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.
npx skills add devcxl/mattpocock-skills-zh --skill scaffold-exercisesgit clone --depth 1 https://github.com/devcxl/mattpocock-skills-zhWrote 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.
[](https://agentmods.dev/skills/devcxl/mattpocock-skills-zh/scaffold-exercises)<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.
<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>- NVIDIA SkillSpector pass
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.
| Model | Per session | Once 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 |
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.
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 TODOsolution/— 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
- 解析计划 — 提取章节名称、练习名称和变体类型
- 创建目录 — 使用
mkdir -p创建每个路径 - 创建 readme 模板 — 每个变体文件夹一个
readme.md,包含标题 - 运行 lint — 执行
pnpm ai-hero-cli internal lint进行验证 - 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/或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:
- 使用
git mv(而不是mv)重命名目录 — 这样可以保留 git 历史 - 更新数字前缀以保持顺序
- 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"
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.
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.
- 11d ago First seen · 107 lines · 51 tokens per session scan A 26f08b32301f
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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