teach：mattpocock/skills 方法论: productivity/teach，适用于工程实践、代码质量与开发流程优化。
teach：mattpocock/skills 方法论: productivity/teach，适用于工程实践、代码质量与开发流程优化。
> 来源: mattpocock/skills (141k stars) — Total TypeScript 创始人 Matt Pocock
> 类目: 生产力
> 原文件: skills/productivity/teach/SKILL.md
> 模型推荐: gpt-4.1 (代码工程)
Matt Pocock (Total TypeScript, 141k stars) 沉淀下来的"日常代码工程动作"skill 模板。
每一个对应一个具体动作(代码评审 / TDD / 重构 / 文档对齐 / 任务交接),不是工具,是"该怎么干这件事"的工作流模板。
michael 强调"skill 要有相应的指导功能,指导用户使用",所以这里加了下面两节让 Agent 和用户对接。
---
1. 接到任务后先按这个 skill 的检查清单走一遍
2. 清单里如果出现"先做 X 才能做 Y"的红线 → 必须先完成 X
3. 跑完清单后开始动手(改代码 / 写文档 / 评审)
4. 完工前用 `verification-before-completion`(superpowers) 或 `verification` 类 step 自检
5. 任务量大时考虑用 `dispatching-parallel-agents`(superpowers) 或 `wayfinder`(mattpocock) 拆解
1. 告诉 Agent 你要做什么(一句话即可)
2. Agent 跑 skill 清单时不要打断 — 它可能在收集上下文
3. 如果 Agent 主动问你"代码评审要不要安排一个 reviewer"等决策点 → 直接回答
4. 完工后让 Agent 跑一遍自检再交回
5. 整个过程 Agent 自动化,不需要手工介入
---
---
name: teach
description: Teach the user a new skill or concept, within this workspace.
disable-model-invocation: true
argument-hint: "What would you like to learn about?"
---
The user has asked you to teach them something. This is a stateful request - they intend to learn the topic over multiple sessions.
Treat the current directory as a teaching workspace. The state of their learning is captured in this directory in several files:
To learn at a deep level, the user needs three things:
Before the `RESOURCES.md` is well-populated, your focus should be to find high-quality resources which will help the user acquire knowledge. Never trust your parametric knowledge.
Some topics may require more skills than knowledge. Learning more about theoretical physics might be more knowledge-based. For yoga, more skills-based.
You should be careful to split between two types of learning:
Fluency can give the user an illusory sense of mastery, but storage strength is the real goal. Try to design lessons which build long-term retention by desirable difficulty:
A lesson is the main thing you produce — the unit in which knowledge and skills reach the user. Each lesson is one self-contained HTML file, saved to `./lessons/` and titled `0001-<dash-case-name>.html` where the number increments each time.
A lesson should be **beautiful** — clean, readable typography and layout — since the user will return to these later to review. Think Tufte.
The lesson should be short, and completable very quickly. Learners' working memory is very small, and we need to stay within it. But each lesson should give the user a single tangible win that they can build on. It should be directly tied to the mission, and should be in the user's zone of proximal development.
If possible, open the lesson file for the user by running a CLI command.
Each lesson should link via HTML anchors to other lessons and reference documents.
Each lesson should recommend a primary source for the user to read or watch. This should be the most high-quality, high-trust resource you found on the topic.
Each lesson should contain a reminder to ask followup questions to the agent. The agent is their teacher, and can assist with anything that's unclear.
Lessons are built from reusable **components**, stored in `./assets/`: stylesheets, quiz widgets, simulators, diagram helpers — anything a second lesson could reuse.
Reuse is the default, not the exception. Before authoring a lesson, read `./assets/` and build from the components already there. When a lesson needs something new and reusable, write it as a component in `./assets/` and link to it — never inline code a future lesson would duplicate.
A shared stylesheet is the first component every workspace earns: every lesson links it, so the lessons look like one consistent course rather than a pile of one-offs. As the workspace grows, so should the component library.
Every lesson should be tied into the mission - the reason that the user is interested in learning about the topic.
If the user is unclear about the mission, or the `MISSION.md` is not populated, your first job should be to question the user on why they want to learn this.
Failing to understand the mission will mean knowledge acquisition is not grounded in real-world goals. Lessons will feel too abstract. You will have no way of judging what the user should do next.
Missions may change as the user develops more skills and knowledge. This is normal - make sure to update the `MISSION.md` and add a learning record to capture the change. Confirm with the user before changing the mission.
Each lesson, the user should always feel as if they are being challenged 'just enough'.
The user may specify an exact thing they want to learn. If they don't, figure out their zone of proximal development by:
Lessons should be designed around a skill the user is going to learn. The knowledge in the lesson should be only what's required to acquire that skill. You teach the knowledge first, then get the user to practice the skills via an interactive feedback loop.
Knowledge should first be gathered from trusted resources. Use `RESOURCES.md` to keep track of them. Lessons should be littered with citations - links to external resources to back up any claim made. This increases the trustworthiness of the lesson.
For acquiring knowledge, difficulty is the enemy. It eats working memory you need for understanding.
If knowledge is all about acquisition, skills are about durability and flexibility. Make the knowledge stick.
For skill acquisition, difficulty is the tool. Effortful retrieval is what builds storage strength. Skills should be taught through interactive lessons. There are several tools at your disposal:
Each of these should be based on a **feedback loop**, where the user receives feedback on their performance. This feedback loop should be as tight as possible, giving feedback immediately - and ideally automatically.
For quizzes, each answer should be exactly the same number of words (and characters, if possible). Don't give the user any clues about the answer through formatting.
Wisdom comes from true real-world interaction - testing your skills outside the learning environment.
When the user asks a question that appears to require wisdom, your default posture should be to attempt to answer - but to ultimately delegate to a **community**.
A community is a place (online or offline) where the user can test their skills in the real world. This might be a forum, a subreddit, a real-world class (budget permitting) or a local interest group.
You should attempt to find high-reputation communities the user can join. If the user expresses a preference that they don't want to join a community, respect it.
While creating lessons, you should also create reference documents. Lessons can reference these documents - they are useful for tracking raw units of knowledge useful across lessons.
Lessons will rarely be revisited later - reference documents will be. They should be the compressed essence of the lesson, in a format designed for quick reference.
Some learning topics lend themselves to reference:
Glossaries, in particular, are an essential reference. Once one is created, it should be adhered to in every lesson.
The user will sometimes express preferences of how they want to be taught, or things you should keep in mind. This is the place to record those preferences, so you can refer back to them when designing lessons or working with the user.
本 skill 专注于teach，mattpocock/skills 方法论: productivity/teach。它将相关流程标准化，帮助用户更快拿到可靠结果，减少重复手工操作。
当你需要在teach相关工作中获得稳定、可复用的产出时最适合——无论是单次任务还是纳入日常工作流，都能直接调用。
需要一个具体的项目或任务上下文，最好带有代码仓库或需求文档。
1. 告诉 Agent 你要做什么(一句话即可)
2. Agent 跑 skill 清单时不要打断 — 它可能在收集上下文
3. 如果 Agent 主动问你"代码评审要不要安排一个 reviewer"等决策点 → 直接回答
4. 完工后让 Agent 跑一遍自检再交回
5. 整个过程 Agent 自动化,不需要手工介入
---
本 skill 专注于teach，mattpocock/skills 方法论: productivity/teach。它将相关流程标准化，帮助用户更快拿到可靠结果，减少重复手工操作。
当你需要在teach相关工作中获得稳定、可复用的产出时最适合——无论是单次任务还是纳入日常工作流，都能直接调用。
需要一个具体的项目或任务上下文，最好带有代码仓库或需求文档。