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Course Description Analyzer

Prompt

run the course-description-analyzer skill

⏺ I'll run the course-description-analyzer skill to analyze your course description.

The "course-description-analyzer" skill is running

The "course-description-analyzer" skill is running

⏺ I'll analyze your course description. Let me first read the file.

⏺ Read(docs/course-description.md) ⎿  Read 206 lines

⏺ Excellent! I'll now analyze your course description against the quality criteria.

Course Description Analysis Report

Overall Score: 100/100

Quality Rating: Excellent - Ready for learning graph generation

Detailed Scoring Breakdown

Element Points Earned Max Points Status
Title 5 5 ✓ Complete
Target Audience 5 5 ✓ Complete
Prerequisites 5 5 ✓ Complete
Main Topics Covered 10 10 ✓ Excellent
Topics Excluded 5 5 ✓ Complete
Learning Outcomes Header 5 5 ✓ Complete
Remember Level 10 10 ✓ Excellent
Understand Level 10 10 ✓ Excellent
Apply Level 10 10 ✓ Excellent
Analyze Level 10 10 ✓ Excellent
Evaluate Level 10 10 ✓ Excellent
Create Level 10 10 ✓ Excellent
Descriptive Context 5 5 ✓ Excellent

Strengths

  1. Comprehensive Topic Coverage (90+ topics)
  2. Exceptional breadth covering fundamentals (nodes, edges, properties) to advanced concepts (fraud detection, AI/LLM integration)
  3. Well-organized progression from basic graph concepts to complex healthcare applications

  4. Includes coding standards (ICD, CPT, HCPCS), business models (fee-for-service, value-based care), and modern technologies (vector stores, embeddings)

  5. Multiple Perspectives

  6. Patient-centric modeling
  7. Provider viewpoint (hospitals, clinics, schedules, revenue)
  8. Payer perspective (claims, fraud, policies, networks)

  9. This multi-perspective approach significantly enriches concept diversity

  10. Complete Bloom's Taxonomy Coverage

  11. All six cognitive levels well-represented with 5 specific, measurable outcomes each
  12. Appropriate action verbs used throughout (define, explain, construct, analyze, critique, design)

  13. Progressive complexity from remembering to creating

  14. Strong Capstone Integration

  15. Four distinct capstone project options provided
  16. Clear deliverables including presentation and demonstration requirements

  17. Emphasis on explainability and measurable impact

  18. Rich Context

  19. Compelling rationale connecting healthcare costs to graph database solutions
  20. Clear explanation of course value and real-world applications

  21. Integration of emerging technologies (AI, LLMs, vector stores)

  22. Clear Boundaries

  23. Topics excluded section effectively sets scope
  24. Helps students understand what the course won't cover

Concept Generation Readiness Assessment

Estimated Concept Count Potential: 250-300 concepts

This course description exceeds requirements for generating 200 concepts due to:

  1. Topic Breadth: 90+ main topics provide strong foundation
  2. Depth Indicators: Each major area (patient, provider, payer) suggests 40-60 subtopics
  3. Bloom's Diversity: All six cognitive levels ensure concept variety
  4. Technical Depth: Specific technologies, algorithms, and standards mentioned
  5. Application Domains: Multiple use cases (fraud detection, clinical decision support, network optimization)

Gap Analysis

No significant gaps identified. This is a complete, well-structured course description.

Minor Enhancement Suggestions (Optional)

While your course description scores perfectly, consider these optional enhancements:

  1. Prerequisites Expansion (Optional)
    • Current: "Knowledge of databases"
    • Consider specifying: "Relational database fundamentals (SQL queries, table design) and basic programming experience (Python or similar)"
    • This helps students self-assess readiness more accurately
  2. Learning Outcomes - Remember Level (Minor refinement)
    • Consider adding: "Recall key HIPAA privacy and security principles relevant to healthcare data"
    • This would strengthen the security/compliance thread that runs through the course
  3. Topics Covered - Organization (Optional)
    • The extensive list (90+ topics) is excellent but could benefit from grouping into categories:
      • Graph Fundamentals (10 topics)
    • Healthcare Domain (30 topics)
    • Perspectives (15 topics)
    • Analytics & Applications (20 topics)
    • Advanced Topics (15 topics)
    • Current flat list works fine but categorization might improve readability

Next Steps

✅ Ready to proceed with learning graph generation

Your course description contains all necessary elements to generate a comprehensive learning graph with 200+ concepts. The combination of: - Extensive topic coverage - Multiple domain perspectives - Complete Bloom's Taxonomy outcomes - Clear application areas - Integration of modern technologies

...provides an excellent foundation for the next phase.

Recommended next action: Run the learning-graph-generator skill to create your comprehensive learning graph.

Would you like me to add the course-description.md file to your mkdocs.yml navigation? It would be placed after the about.md file.