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Book Chapter Generator

Overview

The book-chapter-generator skill generates optimal chapter structures for intelligent textbooks by analyzing course descriptions, learning graphs, and concept dependencies to distribute content evenly across 6-20 chapters while respecting prerequisite relationships.

Purpose

This skill automates the design of chapter organization for educational textbooks, ensuring logical progression from foundational to advanced concepts based on the learning graph's directed acyclic graph (DAG) structure.

Key Features

  • Prerequisite-Based Organization: Chapters follow concept dependencies from the learning graph
  • Even Distribution: Balances content across 6-20 chapters (typically 10-15 for most courses)
  • Taxonomy Integration: Uses concept categories to group related topics
  • Chapter Structure: Creates /docs/chapters/ directory with numbered subdirectories
  • Chapter Metadata: Generates index.md for each chapter with title, summary, and concept list

When to Use

Use this skill after: - Learning graph has been generated (learning-graph.json exists) - Course description is finalized - Concept taxonomy has been established - Before generating chapter content

Workflow Steps

Step 1: Analyze Learning Graph

Reads the learning graph to understand: - Total number of concepts (~200) - Concept dependencies (DAG structure) - Foundational vs advanced concepts - Concept categories from taxonomy

Step 2: Determine Chapter Count

Calculates optimal number of chapters based on: - Total concepts (aim for 10-20 concepts per chapter) - Course level (junior-high: 6-10, high school: 10-15, college: 12-18, graduate: 15-20) - Natural topic boundaries - User preferences

Step 3: Group Concepts into Chapters

Organizes concepts following these principles: - Respect Dependencies: Prerequisites must come before dependent concepts - Logical Grouping: Related concepts (same taxonomy category) grouped together - Progressive Difficulty: Foundational concepts early, advanced concepts later - Balanced Distribution: Roughly equal concepts per chapter

Step 4: Create Chapter Directory Structure

Generates folder hierarchy: 

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docs/chapters/
├── index.md                    # Table of contents
├── 01-introduction/
│   └── index.md               # Chapter 1 metadata
├── 02-foundational-concepts/
│   └── index.md               # Chapter 2 metadata
├── 03-core-principles/
│   └── index.md               # Chapter 3 metadata
└── ...

Step 5: Generate Chapter Index Files

Creates index.md for each chapter containing: - Title: Descriptive chapter title (Title Case) - Summary: 2-3 sentence overview of chapter content - Concept List: Numbered list of concepts covered (from learning graph) - Prerequisites: Required prior knowledge - Learning Objectives: What students will learn

Step 6: Create Table of Contents

Generates /docs/chapters/index.md with: - Overview of the textbook structure - Numbered list of all chapters with summaries - Concept count per chapter - Estimated reading time

Step 7: Update MkDocs Navigation

Adds chapter structure to mkdocs.yml: 

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nav:
  - Chapters:
      - Overview: chapters/index.md
      - 1. Introduction: chapters/01-introduction/index.md
      - 2. Foundational Concepts: chapters/02-foundational-concepts/index.md
      - ...

Chapter Organization Patterns

Introductory Chapter (Chapter 1)

  • Welcome and motivation
  • Course overview
  • Prerequisites review
  • Key terminology introduction
  • Roadmap for learning

Foundational Chapters (2-4)

  • Basic concepts with zero or few dependencies
  • Core vocabulary
  • Fundamental principles
  • Simple examples

Intermediate Chapters (5-10)

  • Building on foundations
  • Integration of concepts
  • Real-world applications
  • More complex examples

Advanced Chapters (11+)

  • High-dependency concepts
  • Synthesis and integration
  • Advanced techniques
  • Capstone project preparation

Quality Standards

A well-structured chapter organization should have: - Clear progression from simple to complex - No concept appears before its prerequisites - Balanced chapter sizes (10-20 concepts each) - Logical topic groupings - Clear chapter titles that indicate content - Comprehensive coverage of all learning graph concepts

Output Files

  1. /docs/chapters/index.md: Table of contents
  2. /docs/chapters/NN-chapter-name/index.md: Chapter metadata files
  3. Updated mkdocs.yml: Navigation structure

Integration

This skill coordinates with: - learning-graph-generator: Uses the DAG structure and concept list - chapter-content-generator: Provides structure for content generation - glossary-generator: Concepts align with glossary terms - quiz-generator: Each chapter gets assessment aligned with concepts

Best Practices

  1. Chapter Size: Aim for 10-20 concepts per chapter (adjust for complexity)
  2. Naming: Use descriptive, parallel chapter titles (all nouns or all gerunds)
  3. Dependencies: Always verify prerequisites are in earlier chapters
  4. Taxonomy: Group related taxonomy categories together when possible
  5. Balance: Avoid one very short or very long chapter
  6. Preview: Show what's coming in chapter summaries
  7. Review: Reference earlier concepts when introducing new ones

Example Chapter Structure

For a 200-concept course on Machine Learning:

  1. Introduction to Machine Learning (12 concepts)
  2. Foundational terminology, motivation, overview
  3. Mathematical Foundations (18 concepts)
  4. Linear algebra, calculus, probability
  5. Data Preprocessing (15 concepts)
  6. Cleaning, transformation, feature engineering
  7. Supervised Learning: Regression (20 concepts)
  8. Linear regression, polynomial, regularization
  9. Supervised Learning: Classification (22 concepts)
  10. Logistic regression, decision trees, SVM
  11. Unsupervised Learning (18 concepts)
  12. Clustering, dimensionality reduction
  13. Neural Networks (20 concepts)
  14. Perceptrons, backpropagation, activation functions
  15. Deep Learning (25 concepts)
  16. CNNs, RNNs, transformers
  17. Model Evaluation (16 concepts)
  18. Metrics, cross-validation, bias-variance
  19. Advanced Topics and Applications (20 concepts)
    • Transfer learning, reinforcement learning, capstone

References