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Scaffold Exercises

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mattpocock
scaffold-exercises

Create exercise directory structures with sections, problems, solutions, and explainers that pass linting. Use when user wants to scaffold exercises, create exercise stubs, or set up a new course section.

Overview

Publishermattpocock
Repositoryskills
Skill namescaffold-exercises
Stars
264.4K
Forks
22.3K
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 mattpocock 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/mattpocock/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/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

Create exercise directory structures that pass pnpm ai-hero-cli internal lint, then commit with git commit.

Directory naming

  • Sections: XX-section-name/ inside exercises/ (e.g., 01-retrieval-skill-building)
  • Exercises: XX.YY-exercise-name/ inside a section (e.g., 01.03-retrieval-with-bm25)
  • Section number = XX, exercise number = XX.YY
  • Names are dash-case (lowercase, hyphens)

Exercise variants

Each exercise needs at least one of these subfolders:

  • problem/ - student workspace with TODOs
  • solution/ - reference implementation
  • explainer/ - conceptual material, no TODOs

When stubbing, default to explainer/ unless the plan specifies otherwise.

Required files

Each subfolder (problem/, solution/, explainer/) needs a readme.md that:

  • Is not empty (must have real content, even a single title line works)
  • Has no broken links

When stubbing, create a minimal readme with a title and a description:

md
# Exercise Title

Description here

If the subfolder has code, it also needs a main.ts (>1 line). But for stubs, a readme-only exercise is fine.

Workflow

  1. Parse the plan - extract section names, exercise names, and variant types
  2. Create directories - mkdir -p for each path
  3. Create stub readmes - one readme.md per variant folder with a title
  4. Run lint - pnpm ai-hero-cli internal lint to validate
  5. Fix any errors - iterate until lint passes

Lint rules summary

The linter (pnpm ai-hero-cli internal lint) checks:

  • Each exercise has subfolders (problem/, solution/, explainer/)
  • At least one of problem/, explainer/, or explainer.1/ exists
  • readme.md exists and is non-empty in the primary subfolder
  • No .gitkeep files
  • No speaker-notes.md files
  • No broken links in readmes
  • No pnpm run exercise commands in readmes
  • main.ts required per subfolder unless it's readme-only

Moving/renaming exercises

When renumbering or moving exercises:

  1. Use git mv (not mv) to rename directories - preserves git history
  2. Update the numeric prefix to maintain order
  3. Re-run lint after moves

Example:

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

Example: stubbing from a plan

Given a plan like:

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

Create:

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

Then create 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?

Create exercise directory structures with sections, problems, solutions, and explainers that pass linting. Use when user wants to scaffold exercises, create exercise stubs, or set up a new course section.

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/mattpocock/skills/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 mattpocock 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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