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scaffold-exercises

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.

DeepseekModel 官方收录技能 质量 优秀 · 90 v1.0.0

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https://deepseekmodel.com/api/download.php?id=mattpocock-skills-skills-misc-scaffold-exercises-skill-md&format=skill
下载 .skill 标准格式,含 system_prompt 与 model_config,导入任意 Agent 框架即可使用
.skill 文件中 system_prompt 字段的实际内容。
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: # 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 Parse the plan - extract section names, exercise names, and variant types Create directories - mkdir -p for each path Create stub readmes - one readme.md per variant folder with a title Run lint - pnpm ai-hero-cli internal lint to validate 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: Use git mv (not mv ) to rename directories - preserves git history Update the numeric prefix to maintain order Re-run lint after moves Example: 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: 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"
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下载的 .skill 包内含以下字段。
字段 说明
format格式标识(skill/v1)
skill_id技能唯一 ID
name技能名称
version版本号
description技能描述
category所属分类(数组)
trigger_words触发词列表
tags标签列表
source来源标识
source_url来源链接(本页地址)
exported_at导出时间(每次下载生成)
system_prompt系统提示词正文
model_config模型参数:provider / model / temperature / max_tokens / top_p
examples示例
install_guide各平台导入说明(Coze / Dify / Claude / 自定义框架)
同一份技能可按不同平台格式导出。
.skill 标准格式,含 system_prompt 与 model_config,导入任意 Agent 框架即可使用 下载
.skillpro 增强格式,额外含脚本 / 工具 / 依赖 / 钩子占位 下载
.json 纯 JSON 导出,只含 system_prompt 与模型参数 下载
Coze 带 frontmatter 的 Markdown,Coze 平台导入用 下载
Dify Dify DSL,创建应用后直接导入 下载

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