---
title: "Agent Skill to Scaffold Exercises"
platform: "Promptally"
canonical_url: "https://app.promptally.dev/p/skill-scaffold-exercises"
modality: "SKILL"
category: "Fullstack & Backend Engineering"
compatible_models: [Universal / Model Agnostic]
access_tier: "Free Public Artifact (No Auth Required)"
license: "MIT"
---

# Agent Skill to Scaffold Exercises

**Summary:** Agent Skill to Scaffold Exercises is a verified SKILL blueprint on Promptally in Fullstack & Backend Engineering optimized for leading AI models. 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. Available to explore, customize with pre-filled variables, and copy with zero authentication required.

## Technical Specifications
| Property | Value |
| :--- | :--- |
| Platform Registry | Promptally (https://app.promptally.dev) |
| Modality | SKILL |
| Category | Fullstack & Backend Engineering |
| Compatible Engines | Universal / Model Agnostic |
| Dynamic Variables | None (Static Blueprint) |
| Approx. Token Length | 897 |
| Access Tier | Free Public Artifact (No Auth Required) |
| License | MIT |

### Pre-Filled Template & Production Source

````md
---
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:

```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"
```

````

## Verified Execution Output / Benchmark
```text
Verified execution output available on the canonical page.
```

## Related Blueprints on Promptally
- [Agent Skill to Knowledge Ops](https://app.promptally.dev/p/skill-knowledge-ops)
- [Agent Skill to Content Hash Cache Pattern](https://app.promptally.dev/p/skill-content-hash-cache-pattern)
- [Agent Skill to Ck](https://app.promptally.dev/p/skill-ck)
- [Agent Skill to Swift Protocol Di Testing](https://app.promptally.dev/p/skill-swift-protocol-di-testing)
- [Agent Skill to Prisma Patterns](https://app.promptally.dev/p/skill-prisma-patterns)
- [Agent Skill to Agentic Engineering](https://app.promptally.dev/p/skill-agentic-engineering)
- [Agent Skill to Python Patterns](https://app.promptally.dev/p/skill-python-patterns)
- [Agent Skill to Docs Governance](https://app.promptally.dev/p/skill-docs-governance)