默认选与周围代码更匹配的那条分支(后端模块 → 逻辑;页面或组件 → 界面),并在原型顶部写明这个假设。

两条分支都适用的规则

  1. 从第一天起就是用完即弃,并且明确标注。 把原型代码放在它真正会被使用的位置附近(紧挨着它为之做原型的模块或页面),让上下文一目了然;但命名要让随手翻看的人一眼就知道这是原型,不是生产代码。用完即弃的界面路由要遵守项目已有的路由约定,不要另造一套新的一级结构。
  2. 跑起来毫无门槛。 界面原型从项目任务运行器的一条命令启动:pnpm <name>、python <path>、bun <path> 等。逻辑演示则是用户双击就能打开的单个 HTML 文件。无论哪种,启动时都不需要动脑。
  3. 默认不做持久化。 状态放在内存里。持久化正是原型要_检验_的东西,而不是它应该依赖的东西。如果问题明确涉及数据库,就接一个临时数据库,或者用一个名字写得很清楚的本地文件,例如「PROTOTYPE, wipe me」。
  4. 别做打磨。 不写测试,除了让原型_能跑起来_所必需的部分之外不做错误处理,也不做抽象。重点是把东西快速学明白。
  5. 把状态露出来。 每一次操作之后(逻辑分支),或每一次切换变体时(界面分支),都把完整的相关状态打印或渲染出来,让用户看得见什么变了。
  6. 做完就归档。 把验证过的决定并回真实代码,然后把原型本身作为一份一手来源留存下来:提交到一个用完即弃的分支上,不放在 main 上,并在实现议题上留一个指向该分支的上下文指针。答案也要留档(结论,以及它解决了哪个问题),写进议题或某次提交。main 分支只保留那条被验证通过的决定。

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Prototype AI skill do?

构建一个用完即弃的原型,用来回答某个设计问题。当用户想先验证某种状态模型或逻辑是否顺手,或者想探索界面应该长什么样时使用。

Why use Prototype on TypingMind?

Because you install it once and use it with any model. Prototype 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 Prototype 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/prototype. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Prototype?

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 Prototype?

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

Is the Prototype 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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