scaffold-exercises

$npx mdskill add mkurman/zorai/scaffold-exercises

Generate compliant exercise directories with sections, problems, and solutions.

  • Creates structured course materials with problem, solution, and explainer folders.
  • Integrates with pnpm ai-hero-cli internal lint for validation.
  • Decides folder structure based on section and exercise naming conventions.
  • Delivers ready-to-commit directories with minimal readme and code stubs.

SKILL.md

.github/skills/scaffold-exercisesView on GitHub ↗
---
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.

tags: [mattpocock, scaffold-exercises]
---

# 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"
```

More from mkurman/zorai

SkillDescription
account-management>
agile-scrum>
albumentationsFast image augmentation library (Albumentations). 70+ transforms for classification, segmentation, object detection, keypoints, and pose estimation. Optimized OpenCV-based pipeline with unified API across all CV tasks. Supports images, masks, bounding boxes, and keypoints simultaneously. Note: classic Albumentations (MIT) is no longer maintained; successor AlbumentationsX uses AGPL-3.0. For torchvision-native augmentations, use torchvision.transforms.v2.
aml-complianceAnti-Money Laundering (AML) and Know Your Customer (KYC) compliance workflow. Sanctions screening, PEP detection, transaction monitoring, suspicious activity reporting (SAR), and OFAC compliance.
anki-connectThis skill is for interacting with Anki through AnkiConnect, and should be used whenever a user asks to interact with Anki, including to read or modify decks, notes, cards, models, media, or sync operations.
approval-checkpoint-long-taskCanonical long-task pack for daemon-managed work with deliberate approval checkpoints, status summaries, rollback notes, and mobile-safe governance-aware updates.
auditing-goal-artifactsUse when reviewing recent zorai goal run outputs, closure markers, ledgers, or evidence bundles to judge whether completion is credible or to identify remaining uncertainty.
autogenAutoGen (Microsoft) — multi-agent conversation framework. Agent-to-agent chat, code generation & execution, tool use, group chat, and human-in-the-loop. Build collaborative AI systems with specialized agents.
backtraderPython backtesting framework for trading strategies. Data feeds, brokers, analyzers, and live trading support. Strategy development with commission models, slippage, and signal-based execution.
beautiful-mermaidRender Mermaid diagrams as SVG and PNG using the Beautiful Mermaid library. Use when the user asks to render a Mermaid diagram.