Concept List for Learning Graph

Course: Using Claude Skills to Create Intelligent Textbooks Total Concepts: 200 Generated: 2025-11-08

Instructions for Review

Please review this list and: - Add any missing concepts that are important to the course - Remove any concepts that are out of scope - Ensure each concept is clear and appropriately scoped - Verify concepts are in Title Case with max 32 characters - Confirm concepts build a logical learning progression

Concept Labels

1. Artificial Intelligence
2. Claude AI
3. Large Language Models Overview
4. Anthropic Claude Pro Account
5. Claude Code Interface
6. Claude Skill
7. Skill Definition File Structure
8. YAML Frontmatter in Skills
9. Skill Name and Description
10. Skill License Information
11. Allowed Tools in Skills
12. Skill Workflow Instructions
13. Installing a Claude Skill
14. Listing Available Skills
15. Invoking Skills with Slash Commands
16. Skill Execution Context
17. Claude Command
18. Command Definition Files
19. Installing Claude Commands
20. Difference Between Skills & Commands
21. Skill Directory Structure
22. Supporting Assets in Skills
23. Python Scripts in Skills
24. Template Files in Skills
25. Reference Documentation in Skills
26. Intelligent Textbook
27. Five Levels of Textbook Intelligence
28. Level 1: Static Content
29. Level 2: Hyperlinked Navigation
30. Level 3: Interactive Elements
31. Level 4: Adaptive Content
32. Level 5: AI Personalization
33. MkDocs
34. MkDocs Material Theme
35. MkDocs Configuration File
36. Navigation Structure in MkDocs
37. Markdown Formatting Basics
38. Admonitions in MkDocs
39. Learning Graph
40. Concept Nodes in Learning Graphs
41. Dependency Edges in Learning Graphs
42. Directed Acyclic Graph (DAG)
43. Prerequisite Relationships
44. Concept Dependencies
45. Learning Pathways
46. Course Description
47. Target Audience Definition
48. Course Prerequisites
49. Main Topics Covered
50. Topics Excluded from Course
51. Learning Outcomes
52. Bloom's Taxonomy
53. Bloom's 2001 Revision
54. Remember (Cognitive Level 1)
55. Understand (Cognitive Level 2)
56. Apply (Cognitive Level 3)
57. Analyze (Cognitive Level 4)
58. Evaluate (Cognitive Level 5)
59. Create (Cognitive Level 6)
60. Action Verbs for Learning Outcomes
61. Course Description Quality Score
62. Assessing Course Descriptions
63. Concept Enumeration Process
64. Generating 200 Concepts
65. Concept Label Requirements
66. Title Case Convention
67. Maximum Character Length
68. Concept Granularity
69. Atomic Concepts
70. Dependency Mapping Process
71. CSV File Format for Graphs
72. Pipe-Delimited Dependencies
73. ConceptID Field
74. ConceptLabel Field
75. Dependencies Field
76. Foundational Concepts
77. Prerequisite Concepts
78. Advanced Concepts
79. Circular Dependency Detection
80. DAG Validation
81. Self-Dependency Checking
82. Quality Metrics for Graphs
83. Orphaned Nodes
84. Disconnected Subgraphs
85. Linear Chain Detection
86. Indegree Analysis
87. Outdegree Analysis
88. Average Dependencies Per Concept
89. Maximum Dependency Chain Length
90. Learning Graph Quality Score
91. Taxonomy
92. Concept Categorization
93. Taxonomy Categories
94. TaxonomyID Abbreviations
95. Category Distribution
96. Avoiding Over-Representation
97. TaxonomyID Field in CSV
98. Adding Taxonomy to Graph
99. vis-network JSON Format
100. JSON Schema for Learning Graphs
101. Metadata Section in JSON
102. Groups Section in JSON
103. Nodes Section in JSON
104. Edges Section in JSON
105. Dublin Core Metadata
106. Title Metadata Field
107. Description Metadata Field
108. Creator Metadata Field
109. Date Metadata Field
110. Version Metadata Field
111. Format Metadata Field
112. License Metadata Field
113. Color Coding in Visualizations
114. Font Colors for Readability
115. Glossary
116. ISO 11179 Standards
117. Precise Definitions
118. Concise Definitions
119. Distinct Definitions
120. Non-Circular Definitions
121. Definitions Without Business Rules
122. Glossary Generation Process
123. FAQ
124. FAQ Generation Process
125. Common Student Questions
126. FAQ from Course Content
127. MicroSim
128. p5.js JavaScript Library
129. Interactive Simulations
130. MicroSim Directory Structure
131. main.html in MicroSims
132. index.md for MicroSim Docs
133. Iframe Embedding
134. Seeded Randomness
135. Interactive Controls (Sliders)
136. Interactive Controls (Buttons)
137. MicroSim Metadata
138. Educational Simulation Design
139. Quiz
140. Multiple-Choice Questions
141. Quiz Alignment with Concepts
142. Bloom's Taxonomy in Quizzes
143. Quiz Distribution Across Levels
144. Assessing Student Understanding
145. Chapter Structure
146. Section Organization
147. Content Generation Process
148. Chapter Index Files
149. Chapter Concept Lists
150. Reading Level Appropriateness
151. Worked Examples in Content
152. Practice Exercises
153. Git
154. Version Control Basics
155. Git Repository Structure
156. Git Status Command
157. Git Add Command
158. Git Commit Command
159. Git Push Command
160. GitHub Integration
161. GitHub Pages Deployment
162. Visual Studio Code
163. VS Code for Content Development
164. Terminal in VS Code
165. Bash
166. Shell Scripts
167. Script Execution Permissions
168. Python
169. Python Scripts for Processing
170. pip Package Management
171. Installing Python Packages
172. analyze-graph.py Script
173. csv-to-json.py Script
174. add-taxonomy.py Script
175. taxonomy-distribution.py Script
176. Prompt Engineering
177. Prompt Design Principles
178. Educational Content Prompts
179. Iterative Prompt Refinement
180. Claude Token Limits
181. Token Management Strategies
182. 4-Hour Usage Windows
183. Claude Pro Limitations
184. Optimizing Claude Usage
185. Skill Testing and Debugging
186. Error Analysis in Skills
187. Improving Skill Quality
188. Security in Skill Execution
189. Permission Management
190. File Access Permissions
191. Command-Line Interface Basics
192. Terminal Commands
193. Directory Navigation
194. File Creation and Editing
195. Symlink Creation
196. Installing Skills Globally
197. Project-Specific Skills
198. Skill Distribution Methods
199. Skill Packaging Best Practices
200. Capstone: Complete Textbook Project