Agent skill

Scaffold Exercises

by fossasia in fossasia/eventyay-interpretation

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

Apache-2.0Auto-check passedDevelopment

Install Scaffold Exercises

skills CLI
$ npx skills add fossasia/eventyay-interpretation --skill scaffold-exercises -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install fossasia/eventyay-interpretation scaffold-exercises --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/fossasia/eventyay-interpretation.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.agents/skills/scaffold-exercises .claude/skills/scaffold-exercises && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
scaffold-exercises
GitHub stars
1.6k
Used in
11 other repos
Token cost
~898 tokens
SKILL.md length
294 words
Files
1
Skills in repo
38
Repo updated
First seen
Licence
Apache-2.0

At a glance

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

  • Works in 5 steps: Parse the plan - extract section names,… → Create directories - mkdir -p for each… → Create stub readmes - one readme.md per… → …
  • User wants to scaffold exercises
  • SKILL.md covers Directory naming, Exercise variants, Required files and Workflow, plus 3 more sections
  • Calls pnpm and git

What it does

Scaffold Exercises is an agent skill from fossasia/eventyay-interpretation. 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.

Its SKILL.md is about 900 tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Development, covering Linting and formatting. It works with pnpm. The repository describes itself as: A plugin for live interpretation of video streams. The licence is Apache-2.0.

When your agent uses it

  • User wants to scaffold exercises
  • Create exercise stubs
  • Set up a new course section

Example prompts

  • “/scaffold-exercises”

Workflow steps

5 steps, taken from the first numbered list in SKILL.md.

  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

What it can do on your machine

Read from SKILL.md and the folder at commit 1ca0139. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • pnpm
    • git

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use pnpm and git, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Scaffold Exercises loads about 898 tokens when it runs. Until then it costs about 56 tokens; SKILL.md has 294 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~56
When it runs · the whole SKILL.md, loaded when a task matches
~898

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from fossasia/eventyay-interpretation at commit 1ca0139, republished under its Apache-2.0 licence (© fossasia). 294 words, ~898 tokens.

Download SKILL.mdSave it as .claude/skills/scaffold-exercises/SKILL.md (or your agent's skills folder).
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"

© fossasia, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in .agents/skills/scaffold-exercises of fossasia/eventyay-interpretation.

Open the folder on GitHubat commit 1ca0139

Used in 12 other repositories

We found 15 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 11 other GitHub owners. This page covers the copy in fossasia/eventyay-interpretation, which our catalogue first saw on October 7, 2026.

Compare with similar skills

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Antfuoyjt/uniapp-vue3-template627—~1.3kAutomated safety check: PassMIT
Add Plugin Ruleeslint-config/airbnb-extended131—~646Automated safety check: PassMIT
Kouchou AI Developmentdigitaldemocracy2030/kouchou-ai171—~540Automated safety check: NotesAGPL-3.0

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Works with

Categories

Questions about Scaffold Exercises

What does Scaffold Exercises do?

Create exercise directory structures with sections, problems, solutions, and explainers that pass linting. Scaffold Exercises is an agent skill from fossasia/eventyay-interpretation. Create exercise directory structures with sections, problems, solutions, and explainers that pass linting.

When should I use Scaffold Exercises?

Scaffold Exercises fits situations like: user wants to scaffold exercises; create exercise stubs; set up a new course section.

How do I install Scaffold Exercises in Claude Code?

Run `npx skills add fossasia/eventyay-interpretation --skill scaffold-exercises -a claude-code`. Or copy the skill folder (.agents/skills/scaffold-exercises in fossasia/eventyay-interpretation) into .claude/skills/scaffold-exercises in your project. Claude Code loads it when a task matches its description.

How do I install Scaffold Exercises in Codex?

Run `npx skills add fossasia/eventyay-interpretation --skill scaffold-exercises -a codex`. Or copy the skill folder (.agents/skills/scaffold-exercises in fossasia/eventyay-interpretation) into .agents/skills/scaffold-exercises in your project. Codex loads it when a task matches its description.

Can I use Scaffold Exercises in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add fossasia/eventyay-interpretation --skill scaffold-exercises -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/scaffold-exercises, .gemini/skills/scaffold-exercises, .github/skills/scaffold-exercises and .opencode/skills/scaffold-exercises in your project.

What does Scaffold Exercises need to run?

Going by SKILL.md and its folder, Scaffold Exercises needs the command-line tools its instructions call (pnpm and git).

Does Scaffold Exercises access the network?

SKILL.md contains no URLs. Its commands use git, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Scaffold Exercises safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Scaffold Exercises use?

Scaffold Exercises is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Scaffold Exercises use?

About 898 tokens (SKILL.md is roughly 3.6k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Scaffold Exercises?

Skills that share tags, products or a category with Scaffold Exercises: WooCommerce Dev Cycle (woocommerce/woocommerce, 11k stars), Check Push (pockethost/pockethost, 1.4k stars), Antfu (oyjt/uniapp-vue3-template, 627 stars) and Add Plugin Rule (eslint-config/airbnb-extended, 131 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Scaffold Exercises?

fossasia (a GitHub organization) maintains it in fossasia/eventyay-interpretation, which has 1,551 GitHub stars. The repository holds 38 skills in this directory. The repository was last updated on October 5, 2026.

Source: fossasia/eventyay-interpretation on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.