tools-only 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:
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:
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
/docs/chapters/index.md: Table of contents/docs/chapters/NN-chapter-name/index.md: Chapter metadata files- 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
- Chapter Size: Aim for 10-20 concepts per chapter (adjust for complexity)
- Naming: Use descriptive, parallel chapter titles (all nouns or all gerunds)
- Dependencies: Always verify prerequisites are in earlier chapters
- Taxonomy: Group related taxonomy categories together when possible
- Balance: Avoid one very short or very long chapter
- Preview: Show what's coming in chapter summaries
- Review: Reference earlier concepts when introducing new ones
Example Chapter Structure
For a 200-concept course on Machine Learning:
- Introduction to Machine Learning (12 concepts)
- Foundational terminology, motivation, overview
- Mathematical Foundations (18 concepts)
- Linear algebra, calculus, probability
- Data Preprocessing (15 concepts)
- Cleaning, transformation, feature engineering
- Supervised Learning: Regression (20 concepts)
- Linear regression, polynomial, regularization
- Supervised Learning: Classification (22 concepts)
- Logistic regression, decision trees, SVM
- Unsupervised Learning (18 concepts)
- Clustering, dimensionality reduction
- Neural Networks (20 concepts)
- Perceptrons, backpropagation, activation functions
- Deep Learning (25 concepts)
- CNNs, RNNs, transformers
- Model Evaluation (16 concepts)
- Metrics, cross-validation, bias-variance
- Advanced Topics and Applications (20 concepts)
- Transfer learning, reinforcement learning, capstone