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 teachgit 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/teach)<a href="https://agentmods.dev/skills/devcxl/mattpocock-skills-zh/teach"><img src="https://agentmods.dev/badge/skills/devcxl/mattpocock-skills-zh/teach/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/teach"><img src="https://agentmods.dev/badge/skills/devcxl/mattpocock-skills-zh/teach.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 | $0.00018 | $0.02135 |
| Opus 5 | $0.00009 | $0.01068 |
| Sonnet 5 | $0.00004 | $0.00427 |
| Haiku 4.5 | $0.00002 | $0.00214 |
Grade A, and why
teach 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 10d 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 — 141 lines — stays where its author put it; the contents beside it link to each section on GitHub.
用户让你教他们一些东西。这是一个有状态的请求:他们打算通过多个会话来学习这个主题。
教学工作区
将当前目录视为教学工作区。他们的学习状态通过以下几个文件记录在此目录中:
MISSION.md:记录用户对这个主题感兴趣的原因的文档。所有教学都应以此为基准。使用 MISSION-FORMAT.md 中的格式。./reference/*.html:参考资料目录。这些是课程的压缩学习成果:速查表、参考算法、语法、瑜伽体式、词汇表。它们是学习的原始单元。应该是排版精美的文档,适合打印,并且为快速查阅而设计。RESOURCES.md:一份资源列表,可以探索以将你的教学建立在情境化知识上,或获取知识和智慧。使用 RESOURCES-FORMAT.md 中的格式。./learning-records/*.md:学习记录目录,记录用户已经学会了什么。这大致相当于软件开发中的架构决策记录:它们捕获那些非显而易见的教训和关键见解,后续可能需要修正,或用于驱动未来的课程。这些应用于计算最近发展区。文件命名格式为0001-<dash-case-name>.md,编号每次递增。使用 LEARNING-RECORD-FORMAT.md 中的格式。./lessons/*.html:课程目录。课程是一个独立、自包含的 HTML 输出,教授一个与使命紧密相关的、范围狭窄的内容。这是本工作区教学的主要单元。./assets/*:跨课程共享的可复用组件。参见 Assets(资源)。NOTES.md:供你记录用户偏好或工作笔记的草稿本。
哲学
要深度学习,用户需要三样东西:
- 知识(Knowledge),从高质量、高信任度的资源中获取
- 技能(Skills),通过你基于知识设计的高度相关的互动课程来习得
- 智慧(Wisdom),来自与其他学习者和实践者的交流
在 RESOURCES.md 充分充实之前,你的重点应该是寻找能帮助用户获取知识的高质量资源。绝不要信任你的参数化知识。
某些主题可能需要更多技能而非知识。学习理论物理可能更偏重知识。瑜伽则更偏重技能。
流利度 vs 存储强度
你应该小心区分两种学习:
- 流利度(Fluency strength):当下即时调取知识的能力
- 存储强度(Storage strength):长期保留知识的能力
流利度会给用户一种虚假的掌握感,但存储强度才是真正的目标。尝试设计能通过适度困难建立长期记忆的课程:
- 使用检索练习(从记忆中回忆)
- 间隔(将练习分散到不同时间)
- 交错(在练习中混合不同但相关的主题:仅适用于技能练习)
课程
课程是你生产的主要内容:知识和技能到达用户的单元。每个课程是一个独立的 HTML 文件,保存在 ./lessons/ 中,命名格式为 0001-<dash-case-name>.html,编号递增。
课程应该精美:干净、可读的排版和布局:因为用户以后会回来看。想想 Tufte 的设计哲学。
课程应该简短,能在很短的时间内完成。学习者的工作记忆非常有限,我们需要保持在它的容量之内。但每节课都应该给用户一个具体、可触及的成果,让他们能在此基础上继续前进。它应该直接与使命相关,并且处于用户的最近发展区。
如果可能,通过运行 CLI 命令为用户打开课程文件。
每个课程应通过 HTML 锚点链接到其他课程和参考文档。
每个课程应推荐一个主要来源供用户阅读或观看。这应该是你找到的关于该主题的最高质量、最高信任度的资源。
每个课程应包含一条提醒,让用户可以向智能体提问。智能体是他们的老师,可以协助解答任何不清楚的地方。
Assets(资源)
课程由可复用的组件构建,存放在 ./assets/ 下:样式表、测验组件、模拟器、图表辅助工具:任何可能被第二个课程复用的内容。
复用是默认原则,而非例外。在编写课程之前,先阅读 ./assets/,从已有的组件开始构建。当课程需要新东西且可复用时,将其作为组件写入 ./assets/ 并引用:切勿内联编写未来课程会重复的代码。
共享样式表是每个工作区的第一个组件:每节课都链接它,使所有课程看起来像一套连贯的课程体系,而非一堆一次性产物。随着工作区的增长,组件库也应随之增长。
使命
每节课都应该与使命挂钩:也就是用户对学习该主题感兴趣的原因。
What ships with it
5 files 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.
- 10d ago First seen · 141 lines · 18 tokens per session scan A 92c1a8ed5a33
teach is a skill published in the GitHub repository devcxl/mattpocock-skills-zh (331 stars, last pushed 5d ago), licensed MIT. It adds 18 tokens to every session and 2,135 once invoked, about $0.0001 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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