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Scaffold Exercises

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vinvcn
scaffold-exercises

创建包含章节、题目、答案和讲解的练习目录结构,并确保通过 linting。适用于用户想 scaffold exercises、创建 exercise stubs,或设置新的课程章节时。

Overview

Publishervinvcn
Repositorymattpocock-skills-zh-CN
Skill namescaffold-exercises
Stars
4.3K
Forks
347
Bundled files
1
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.

  • 1 bundled files

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

  • Open source

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

Installation

Install the Scaffold Exercises 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.
  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/vinvcn/mattpocock-skills-zh-CN.git /tmp/mattpocock-skills-zh-CN
mkdir -p .claude/skills
cp -r /tmp/mattpocock-skills-zh-CN/skills/misc/scaffold-exercises .claude/skills/scaffold-exercises
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Scaffold Exercises 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 Scaffold Exercises 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 Scaffold Exercises 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.

Scaffold Exercises

创建能通过 pnpm ai-hero-cli internal lint 的 exercise directory structures,然后用 git commit 提交。

Directory naming

  • Sectionsexercises/ 下的 XX-section-name/(例如 01-retrieval-skill-building
  • Exercises:section 下的 XX.YY-exercise-name/(例如 01.03-retrieval-with-bm25
  • Section number = XX,exercise number = XX.YY
  • Names 使用 dash-case(小写、连字符)

Exercise variants

每个 exercise 至少需要这些 subfolders 中的一个:

  • problem/ — student workspace,包含 TODOs
  • solution/ — reference implementation
  • explainer/ — conceptual material,不含 TODOs

创建 stub 时,除非 plan 指定其他 variant,否则默认使用 explainer/

Required files

每个 subfolder(problem/solution/explainer/)都需要一个 readme.md,要求:

  • 非空(必须有真实内容,即使只有一行 title 也可以)
  • 没有 broken links

创建 stub 时,生成带 title 和 description 的最小 readme:

md
# Exercise Title

Description here

如果 subfolder 有 code,还需要 main.ts(>1 行)。但对 stubs 来说,readme-only exercise 可以接受。

Workflow

  1. Parse the plan — 提取 section names、exercise names 和 variant types
  2. Create directories — 对每个 path 执行 mkdir -p
  3. Create stub readmes — 每个 variant folder 一个带 title 的 readme.md
  4. Run lint — 执行 pnpm ai-hero-cli internal lint 验证
  5. Fix any errors — 迭代直到 lint 通过

Lint rules summary

linter(pnpm ai-hero-cli internal lint)检查:

  • 每个 exercise 有 subfolders(problem/solution/explainer/
  • 至少存在 problem/explainer/explainer.1/ 之一
  • primary subfolder 中存在非空 readme.md
  • 没有 .gitkeep files
  • 没有 speaker-notes.md files
  • readmes 中没有 broken links
  • readmes 中没有 pnpm run exercise commands
  • 除非是 readme-only,否则每个 subfolder 都需要 main.ts

Moving/renaming exercises

重新编号或移动 exercises 时:

  1. 使用 git mv(不是 mv)重命名 directories,保留 git history
  2. 更新 numeric prefix 以维持顺序
  3. 移动后重新运行 lint

Example:

bash
git mv exercises/01-retrieval/01.03-embeddings exercises/01-retrieval/01.04-embeddings

Example: stubbing from a plan

给定这样的 plan:

Section 05: Memory Skill Building
- 05.01 Introduction to Memory
- 05.02 Short-term Memory (explainer + problem + solution)
- 05.03 Long-term Memory

创建:

bash
mkdir -p exercises/05-memory-skill-building/05.01-introduction-to-memory/explainer
mkdir -p exercises/05-memory-skill-building/05.02-short-term-memory/{explainer,problem,solution}
mkdir -p exercises/05-memory-skill-building/05.03-long-term-memory/explainer

然后创建 readme stubs:

exercises/05-memory-skill-building/05.01-introduction-to-memory/explainer/readme.md -> "# Introduction to Memory"
exercises/05-memory-skill-building/05.02-short-term-memory/explainer/readme.md -> "# Short-term Memory"
exercises/05-memory-skill-building/05.02-short-term-memory/problem/readme.md -> "# Short-term Memory"
exercises/05-memory-skill-building/05.02-short-term-memory/solution/readme.md -> "# Short-term Memory"
exercises/05-memory-skill-building/05.03-long-term-memory/explainer/readme.md -> "# Long-term Memory"

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 Scaffold Exercises AI skill do?

创建包含章节、题目、答案和讲解的练习目录结构,并确保通过 linting。适用于用户想 scaffold exercises、创建 exercise stubs,或设置新的课程章节时。

Why use Scaffold Exercises on TypingMind?

Because you install it once and use it with any model. Scaffold Exercises 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 Scaffold Exercises in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/vinvcn/mattpocock-skills-zh-CN/tree/main/skills/misc/scaffold-exercises. 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 Scaffold Exercises?

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 Scaffold Exercises?

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

Is the Scaffold Exercises AI skill free?

Yes. It is published on GitHub by vinvcn 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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