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Learning Graph Quality Metrics Report

Overview

  • Total Concepts: 570
  • Foundational Concepts (no prerequisites, other concepts depend on them): 6
  • Terminal Nodes (nothing depends on them, but have prerequisites): 219
  • Orphaned Nodes (completely disconnected, no edges): 0
  • Concepts with Dependencies: 564
  • Average Dependencies per Concept: 2.02

Graph Structure Validation

  • Valid DAG Structure: ✅ Yes
  • Self-Dependencies: None detected ✅
  • Cycles Detected: 0

Foundational Concepts

These concepts have no prerequisites:

  • 1: Artificial Intelligence
  • 12: Python
  • 13: Markdown Formatting
  • 14: Terminal Commands
  • 17: Git
  • 21: Visual Studio Code

Dependency Chain Analysis

  • Maximum Dependency Chain Length: 26

Longest Learning Path:

  1. Artificial Intelligence (ID: 1)
  2. Large Language Model (ID: 2)
  3. Prompt (ID: 6)
  4. Prompt Engineering (ID: 7)
  5. System Prompt (ID: 8)
  6. Tool Use by Agents (ID: 24)
  7. Agentic Workflow (ID: 25)
  8. Agent Skill (ID: 49)
  9. SKILL.md File (ID: 51)
  10. YAML Frontmatter (ID: 52)
  11. Skill Description Field (ID: 54)
  12. Skill Trigger Matching (ID: 69)
  13. Trigger Keyword Table (ID: 70)
  14. Meta-Skill (ID: 72)
  15. Skill Consolidation (ID: 76)
  16. Skill Library (ID: 89)
  17. Skill Packaging (ID: 103)
  18. Symbolic Link Installation (ID: 105)
  19. Global Skill Installation (ID: 106)
  20. Listing Available Skills (ID: 110)
  21. Invoking a Skill (ID: 111)
  22. Slash Command Invocation (ID: 112)
  23. Claude Command (ID: 113)
  24. Runbook Command (ID: 116)
  25. ibook Runbook (ID: 117)
  26. Read-Only State Detection (ID: 118)

Terminal Nodes Analysis

Terminal nodes are concepts that nothing else depends on but have prerequisites. They represent natural endpoints of learning paths — culminating or specialized concepts.

  • Total Terminal Nodes: 219 (38.4% of all concepts)
  • Healthy Range: 5-40% of total concepts

Concepts at the end of learning paths:

  • 4: Tokenization
  • 22: VS Code Terminal
  • 28: IDE Agent Integration
  • 29: Chat Versus Agent Interfaces
  • 37: Five Levels of Intelligence
  • 39: Learning Record Store
  • 48: Blank Line Before Lists
  • 55: Skill License Field
  • 56: Compatibility Field
  • 57: Skill Metadata Field
  • 59: Vendor Extension Fields
  • 60: Frontmatter Contract
  • 67: Body Loading Budget
  • 78: Skill Alias Map
  • 80: Opus Versus Sonnet Routing
  • 84: Skill Composition
  • 85: Skill Naming Conventions
  • 86: Supporting Assets in Skills
  • 87: Reference Docs in Skills
  • 88: Template Files in Skills

...and 199 more

Orphaned Nodes Analysis

Orphaned nodes are completely disconnected concepts with no inbound AND no outbound edges. These indicate a quality problem — every concept should connect to the graph.

  • Total Orphaned Nodes: 0

✅ No orphaned nodes detected. All concepts are connected to the graph.

Connected Components

  • Number of Connected Components: 1

✅ All concepts are connected in a single graph.

Indegree Analysis

Top 10 concepts that are prerequisites for the most other concepts:

Rank Concept ID Concept Label Indegree
1 13 Markdown Formatting 40
2 178 Python Scripts in Skills 28
3 49 Agent Skill 21
4 2 Large Language Model 20
5 31 Intelligent Textbook 17
6 12 Python 16
7 360 script.js File 14
8 14 Terminal Commands 13
9 43 Quality Gate 13
10 229 Learning Graph 13

Outdegree Distribution

Dependencies Number of Concepts
0 6
1 5
2 550
3 7
5 1
6 1

Recommendations

  • ✅ Terminal node percentage (38.4%): Within healthy range (5-40%)
  • ✅ DAG structure verified: Graph supports valid learning progressions
  • ℹ️ Long dependency chains (26): Ensure students can follow extended learning paths

Report generated by learning-graph-reports/analyze_graph.py