scaffold-exercises

scaffold-exercises is a skill for Claude Code, Codex from devcxl/mattpocock-skills-zh. It costs 51 tokens per session (1,079 once invoked), scanned A, original, MIT.

A template builder for coding exercises, organized into sections and exercises with problem, solution, and explanation folders. It creates the files and structure expected by the project’s lint checker.

In plain words
What is it for?
Use it to start a course section, create an exercise template, add readme files, and check the result with the project lint command.
Why use it?
It removes repetitive folder and file setup and helps catch missing or invalid exercise content early.

Skill for Claude CodeCodex

Written for Claude Code and Codex: shipped in a Claude Code plugin, but also agents/openai.yaml present.

Part of the mattpocock-skills plugin — 37 skills shipped together

Good fit Use it to start a course section, create an exercise template, add readme files, and check the result with the project lint command.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/devcxl/mattpocock-skills-zh/scaffold-exercises
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add devcxl/mattpocock-skills-zh --skill scaffold-exercises
Clone the repo
git clone --depth 1 https://github.com/devcxl/mattpocock-skills-zh

Made for: Claude Code, Codex.

Or install mattpocock-skills, the plugin that ships this one along with the rest of its 37 skills.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for scaffold-exercises

README.md
[![agentmods](https://agentmods.dev/badge/skills/devcxl/mattpocock-skills-zh/scaffold-exercises/github.svg)](https://agentmods.dev/skills/devcxl/mattpocock-skills-zh/scaffold-exercises)
Your own site
<a href="https://agentmods.dev/skills/devcxl/mattpocock-skills-zh/scaffold-exercises"><img src="https://agentmods.dev/badge/skills/devcxl/mattpocock-skills-zh/scaffold-exercises/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for scaffold-exercises

Your own site · 80×15
<a href="https://agentmods.dev/skills/devcxl/mattpocock-skills-zh/scaffold-exercises"><img src="https://agentmods.dev/badge/skills/devcxl/mattpocock-skills-zh/scaffold-exercises.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 51 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,079 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00051 $0.01079
Opus 5 $0.00026 $0.00540
Sonnet 5 $0.00010 $0.00216
Haiku 4.5 $0.00005 $0.00108

Measured 11d ago against content hash 26f08b32301f, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

scaffold-exercises scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/misc/scaffold-exercises/SKILL.md · 107 lines

How it starts

The opening of the file, as written. The whole thing — 107 lines — stays where its author put it; the contents beside it link to each section on GitHub.

搭建练习框架

创建能通过 pnpm ai-hero-cli internal lint 检查的练习目录结构,然后使用 git commit 提交。

目录命名规则

  • 章节(Sections):位于 exercises/ 下的 XX-section-name/ 格式(例如 01-retrieval-skill-building
  • 练习(Exercises):位于章节内的 XX.YY-exercise-name/ 格式(例如 01.03-retrieval-with-bm25
  • 章节编号 = XX,练习编号 = XX.YY
  • 名称使用短横线命名法(小写字母、连字符)

练习变体

每个练习至少需要以下子文件夹之一:

  • problem/ — 学生工作区,包含 TODO
  • solution/ — 参考实现
  • explainer/ — 概念讲解材料,不包含 TODO

创建模板时,除非计划另有说明,否则默认使用 explainer/

必需文件

每个子文件夹(problem/solution/explainer/)需要一个 readme.md,要求:

  • 不能为空(必须有实际内容,哪怕只有一行标题也可以)
  • 不能有损坏的链接

创建模板时,创建一个包含标题和描述的极简 readme:

# Exercise Title

Description here

如果子文件夹包含代码,还需要一个 main.ts(超过 1 行)。但对于模板来说,仅有 readme 的练习也是可以的。

工作流程

  1. 解析计划 — 提取章节名称、练习名称和变体类型
  2. 创建目录 — 使用 mkdir -p 创建每个路径
  3. 创建 readme 模板 — 每个变体文件夹一个 readme.md,包含标题
  4. 运行 lint — 执行 pnpm ai-hero-cli internal lint 进行验证
  5. 修复错误 — 反复修改直到 lint 通过

Lint 规则摘要

linter(pnpm ai-hero-cli internal lint)检查以下内容:

  • 每个练习都有子文件夹(problem/solution/explainer/
  • 至少存在 problem/explainer/explainer.1/ 其中之一
  • 主要子文件夹中存在 readme.md 且不为空
  • 没有 .gitkeep 文件
  • 没有 speaker-notes.md 文件
  • readme 中没有损坏的链接
  • readme 中没有 pnpm run exercise 命令
  • 每个子文件夹需要 main.ts,除非仅有 readme

移动/重命名练习

在重新编号或移动练习时:

  1. 使用 git mv(而不是 mv)重命名目录 — 这样可以保留 git 历史
  2. 更新数字前缀以保持顺序
  3. 移动后重新运行 lint

示例:

git mv exercises/01-retrieval/01.03-embeddings exercises/01-retrieval/01.04-embeddings

示例:根据计划创建模板

给定一个计划如:

Section 05: Memory Skill Building
- 05.01 Introduction to Memory
- 05.02 Short-term Memory (explainer + problem + solution)
- 05.03 Long-term Memory

创建:

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

然后创建 readme 模板:

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"

Read the full file on GitHub · 107 lines

Files

What ships with it

1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 11d ago First seen · 107 lines · 51 tokens per session scan A 26f08b32301f

Subscribe to this mod's changes

scaffold-exercises is a skill published in the GitHub repository devcxl/mattpocock-skills-zh (340 stars, last pushed 6d ago), licensed MIT. It adds 51 tokens to every session and 1,079 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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