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Chapters

This textbook is organized into 31 chapters covering all 570 concepts in the learning graph, plus four practical appendices.

Chapter Overview

  1. Foundations of AI, Language Models, and Prompting — Introduces artificial intelligence, large language models, tokens, and context windows, then covers prompting and prompt engineering as the interface between a person and a model.
  2. AI Coding Agents and the Five Levels of Textbook Intelligence — Covers AI coding agents, agentic workflows, tool use, and the human-in-the-loop pattern that keeps a person in control of an agent's actions.
  3. Python Fundamentals for Skill Automation — Introduces the Python building blocks used across the skill library: the standard library, pip and virtual environments, JSON and CSV parsing, regular expressions, and command-line arguments.
  4. Development Tools: Editor, Terminal, and Git Basics — Sets up the core development environment: Visual Studio Code and its integrated terminal, Git repository structure, and the first commands used to track changes.
  5. MkDocs Site Features, Deployment, and Analytics — Expands the MkDocs Material feature set and walks through the Git commit, push, and branch workflow to a deployed site.
  6. Agent Skill Fundamentals — Defines what an Agent Skill is and how it differs from a prompt, then unpacks the SKILL.md frontmatter contract field by field.
  7. Progressive Disclosure and Meta-Skill Routing — Explains progressive disclosure and its three loading budgets, along with how trigger matching and meta-skills keep a library under the loading limit.
  8. Token Budgets and Usage Limits — Treats tokens as an engineering budget: the token cost model, plan limits and usage windows, and serial versus parallel execution.
  9. Measuring and Optimizing Token Usage — Covers file-layout token strategies, usage-tracking hooks, JSONL logs, and dashboards for token consumption.
  10. Building and Testing Portable Skills — Covers permission management, writing a trigger-reliable skill description, and testing skill portability across AI platforms.
  11. Distributing Skills and Building Commands — Covers packaging and installing skills, plus creating Claude commands and ordered runbooks.
  12. Writing a Course Description — Walks through every required element of a course description and introduces Bloom's Taxonomy as the outcomes framework.
  13. Bloom's Taxonomy and Instructional Design — Details all six Bloom's cognitive levels and the instructional-design conventions used to write chapter content.
  14. Learning Graphs and Concept Enumeration — Defines what a learning graph is and covers concept enumeration and dependency mapping.
  15. Learning Graph Data Formats and Taxonomy — Covers the CSV and vis-network JSON formats that encode a learning graph, and taxonomy categorization.
  16. Learning Graph Quality Validation — Covers the quality checks a learning graph must pass and the interactive graph viewer.
  17. Chapter Structure and Content Elements — Introduces the book-chapter-generator workflow and the markdown content elements available inside a chapter.
  18. Chapter Content Quality and Review — Covers section organization, diagram specification blocks, and the chapter review workflow.
  19. FAQs and Curated References — Covers generating a FAQ set and building a curated reference list that credits pedagogical authors.
  20. Glossaries and Quizzes — Covers writing ISO 11179-compliant glossary definitions and generating Bloom's-aligned quizzes.
  21. MicroSim Anatomy and p5.js Basics — Introduces the standard MicroSim directory structure and builds a first interactive simulation with p5.js.
  22. p5.js Controls and MicroSim Quality — Covers the built-in p5.js control widgets and the standardization and quality-scoring checks every MicroSim must pass.
  23. The MicroSim Generator and Metadata Schema — Explains how the MicroSim generator routes a request to a visualization library and documents it in metadata.
  24. Visualization Libraries and Systems Diagrams — Surveys the MicroSim generator's other visualization families, including causal loop diagrams and batch scripts.
  25. Text-to-Image Models and the Verified Infographic Pipeline — Explains why one-shot infographic generation fabricates facts and introduces the eight-phase verified pipeline.
  26. Interactive Infographic Overlays — Covers the annotation-free illustration technique paired with a JavaScript overlay layer and its audit trail.
  27. Slide Decks, Stories, and Audio Media — Covers MARP decks, PowerPoint lectures, illustrated stories, freely-licensed image sourcing, and text-to-speech narration.
  28. Domain-Specific Skill Extension: Electronics Case Study — Walks through extending the skill library into a new domain using a beginning-electronics case study.
  29. Book Installer Features — Covers the book-installer meta-skill's feature system, from scaffolding a new textbook to installable extras.
  30. Session Logs and Book Metrics — Covers writing session logs and design decision records, and the canonical book-metrics.json hub.
  31. Publishing and Announcing a Finished Book — Covers generating a README, LinkedIn posts, and a press release, closing with the book's capstone launch checklist.

Appendices

Practical setup and workflow guides that stand outside the concept-dependency sequence above:

How to Use This Textbook

Work through the chapters in order — each one lists the earlier chapters its concepts depend on, so prerequisites are always covered before they are needed. The appendices can be read at any time; they support specific platforms and workflows rather than building on the concept graph.

Note: Each chapter includes a list of concepts covered. Make sure to complete prerequisites before moving to advanced chapters.