tools-only FAQ Coverage Gaps
Concepts from the learning graph not covered in the FAQ.
Summary
- Total Concepts in Learning Graph: 200
- Concepts Covered in FAQ: 128 (64%)
- Concepts Not Covered: 72 (36%)
Critical Gaps (High Priority)
These are high-centrality concepts with many dependencies that are not adequately covered in the FAQ. Adding questions about these concepts would significantly improve FAQ utility.
1. Large Language Models Overview (Concept 3)
- Centrality: High (foundation for understanding Claude AI)
- Dependencies: Artificial Intelligence (1)
- Category: AI Fundamentals
- Suggested Question: "What are Large Language Models and how do they enable intelligent textbook creation?"
- Rationale: Essential background for understanding how Claude Skills work
2. Prompt Engineering (Concept 176)
- Centrality: High (core skill for effective AI usage)
- Dependencies: Artificial Intelligence (1)
- Category: AI Fundamentals
- Suggested Question: "What is prompt engineering and why is it important for creating textbooks?"
- Rationale: Key skill users need to master for customizing skill outputs
3. Generating 200 Concepts (Concept 64)
- Centrality: High (central process in learning graph generation)
- Dependencies: Concept Enumeration Process (63)
- Category: Learning Graphs
- Suggested Question: "How do I generate exactly 200 concepts for my learning graph?"
- Rationale: Specific technical guidance on a core process
4. Dependency Mapping Process (Concept 70)
- Centrality: High (core learning graph activity)
- Dependencies: Concept Dependencies (44)
- Category: Learning Graphs
- Suggested Question: "What is the dependency mapping process and how do I do it effectively?"
- Rationale: Critical process that determines learning sequence
5. Glossary Generation Process (Concept 122)
- Centrality: High (automated content generation)
- Dependencies: Glossary (115), ISO 11179 Standards (116)
- Category: Resources
- Suggested Question: "How does the glossary-generator skill work and when should I use it?"
- Rationale: Key workflow step for maintaining consistent terminology
6. FAQ Generation Process (Concept 124)
- Centrality: High (this very skill!)
- Dependencies: FAQ (123)
- Category: Resources
- Suggested Question: "What is the FAQ generation process and how was this FAQ created?"
- Rationale: Meta-question with high relevance to current document
7. Quiz Generation Process (Concept 140)
- Centrality: High (assessment automation)
- Dependencies: Quiz (139)
- Category: Resources
- Suggested Question: "How do I use the quiz-generator skill to create assessments?"
- Rationale: Important assessment tool not covered
8. Course Description Quality Score (Concept 61)
- Centrality: Medium-High (quality validation)
- Dependencies: Course Description (46)
- Category: Educational Theory
- Suggested Question: "How is the course description quality score calculated?"
- Rationale: Important quality metric for initial workflow step
9. Taxonomy Distribution (Concept 95)
- Centrality: Medium-High (balance metric)
- Dependencies: Category Distribution (92)
- Category: Data Structures
- Suggested Question: "How do I interpret taxonomy distribution reports?"
- Rationale: Practical guidance on quality assessment
10. Prompt Design Principles (Concept 177)
- Centrality: High (advanced skill usage)
- Dependencies: Prompt Engineering (176)
- Category: AI Fundamentals
- Suggested Question: "What are the key principles of prompt design for educational content?"
- Rationale: Advanced technique for customizing outputs
11. Artificial Intelligence (Concept 1)
- Centrality: Highest (foundational concept)
- Dependencies: None
- Category: AI Fundamentals
- Suggested Question: "What is artificial intelligence in the context of educational content creation?"
- Rationale: Foundation for understanding all AI-powered tools
12. Claude AI (Concept 2)
- Centrality: Highest (core technology)
- Dependencies: Artificial Intelligence (1)
- Category: AI Fundamentals
- Suggested Question: "What is Claude AI and how does it differ from other language models?"
- Rationale: Understanding the underlying technology
13. Multiple-Choice Questions (Concept 140)
- Centrality: Medium (assessment type)
- Dependencies: Quiz (139)
- Category: Resources
- Suggested Question: "How do I create effective multiple-choice questions for my textbook?"
- Rationale: Practical assessment design guidance
14. Quiz Alignment with Concepts (Concept 141)
- Centrality: Medium (quiz quality)
- Dependencies: Quiz (139), Concept Nodes (40)
- Category: Resources
- Suggested Question: "How do I ensure my quiz questions align with learning graph concepts?"
- Rationale: Quality assurance for assessments
15. Bloom's Taxonomy in Quizzes (Concept 142)
- Centrality: Medium (quiz design)
- Dependencies: Quiz (139), Bloom's Taxonomy (52)
- Category: Resources
- Suggested Question: "How do I distribute quiz questions across Bloom's Taxonomy levels?"
- Rationale: Practical quiz design guidance
16. Educational Content Prompts (Concept 178)
- Centrality: Medium-High (skill customization)
- Dependencies: Prompt Design Principles (177), Intelligent Textbook (26)
- Category: AI Fundamentals
- Suggested Question: "How do I write effective prompts for generating educational content?"
- Rationale: Advanced customization technique
17. Token Management Strategies (Concept 181)
- Centrality: Medium (partially covered in FAQ)
- Dependencies: Claude Token Limits (180)
- Category: AI Fundamentals
- Suggested Question: "What are advanced token management strategies for large projects?"
- Rationale: Optimize usage for cost and efficiency
18. Iterative Prompt Refinement (Concept 179)
- Centrality: Medium (skill improvement)
- Dependencies: Prompt Design Principles (177)
- Category: AI Fundamentals
- Suggested Question: "How do I iteratively refine prompts to improve content quality?"
- Rationale: Advanced technique for quality improvement
Medium Priority Gaps
These concepts have moderate centrality and would improve FAQ completeness:
Taxonomy & Categorization (7 concepts)
19. Taxonomy Categories (Concept 93)
- Suggested Question: "What are the main taxonomy categories used in learning graphs?"
- Category: Data Structures
20. TaxonomyID Abbreviations (Concept 94)
- Suggested Question: "How do I choose TaxonomyID abbreviations for my concepts?"
- Category: Data Structures
21. Category Distribution (Concept 95) - Already in critical gaps 22. Avoiding Over-Representation (Concept 96)
- Suggested Question: "How do I avoid over-representing certain taxonomy categories?"
- Category: Best Practice
23. Adding Taxonomy to Graph (Concept 98)
- Suggested Question: "How do I add taxonomy categorization to my learning graph?"
- Category: Technical Detail
24. Concept Categorization (Concept 92)
- Suggested Question: "What is concept categorization and why is it important?"
- Category: Learning Graphs
Git & Version Control (8 concepts)
25. Git Repository Structure (Concept 155)
- Suggested Question: "How should I structure my Git repository for a textbook project?"
- Category: Best Practice
26. Git Status Command (Concept 156) - Partially covered 27. Git Add Command (Concept 157) - Partially covered 28. Git Commit Command (Concept 158) - Partially covered 29. Git Push Command (Concept 159) - Partially covered
30. GitHub Integration (Concept 160) - Covered 31. GitHub Pages Deployment (Concept 161) - Covered
32. Version Control Basics (Concept 154) - Covered
Graph Quality Metrics (6 concepts)
33. Indegree Analysis (Concept 86)
- Suggested Question: "What is indegree analysis and how do I interpret it?"
- Category: Technical Detail
34. Outdegree Analysis (Concept 87)
- Suggested Question: "What is outdegree analysis in learning graphs?"
- Category: Technical Detail
35. Average Dependencies Per Concept (Concept 88) - Covered 36. Maximum Dependency Chain Length (Concept 89)
- Suggested Question: "What is maximum dependency chain length and why does it matter?"
- Category: Technical Detail
37. Linear Chain Detection (Concept 85)
- Suggested Question: "What are linear chains in learning graphs and should I avoid them?"
- Category: Common Challenges
38. Disconnected Subgraphs (Concept 84) - Covered as orphaned nodes
Concept Refinement (5 concepts)
39. Concept Granularity (Concept 68) - Partially covered 40. Atomic Concepts (Concept 69) - Covered
41. Concept Label Requirements (Concept 65)
- Suggested Question: "What are the requirements for concept labels?"
- Category: Technical Detail
42. Title Case Convention (Concept 66)
- Suggested Question: "Why do concept labels use Title Case?"
- Category: Technical Detail
43. Maximum Character Length (Concept 67)
- Suggested Question: "What is the maximum character length for concept labels and why?"
- Category: Technical Detail
Chapter & Content Generation (4 concepts)
44. Chapter Index Files (Concept 148)
- Suggested Question: "What are chapter index files and how should I structure them?"
- Category: Technical Detail
45. Chapter Concept Lists (Concept 149)
- Suggested Question: "How do I determine which concepts belong in each chapter?"
- Category: Best Practice
46. Reading Level Appropriateness (Concept 150)
- Suggested Question: "How do I ensure my content is at the appropriate reading level?"
- Category: Best Practice
47. Practice Exercises (Concept 152)
- Suggested Question: "How do I create effective practice exercises for my textbook?"
- Category: Best Practice
Low Priority Gaps
These are leaf nodes, very specialized concepts, or concepts with low centrality that would have limited FAQ utility:
Specific Technical Details (24 concepts)
Foundational Concepts (3 concepts):
- Concept 76: Foundational Concepts (covered conceptually)
- Concept 77: Prerequisite Concepts (covered conceptually)
- Concept 78: Advanced Concepts (covered conceptually)
Metadata Fields (11 concepts):
- Concepts 101-114: Specific JSON and metadata fields (too granular for FAQ)
Visual Design (2 concepts):
- Concept 113: Color Coding in Visualizations
- Concept 114: Font Colors for Readability
File System (2 concepts):
- Concept 190: File Access Permissions (covered in permissions question)
- Concept 194: File Creation and Editing
Terminal Commands (3 concepts):
- Concept 192: Terminal Commands (covered generally)
- Concept 193: Directory Navigation
- Concept 195: Symlink Creation (covered in installation)
Skill Specifics (3 concepts):
- Concept 196: Installing Skills Globally (covered)
- Concept 197: Project-Specific Skills (covered)
- Concept 198: Skill Distribution Methods (covered in advanced topics)
Recommendations
Immediate Actions (Add 10-12 Questions)
Focus on Critical Gaps to improve coverage from 64% to 75%:
- Add questions for Concepts 1, 2, 3 (AI Fundamentals)
- Add questions for Concepts 176, 177, 178, 179 (Prompt Engineering)
- Add questions for Concepts 122, 124, 140 (Resource Generation)
- Add questions for Concepts 64, 70 (Learning Graph Processes)
These 11 additions would cover the highest-priority gaps and significantly improve FAQ utility.
Short-Term Actions (Add 8-10 More Questions)
Address remaining Critical Gaps and top Medium Priority Gaps:
- Taxonomy concepts (93, 94, 96, 98)
- Quiz design concepts (141, 142)
- Graph quality metrics (86, 87, 89)
Long-Term Maintenance
- Review quarterly: Check for new concepts added to learning graph
- User feedback: Track which topics generate support requests
- Analytics: If chatbot JSON is deployed, monitor which questions get asked
- Update after course changes: Revise FAQ when course content or structure changes
Coverage Improvement Strategy
Target Coverage: 75-80% (150-160 concepts of 200)
Current Coverage: 64% (128 concepts)
Gap to Target: 22-32 additional concepts
Recommended Approach:
- Phase 1 (Immediate): Add 11 critical gap questions → 69% coverage
- Phase 2 (Short-term): Add 10 medium priority questions → 74% coverage
- Phase 3 (Long-term): Add 6-11 selective low priority questions → 77-80% coverage
Rationale for 75-80% target:
- Covers all high and medium centrality concepts
- Leaves very specialized/technical details for documentation
- Maintains FAQ usability (too many questions reduces discoverability)
- Focuses on user needs rather than exhaustive coverage
Conclusion
The current FAQ provides solid coverage (64%) of core concepts with excellent depth in Claude Skills, learning graphs, MkDocs, and educational theory. The main gaps are in:
- AI Fundamentals (prompt engineering, LLMs, AI basics)
- Resource Generation (quiz and glossary skill details)
- Learning Graph Processes (specific workflows for generation)
- Advanced Techniques (prompt refinement, optimization)
Adding 11 questions addressing critical gaps would bring coverage to ~69%, providing comprehensive support for most user needs. Further additions targeting medium priority gaps could achieve 75-80% coverage, representing excellent FAQ completeness for a professional development course.