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Scaffold Exercises

Creates course exercise folder structures with stub readmes that pass the course linter

Source Download skill
Repository
mattpocock/skills
Stars
309,658
Licence
MIT
Files
2 files

What it does

This skill teaches an AI agent how to set up the folder layout for a coding course: numbered sections, numbered exercises, and subfolders for problems, solutions, and explainers. It tells the agent to create placeholder readme files, run the course's lint command, fix any errors it reports, and commit the result with git. It also explains how to rename or move exercises safely using git mv so history is kept.

Great for

  • Course authors who use the ai-hero-cli project and want to stub out a new section from a plan
  • Developers maintaining a numbered exercise repository who need consistent folder naming

Skip it if

  • Projects that do not use the ai-hero-cli lint command, since the workflow depends on it and the agent would fail or improvise
  • Anyone wanting to write the actual exercise content, because the skill only creates stubs and placeholder readmes

Try asking

  • “Scaffold section 05 'Memory Skill Building' with three exercises from this plan outline”
  • “Create a new course section folder with explainer stubs for each exercise in my plan”
  • “Renumber exercises 01.03 onward to make room for a new exercise and re-run lint”
Step-by-step guide: how to use Scaffold ExercisesInstall, first run, examples, tips and FAQs

Install in 3 steps

  1. 1

    Download the skill

    The ZIP contains the 2 original files, the MIT licence and an attribution note.

    scaffold-exercises.zip
  2. 2

    Add it to your agent

    Claude Code (all projects)

    mkdir -p ~/.claude/skills && unzip scaffold-exercises.zip -d ~/.claude/skills/

    Claude Code / Agent SDK (this project only)

    mkdir -p .claude/skills && unzip scaffold-exercises.zip -d .claude/skills/

    Claude apps (web/desktop): Settings → Capabilities → Skills → Upload skill, and choose the ZIP.

  3. 3

    Ask for the task

    No special command needed — the agent reads the skill's description and loads it when your request matches. Try one of the prompts above.

Get better results

  • Give the agent a clear plan listing sections, exercise names, and which variants (explainer, problem, solution) each needs.
  • Review the git commit the agent makes before pushing, since it commits automatically as part of the workflow.
  • Run the skill inside a branch of the course repository so stubs can be discarded easily if the plan changes.

What's inside

  • agents/openai.yaml
  • SKILL.md

Redistributed unmodified from mattpocock/skills under the MIT licence. The assessment was drafted with AI from the skill's files on 2026-10-29. Skills can instruct your agent to run code — read SKILL.md and any scripts before installing.