| name | book-docusaurus |
| description | Scaffold, structure, and deploy the Physical AI textbook in Docusaurus with book-aware content and RAG-ready exports. Use when creating or updating the Docusaurus site, adding chapters, configuring sidebar, or deploying to GitHub Pages/Vercel. |
Book Docusaurus Skill
Instructions
Scaffold the site
- Ensure Node >=18 is installed
- Run
npx create-docusaurus@latest physical-ai-book classicin project root or/docs - Configure
docusaurus.config.jswith site metadata, GitHub Pages URLs, i18n (en default)
Structure content
- Build
sidebars.jsfor Quarter overview, Modules 1-4, Capstone, Assessments, Hardware kits, Cloud option - Create MDX stubs per module/week with learning outcomes and tasks
- Add capstone outline
- Build
Authoring affordances
- Add MDX components for callouts, checklists, hardware tables, code blocks
- Enable search (Algolia DocSearch placeholder) and local search plugin for dev
Deploy
- Add GitHub Actions workflow for GH Pages (
npm ci,npm run build,npm run deploy) - Document Vercel deploy steps (import repo, build command
npm run build, outputbuild)
- Add GitHub Actions workflow for GH Pages (
RAG-readiness
- Enforce frontmatter fields:
title,description,module,week,tags - Keep headings semantic (h2 for weeks, h3 for sections)
- Avoid heavy client-side rendering for core text
- Export ingestion guidance: markdown path glob, ignore build/static
- Enforce frontmatter fields:
Examples
# Create new chapter
mkdir -p docs/module-1/week-1
cat > docs/module-1/week-1/intro.mdx << 'EOF'
---
title: Introduction to Physical AI
description: Overview of embodied intelligence and humanoid robotics
module: 1
week: 1
tags: [physical-ai, robotics, introduction]
---
# Introduction to Physical AI
Content here...
EOF
# Build and test locally
npm run build
npm run serve
Definition of Done
npm run buildpasses; site renders outline and sample content- Sidebar matches course hierarchy; links valid
- GH Pages workflow present; deploy instructions written
- Content annotated with frontmatter and semantic headings suitable for chunking