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Teach the user a new skill or concept, within this workspace.
$ npx -y skills add codingSamss/all-my-ai-needs --skill teach --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/teachContext preview
The summary Claude sees to decide when to auto-load this skill.
Teach the user a new skill or concept, within this workspace.
name: teach description: Teach the user a new skill or concept, within this workspace.
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
跨 Claude Code 与 Codex 两套 agent 的 skill 能力真源与同步规则。写入由 agent 执行,人只审校与决策。 把 Claude Code 与 Codex 的可复用能力收敛到一个仓库,按 platform-first 维护:每个平台独立持有自己的 skills/ 与运行约定,同名 skill 允许在两端并存,不强行抽象去重。 仓库要解决的是多端 AI 配置的漂移——GitHub 仓库、本地工作区、本地 CLI
Repo: codingSamss/all-my-ai-needs
查询 AIHOT 的中文 AI 资讯、精选、当前热点和日报。用户询问今天或最近的 AI 新闻、AI 圈动态、大模型或产品发布、OpenAI/Anthropic/Google 最新消息、AI 论文、AI 日报、AIHOT 精选、当前最热事件,或需要同步当前全部精选时使用。必须通过 aihot.news 的匿名只读…
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