aihot
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Teach the user a new skill or concept, within this workspace.
Claude Code
$ npx -y skills add codingSamss/all-my-ai-needs --skill teach --agent claude-code
Codex
$ npx skills add codingSamss/all-my-ai-needs --skill teach -a codex -y
Cursor
$ npx skills add codingSamss/all-my-ai-needs --skill teach -a cursor -y
Gemini CLI
$ npx skills add codingSamss/all-my-ai-needs --skill teach -a gemini-cli -y
GitHub Copilot
$ npx skills add codingSamss/all-my-ai-needs --skill teach -a github-copilot -y
OpenCode
$ npx skills add codingSamss/all-my-ai-needs --skill teach -a opencode -y
Windsurf
$ npx skills add codingSamss/all-my-ai-needs --skill teach -a windsurf -y
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. Workspace paths (`./lessons/` and the rest) resolve from the directory `/teach` was run in; only the `*-FORMAT.md` links resolve from this skill's folder. 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**, with 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, and anything else 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.
跨 Claude Code 与 Codex 两套 agent 的 skill 真源,以及点名安装的 Claude Mods 来源与迁移约定。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
查询 AIHOT(aihot.news)的中文 AI 资讯。用户问今天或最近 AI 圈发生了什么、AI 新闻和 AI 日报、大模型与 AI 产品发布、OpenAI/Anthropic/Google 等公司或某个 AI 话题的最新消息、当前最热的 AI…
通过 Apifox CLI 管理 Apifox 项目资源。触发场景:运行接口自动化测试/测试套件,查询/创建/更新/删除接口、环境、Schema、Mock、分支等项目资源,导入导出 API 文档,查看测试报告,管理 Runner、定时任务、通知等 CI/CD…
Apifox 接口测试用例:test-case 与 test-data 的查询、创建、更新、删除、分类和运行;处理测试步骤、断言、提取变量、前后置处理器、数据集,以及“CLI 创建后前端测试步骤无法展示”等问题。
Read Bilibili (B站) content. Actions: search videos, view hot/rank feeds, read video details, prepare audio, and read subtitles…
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Read X/Twitter content via Bird CLI. Actions: read tweets, search, view all bookmarks or bookmark folders, trending, news,…
You MUST use this before any creative work - creating features, building components, adding functionality, modifying behavior, or…
Ask which skill or flow fits your situation. A router over the skills in this repo.