Skip to main content
ZICQ

Skills ZICQ category:Agent Workflows scaffold-exercises

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.

538873 installs

Official URL:skills.sh

What this skill does

Intro in this page language first. The official description stays in its original wording; we do not rewrite SKILL.md.

What it does

Create exercise directory structures with sections, problems, solutions, and explainers that pass linting

When to use it

user wants to scaffold exercises, create exercise stubs, or set up a new course section

How agents load it

Per Agent Skills progressive disclosure: name and description load at startup (~100 tokens); the full SKILL.md body loads when the skill activates; scripts/, references/, and assets/ load only as needed. This file's sections: Scaffold Exercises; Directory naming; Exercise variants; Required files; Exercise Title; Workflow. It includes spec-recommended sections: step-by-step instructions, input/output examples.

File analysis

File analysis: instruction-only skill (SKILL.md). The agent loads the full body when activated.

Scaffold ExercisesDirectory namingExercise variantsRequired filesExercise TitleWorkflowLint rules summaryMoving/renaming exercisesExample: stubbing from a plan

Source category:skills.sh agent-skill

SKILL.md & Agent activation

Official spec ↗
name
scaffold-exercises
description
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.
  1. DiscoverThe client exposes names and descriptions to the agent.
  2. ActivateYour request or the task context selects the skill and loads its instructions.
  3. Load resourcesReferenced scripts, documentation and assets are used when needed.

Invocation syntax and available tools depend on your Agent client. Client integration guide ↗

Install this skill

Skills CLI ↗

Choose the target agent and installation scope, keep referenced package files, then verify the skill appears in the client's catalog.

Ask your Agent to install

Copy these instructions to a compatible agent and confirm the target directory matches your client.

Install the agent skill "scaffold-exercises" into my project. The full SKILL.md and official description are at https://zicq.com/en/skills/skl-fcb41a181c426bb6-Scaffold-Exercises.html
Save it as .cursor/skills/scaffold-exercises/SKILL.md or .claude/skills/scaffold-exercises/SKILL.md and keep the frontmatter name and description exactly as-is.

Full package on GitHub ↗

Install from the terminal · Skills CLI

Requires Node.js and npx. First inspect the repository's skill list to confirm the name.

npx skills add 'https://github.com/mattpocock/skills' --list

npx skills add 'https://github.com/mattpocock/skills' --skill 'scaffold-exercises'

The CLI lets you choose the agent interactively. The default scope is the project; use -g for user scope. Confirm package availability with the discovery command, then use npx skills list to inspect installed skills.

Readable layout

name: scaffold-exercises description: 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.

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:

# 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:

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:

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"

Related skills

Agent Workflows

Skill Creator

Guide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that exte…

Agent Workflows

Clawdhub

Use the ClawdHub CLI to search, install, update, and publish agent skills from clawdhub.com. Use when you need to fetch new skills on the fl…

Agent Workflows

Agent Team Orchestration

Orchestrate multi-agent teams with defined roles, task lifecycles, handoff protocols, and review workflows. Use when: (1) Setting up a team …

Agent Workflows

Superpowers

Spec-first, TDD, subagent-driven software development workflow. Use when: (1) building any new feature or app — triggers brainstorm → plan →…