# FAQ Coverage Gaps

> 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.

- Skill: `tools-only/faq-coverage-gaps` (Agent Skill, multi-file: 3 files)
- Install (CLI): `npx skillmds@latest add tools-only/faq-coverage-gaps`
- Raw SKILL.md: https://api.skillmd.com/api/skills/tools-only/faq-coverage-gaps/raw
- Safety review: pending (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: tools-only (https://skillmd.com/u/tools-only)
- Updated: 2026-09-29
- Page: https://skillmd.com/skills/tools-only/faq-coverage-gaps

---

# 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%:

1. Add questions for Concepts 1, 2, 3 (AI Fundamentals)
2. Add questions for Concepts 176, 177, 178, 179 (Prompt Engineering)
3. Add questions for Concepts 122, 124, 140 (Resource Generation)
4. 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**:

1. Taxonomy concepts (93, 94, 96, 98)
2. Quiz design concepts (141, 142)
3. 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:**

1. **Phase 1 (Immediate):** Add 11 critical gap questions → 69% coverage
2. **Phase 2 (Short-term):** Add 10 medium priority questions → 74% coverage
3. **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:

1. **AI Fundamentals** (prompt engineering, LLMs, AI basics)
2. **Resource Generation** (quiz and glossary skill details)
3. **Learning Graph Processes** (specific workflows for generation)
4. **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.