Intelligent Textbook Creation Workshop

Intelligent Textbook Creation Workshop

Duration: 2 hours

!!! note "Update" We are hosting one at The Thinking Spot on Monday Dec. 1st 2025 from 6:30 pm to 8:30 pm. Please let Dan know if you can attend.

Cheat Sheet (PDF)

Prerequisites

• Claude Code Account: - All users must have the $20/month Claude Code Pro account. We do not use ChatGPT. Visitors are welcome to watch others. However, to leave the workshop with your own working textbook, Claude Code is required.

• Background: Users should know how to use a computer keyboard (including copy and paste), the computer mouse and have basic familiarity with tools like GitHub, markdown and command-line tools in the Terminal or Shell.

• Course Description: Please come with a detailed course description prepared and ask your AI to classify learning objectives using the 2001 Bloom Taxonomy.

• GitHub Account: Users should have created and tested a GitHub account before they arrive at class.

• Claude Code Skills Installed: Please make sure that Claude Code installed with all skills loaded and usage visible. Test the installation like this:

  > Hi Claude! What skills do you know about?

This should return a list of about 30 Skills

• Claude Code Account Monitoring: Users should know how to check and extend their own token usage: https://claude.ai/settings/usage

!!! Warning You must have administrative rights on your computer. Computers managed by an institution must have their institution install a developer environment and test the Claude Code Skills before those computers can be used. It is critical that the gaols of the institution are aligned with the generation of free open-source textbooks before institutional resources are used in this course.

Workshop Goal

By the end of this workshop, participants will understand the steps to create an intelligent textbook using Claude Code Skills. Students will learn the steps in the creation of intelligent textbooks and the MicroSims within them.

Workshop Outline

Step 1: Introduction & Setup

1.1 Welcome & Overview

• What are intelligent textbooks? (5 levels of intelligence)
• What is Claude Code? How is it different that just using the web-based Claude or ChatGPT?
• Can I use Cursor or Windsurf
• Why use Claude Code Skills for textbook creation?
• Workshop structure and expected outcomes

1.2 Environment Setup

Our book building tools depend on Claude Code Skills and the mkdocs build system. The following shows how these tools also depend on other systems.

• Verify Claude Code installation
• List available skills with /skills command
• Review course description format
• Clone starter template or create new MkDocs project

Hands-on: Each participant runs ./scripts/bk-list-skills and verifies their course description file exists.

Step 2: Course Description

Course Description Quality

• Components of a quality course description
• Use Bloom's 2001 Taxonomy to list the learning objectives of the course
• Create precise definitions of terms you plan to use in the course

Demo: Use /skill course-description-analyzer on sample course description. The goal is to get your course description above 85 of 100 points before you go to the next step (learning graph generation)

Hands-on: Participants analyze their own course descriptions and refine based on feedback

Step 3: Learning Graph Generation

• What is a learning graph? (concepts + dependencies)
• DAG (Directed Acyclic Graph) constraints
• No circular links (bk-check-loops)
• Taxonomy categorization (12 categories)
• Quality metrics interpretation

Demo:

1. Generate learning graph with /skill learning-graph-generator
2. Review generated files in docs/learning-graph/:
   • learning-graph.csv (concept list with dependencies)
   • quality-metrics.md (validation report)
   • learning-graph.json (vis-network visualization data)

Hands-on: Each participant generates their learning graph and reviews quality metrics

Step 4: View and Edit Your Learning Graph

> run the install-learning-graph-viewer skill

Note that you may need to redo the legend for color and ordering of the taxonomy.

Step 5: Generate Book Structure

> run the book-chapter-generator skill

After this step there will be a docs/chapters directory with one directory for each chapter. The index.md file has a overview of each chapter and a list of the concepts that must be covered in the chapter

Discussion: How were concepts distributed across chapters? Does the ordering make pedagogical sense?

!!! tip By looking at the shell output you can see tradeoffs of breaking different concepts into balanced chapters. You can also try log this session to logs/book-chapter-generator.md

Step 6: Generate Content

• Concept-to-chapter mapping
• Respecting dependency order
• Balancing chapter length and complexity

run the chapter-content-generator on chapter 1 @docs/chapters/01-*/index.md

Repeat this for several chapters

Review together:

• Markdown structure and formatting
• Admonitions and callouts
• Breaking up the "Wall of Text" problem
• Insertion of non-pure-text items (lists, tables, MicroSims)

The remainder of this class is "Supplementary Materials"

Step 7: Glossary Creation

• ISO 11179 definition standards (precise, concise, distinct, non-circular)
• Automatic glossary generation from concept list and terms in chapters
• Suggest terms that might have been missed

Demo: Use run the glossary-generator skill to create docs/glossary.md

Hands-on: Participants generate glossaries and review 3-5 definitions for quality

Demo: Generate content for one chapter with /skill chapter-content-generator

Hands-on: Participants generate content for their first chapter

Step 8: FAQ Generation

• The default generate about 70 FAQs but you can ask for fewer or more
• Use for helping chatbots get started
• Used to map multiple intents into standardized responses

Step 9: Quiz Generation (5 min)

• Bloom's Taxonomy-aligned questions
• Concept mapping to learning graph
• Interactive quiz format

Demo: Use /skill quiz-generator for a sample chapter

Hands-on: Participants generate quiz for their first chapter

Step 10 Interactive Elements - MicroSims

Introduction to MicroSims

• What are MicroSims? (interactive p5.js simulations)
• MicroSim directory structure (docs/sims/[sim-name]/)
• Designed for reuse and placement in any chapter or any website
• Use iframe to insert into a chapter
• Educational value of interactivity
• There are over 10 types of skills that know how to create MicroSims

p5.js MicroSim Creation

• Two-region pattern (drawing canvas + controls)
• Seeded randomness for reproducibility
• Iframe embedding in chapter content

Demo: Create a simple MicroSim with /skill microsim-p5

• Example: Visualizing slope and y-intercept changes in linear equations
• Show main.html structure
• Show index.md documentation with iframe

4.3 Alternative Visualization Skills (10 min)

• Chart.js - data visualizations (bar, line, pie charts)
• Mermaid - flowcharts and process diagrams
• Timeline - chronological event sequences
• Venn diagrams - set relationships
• Maps - geographic visualizations

Demo: Quick examples of 2-3 different visualization types

Discussion: Which visualization types best suit different subject areas?

Part 5: Quality Assurance & Deployment (10 minutes)

5.1 Metrics & Validation (5 min)

• Book metrics generation (word counts, concept coverage)
• Chapter-level metrics
• Quality score interpretation

Demo: Use /skill book-metrics-generator to create comprehensive metrics report

5.2 Site Building & Deployment (5 min)

• MkDocs build process
• Local preview with mkdocs serve
• GitHub Pages deployment with mkdocs gh-deploy

Demo: Build and preview site locally

Hands-on: Participants preview their textbook site at http://localhost:8000

Part 6: Wrap-up & Next Steps (10 minutes)

6.1 Complete Workflow Review (5 min)

The 12-step intelligent textbook workflow:

1. Course Description Development
2. Bloom's Taxonomy Integration
3. Concept Enumeration (200 concepts)
4. Concept Dependencies (DAG)
5. Concept Taxonomy Categorization
6. Learning Graph Visualization
7. Chapter/Section Structure
8. Chapter Content Generation
9. MicroSim Creation
10. Glossary & FAQ Generation
11. Quality Assurance (metrics)
12. Site Deployment

6.2 Advanced Topics & Resources (3 min)

• FAQ generation from course content
• Reference list generation
• Custom skill creation
• Contributing to the skills repository

6.3 Q&A and Troubleshooting (2 min)

• Common issues and solutions
• Where to get help (GitHub issues, documentation)

Workshop Materials Checklist

Before the workshop:

• Sample course descriptions (3-4 different subjects)
• Claude Code installed on all machines
• Skills repository cloned and installed
• MkDocs and Material theme installed
• Python environment with required packages

Handouts:

• Quick reference card with all skill commands
• Bloom's Taxonomy cognitive levels chart
• ISO 11179 definition standards checklist
• Troubleshooting guide

Sample Projects:

• Complete example textbook (e.g., "Introduction to Programming")
• Partially completed textbook for hands-on practice
• Template course-description.md files

Post-Workshop Follow-up

Immediate next steps for participants:

1. Complete chapter generation for remaining chapters
2. Generate FAQs with /skill faq-generator
3. Create 5-10 MicroSims for key concepts
4. Run quality metrics and address gaps
5. Deploy to GitHub Pages

Extended learning:

• Join the Claude skills community
• Contribute new skills or improvements
• Share completed textbooks for peer review
• Explore Level 4+ intelligence features (adaptive learning)

Facilitator Notes

Time Management:

• Parts 1-2 must stay on schedule (foundation is critical)
• Part 3 can flex ±5 minutes based on group needs
• Part 4 is most likely to run over - have backup time
• Part 6 can be shortened if needed

Common Issues:

• Learning graph generation may fail if course description lacks detail
• Quality scores <70 require iteration on concept enumeration
• Circular dependencies in graphs require manual CSV editing
• MicroSim generation requires clear concept specifications

Engagement Strategies:

• Pair programming during hands-on sections
• Share screens to show different subject area examples
• Use chat for questions during demos
• Create shared document for troubleshooting tips

Success Metrics:

• 80%+ participants generate a learning graph
• 60%+ participants generate at least one chapter
• 100% participants can preview their site locally
• Post-workshop survey shows confidence in using skills independently