interactive-learning-experience-builder
Use when creating a grounded, accessible, self-contained interactive learning experience from repository knowledge sources.
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
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Use when creating a grounded, accessible, self-contained interactive learning experience from repository knowledge sources.
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
| name | interactive-learning-experience-builder |
| description | Use when creating a grounded, accessible, self-contained interactive learning experience from repository knowledge sources. |
Create one deterministic index.html that opens through file:// without a
server, network request, account, CDN, external font, or runtime dependency.
Embed the experience content, styles, scripts, SVG, fallbacks, and quiz banks.
Keep explanations and complete quiz content statically readable when JavaScript
or storage is unavailable.
scripts/generate_learning_experience.py,
assets/learning-experience-template.html, and the embedded executable quiz
state machine in assets/quiz-state-machine.js.scripts/validate_learning_experience.py, then
open the output directly through file:// and exercise every setting,
visualization fallback, quiz, keyboard path, visible focus state, reduced
motion behavior, and storage-disabled behavior.Start with this core workflow for one-off work. Create a thin repository adapter only when the same local discovery, source-selection, validation, or publishing workflow recurs. Base it on the repository adapter template. Keep stable local constraints in the adapter while leaving portable rendering, accessibility, payload validation, and offline behavior in this core skill.
http:// or https:// provenance
URLs, or explicit identifiers such as kb:topic/item.foundations, applied, and challenge—with
exactly 10 grounded questions each.Use when creating an interactive review for an ML-course lecture and its course-specific source, safety, and publishing rules are needed.
Improve the ML course interactive textbook in the student repository. Use when adding or revising OKF concepts, learning paths, browser labs, textbook rendering, agent manifests, contribution guides, or public-safe course knowledge modules. Enforce source-grounded pedagogy, formula-level rigor when appropriate, OKF validation, rendered textbook checks, and consistency between descriptions, skills, learning objectives, indexes, and the agent manifest.
Helps a student navigate the ML course materials. Use when a student asks to find a lecture, set up their local environment, find example notebooks, or understand the interactive textbook. Guides the student to the correct commands (like `uv sync`) and paths without giving away practical answers.