agentleFS
WenWuZhiDaoWenWuZhiDao/mattpocock-skills-zh/skills/misc/scaffold-exercises/SKILL.md

scaffold-exercises

Skill27 starsChanged 3 months agoMITView on GitHub

创建包含章节、习题、解答和讲解的练习目录结构,并能通过 lint 检查。当用户想搭建练习脚手架、创建练习存根,或新建一个课程章节时使用。

Files
SKILL.md

脚手架生成练习

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

目录命名

  • 章节(Sections):exercises/ 内的 XX-section-name/(例如 01-retrieval-skill-building)
  • 练习(Exercises):某章节内的 XX.YY-exercise-name/(例如 01.03-retrieval-with-bm25)
  • 章节编号 = XX,练习编号 = XX.YY
  • 名称采用 dash-case(小写,连字符)

练习变体

每个练习需要以下子文件夹中的至少一个:

  • problem/ —— 带 TODO 的学员工作区
  • solution/ —— 参考实现
  • explainer/ —— 概念性材料,无 TODO

生成桩代码时,除非计划另有规定,否则默认使用 explainer/。

必需文件

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

  • 非空(必须有真实内容,哪怕只有一行标题也行)
  • 没有失效链接

生成桩代码时,创建一个带标题和描述的最小 readme:

# Exercise Title

Description here

如果子文件夹有代码,还需要一个 main.ts(多于 1 行)。但对于桩代码而言,只有 readme 的练习也可以。

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 —— 为每个路径执行 mkdir -p
  3. 创建桩 readme —— 每个变体文件夹一个带标题的 readme.md
  4. 运行 lint —— 用 pnpm ai-hero-cli internal lint 验证
  5. 修复所有错误 —— 反复迭代直到 lint 通过

Lint 规则摘要

linter(pnpm ai-hero-cli internal lint)会检查:

  • 每个练习都有子文件夹(problem/、solution/、explainer/)
  • 至少存在 problem/、explainer/ 或 explainer.1/ 中的一个
  • 主子文件夹中存在非空的 readme.md
  • 没有 .gitkeep 文件
  • 没有 speaker-notes.md 文件
  • readme 中没有失效链接
  • readme 中没有 pnpm run exercise 命令
  • 除非只有 readme,否则每个子文件夹都需要 main.ts

移动/重命名练习

重新编号或移动练习时:

  1. 使用 git mv(而非 mv)重命名目录 —— 保留 git 历史
  2. 更新数字前缀以维持顺序
  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:从计划生成桩代码

给定这样一个计划:

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 桩:

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"

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.