Run Learning Graph Generator Skill

# Run the Learning Graph Generator Skill

Would you like to proceed with generating the learning graph using the learning-graph-generator skill?

yes

⏺ I'll run the learning-graph-generator skill to create a comprehensive learning graph from your course description.

The "learning-graph-generator" skill is running

The "learning-graph-generator" skill is running

⏺ I'll generate a comprehensive learning graph from your course description. Let me start by setting up the environment and working through each step.

⏺ Bash(mkdir -p docs/learning-graph) ⎿ (No content)

⏺ Search(pattern: "*/.py") ⎿ Found 0 files

⏺ Bash(ls -la /Users/danmccreary/.claude/skills/learning-graph-generator/) ⎿ total 216
drwxr-xr-x 13 danmccreary staff 416 Nov 3 22:43 . drwxr-xr-x 12 danmccreary staff 384 Nov 3 22:43 .. … +11 lines (ctrl+o to expand)

⏺ Bash(cp /Users/danmccreary/.claude/skills/learning-graph-generator/.py docs/learning-graph/ && cp /Users/danmccreary/.claude/skills/learning-graph-generator/.sh do…) ⎿ total 112
drwxr-xr-x 8 danmccreary staff 256 Nov 3 23:04 . drwxr-xr-x 17 danmccreary staff 544 Nov 3 23:04 .. … +6 lines (ctrl+o to expand)

⏺ Now let me create the course description quality assessment report:

⏺ Write(docs/learning-graph/course-description-assessment.md) ⎿ Wrote 147 lines to docs/learning-graph/course-description-assessment.md # Course Description Quality Assessment ## Overall Score: 100/100 ### Quality Rating: Excellent - Ready for learning graph generation ## Detailed Scoring Breakdown | Element | Points Earned | Max Points | Status | |---------|---------------|------------|--------| … +137 lines (ctrl+o to expand)

⏺ Now I'll generate 200 concept labels covering the full breadth of the course. These will be organized to support the learning outcomes and major topic areas.

⏺ Write(docs/learning-graph/concept-list.md) ⎿ Wrote 205 lines to docs/learning-graph/concept-list.md
# Learning Graph Concept List This list contains 200 concepts for the IT Management Graphs course, organized to support learning progression from foundational knowledge through advanced synthesis. 1. Configuration Item 2. Configuration Management 3. Configuration Management Database 4. CMDB 5. Information Technology Infrastructure Library 6. ITIL … +195 lines (ctrl+o to expand)

⏺ Now I'll create the dependency graph CSV with meaningful learning pathways. This will establish prerequisite relationships between concepts.

⏺ Write(docs/learning-graph/learning-graph.csv) ⎿ Wrote 202 lines to docs/learning-graph/learning-graph.csv ConceptID,ConceptLabel,Dependencies 1,Configuration Item, 2,Configuration Management,1 3,Configuration Management Database,2 4,CMDB,3 5,Information Technology Infrastructure Library, 6,ITIL,5 7,ITIL Version 1,6 8,Service Support,6 9,Service Delivery,6 … +192 lines (ctrl+o to expand)

⏺ Bash(cd docs/learning-graph && python analyze-graph.py learning-graph.csv quality-metrics.md) ⎿ ✅ Quality metrics report generated: quality-metrics.md

⏺ Read(quality-metrics.md) ⎿ Read 158 lines

⏺ I can see the quality report shows some disconnected components that need to be connected. Let me fix these issues by updating the dependencies:

⏺ Update(learning-graph.csv) ⎿ Updated learning-graph.csv with 1 addition and 1 removal 124 123,Monitoring,122 125 124,Telemetry,123 126 125,OpenTelemetry,124 127 - 126,eBPF, 127 + 126,eBPF,124 128 127,Extended Berkeley Packet Filter,126 129 128,Automated Discovery,123 130 129,Auto-Discovery,128

⏺ Update(learning-graph.csv) ⎿ Updated learning-graph.csv with 1 addition and 1 removal 143 142,Digital Operational Resilience Act,141 144 143,Audit Trail,2|135 145 144,Compliance Reporting,136|143 146 - 145,Risk Management, 146 + 145,Risk Management,135 147 146,Risk Assessment,145 148 147,Vendor Management, 149 148,ServiceNow,3

⏺ Update(learning-graph.csv) ⎿ Updated learning-graph.csv with 1 addition and 1 removal 148 147,Vendor Management, 149 148,ServiceNow,3 150 149,Dynatrace,123 151 - 150,Atlassian, 151 + 150,Atlassian,147 152 151,Vendor Evaluation,147 153 152,Technology Selection,151 154 153,Build vs Buy,152

⏺ Update(learning-graph.csv) ⎿ Updated learning-graph.csv with 1 addition and 1 removal 179 178,Validation Rule,177 180 179,Business Rule,178 181 180,Policy Enforcement,179|91 182 - 181,Access Control, 182 + 181,Access Control,91 183 182,Role-Based Access Control,181 184 183,RBAC,182 185 184,Security Model,181

⏺ Bash(cd docs/learning-graph && python analyze-graph.py learning-graph.csv quality-metrics.md) ⎿ Error: Exit code 1 (eval):cd:1: no such file or directory: docs/learning-graph

⏺ Bash(python /Users/danmccreary/Documents/ws/it-management-graph/docs/learning-graph/analyze-graph.py /Users/danmccreary/Documents/ws/it-management-graph/docs/learning-…) ⎿ ✅ Quality metrics report generated:
/Users/danmccreary/Documents/ws/it-management-graph/docs/learning-graph/qua lity-metrics.md

⏺ Read(quality-metrics.md) ⎿ Read 100 lines

⏺ Excellent! The graph quality has improved significantly: - ✅ All concepts now connected in a single graph - ✅ 10 foundational concepts (down from 14) - ✅ No cycles detected - ✅ Average 1.27 dependencies per concept

The 86 orphaned nodes are expected - these are terminal concepts that represent final learning outcomes. Now let me update the todo and create the concept taxonomy.

⏺ Write(concept-taxonomy.md) ⎿ Wrote 189 lines to concept-taxonomy.md
# Concept Taxonomy This taxonomy organizes the 200 concepts into 12 logical categories that reflect the major topic areas of the IT Management Graphs course. ## 1. ITIL (ITIL) Description: ITIL framework, configuration management, and legacy CMDB concepts including service support, service delivery, and traditional IT management processes. Concepts Include: - Configuration Management Database (CMDB) … +179 lines (ctrl+o to expand)

⏺ Read(add-taxonomy.py) ⎿ Read 50 lines

⏺ Let me create an updated CSV with taxonomy IDs added. I'll assign each concept to the appropriate taxonomy category:

⏺ Read(learning-graph.csv) ⎿ Read 50 lines

⏺ Update(learning-graph.csv) ⎿ Updated learning-graph.csv with 1 addition and 1 removal 1 - ConceptID,ConceptLabel,Dependencies 1 + ConceptID,ConceptLabel,Dependencies,TaxonomyID 2 1,Configuration Item, 3 2,Configuration Management,1 4 3,Configuration Management Database,2

⏺ Now I'll add taxonomy IDs to all concepts. Given the large number of rows, let me create a complete updated version:

⏺ Write(learning-graph.csv) ⎿ Updated learning-graph.csv with 200 additions and 200 removals 1 ConceptID,ConceptLabel,Dependencies,TaxonomyID 2 - 1,Configuration Item, 3 - 2,Configuration Management,1 4 - 3,Configuration Management Database,2 5 - 4,CMDB,3 6 - 5,Information Technology Infrastructure Library, 7 - 6,ITIL,5 8 - 7,ITIL Version 1,6 9 - 8,Service Support,6 10 - 9,Service Delivery,6 11 - 10,Change Management,2|8 12 - 11,Incident Management,8 13 - 12,Problem Management,8|11 14 - 13,Release Management,8|10 15 - 14,Configuration Baseline,2 16 - 15,Configuration Audit,2|14 17 - 16,Military-Spec Configuration,2 18 - 17,Asset Management,1 19 - 18,IT Asset,17 20 - 19,Hardware Asset,18 21 - 20,Software Asset,18 22 - 21,Relational Database, 23 - 22,RDBMS,21 24 - 23,Structured Query Language,22 25 - 24,SQL,23 26 - 25,Database Schema,22 27 - 26,Table,25 28 - 27,Column,26 29 - 28,Row,26 30 - 29,Primary Key,26 31 - 30,Foreign Key,26|29 32 - 31,Join Operation,26|30 33 - 32,Inner Join,31 34 - 33,Outer Join,31 35 - 34,Transitive Dependency,30 36 - 35,Multi-Hop Query,31|34 37 - 36,Query Performance,23 38 - 37,Database Index,26|36 39 - 38,Query Optimization,36|37 40 - 39,Schema Rigidity,25 41 - 40,Schema Evolution,25|39 42 - 41,Graph Database,21 43 - 42,Graph Theory, 44 - 43,Node,42 45 - 44,Edge,42|43 46 - 45,Vertex,43 47 - 46,Relationship,44 48 - 47,Property Graph,41|43|44 49 - 48,Node Property,43|47 50 - 49,Edge Property,44|47 51 - 50,Graph Traversal,43|44 52 - 51,Depth-First Search,50 53 - 52,Breadth-First Search,50 54 - 53,Path Finding,50 55 - 54,Shortest Path,53 56 - 55,Graph Algorithm,50 57 - 56,Directed Graph,42|44 58 - 57,Undirected Graph,42|44 59 - 58,Directed Acyclic Graph,56 60 - 59,DAG,58 61 - 60,Cycle Detection,56 62 - 61,Native Graph Storage,41 63 - 62,Graph Layer,41|22 64 - 63,Neo4j,41 65 - 64,Cypher Query Language,63 66 - 65,Graph Query,41|50 67 - 66,Pattern Matching,65 68 - 67,Dependency Tracing,50|46 69 - 68,Upstream Dependency,67 70 - 69,Downstream Dependency,67 71 - 70,Blast Radius,67|69 72 - 71,Impact Analysis,67|70 73 - 72,Root Cause Analysis,67|68 74 - 73,Change Impact Assessment,71|10 75 - 74,Dependency Chain,67 76 - 75,Dependency Map,74 77 - 76,Circular Dependency,60|74 78 - 77,Service Dependency,74 79 - 78,Application Dependency,74 80 - 79,Infrastructure Dependency,74 81 - 80,Business Service, 82 - 81,Technical Service,80 83 - 82,Service Mapping,77|81 84 - 83,Business Service Mapping,82|80 85 - 84,Application Portfolio,20 86 - 85,Digital Estate,18|84 87 - 86,IT Portfolio,85 88 - 87,Technical Debt,85 89 - 88,Legacy System,87 90 - 89,System Integration,81 91 - 90,Data Quality, 92 - 91,Data Governance,90 93 - 92,Data Management,90 94 - 93,DMBOK,92 95 - 94,Data Quality Dimension,90 96 - 95,Accuracy,94 97 - 96,Completeness,94 98 - 97,Consistency,94 99 - 98,Timeliness,94 100 - 99,Validity,94 101 - 100,Fitness for Purpose,90 102 - 101,Data Steward,91 103 - 102,Data Owner,91 104 - 103,Data Custodian,91 105 - 104,Metadata,92 106 - 105,Data Lineage,92|104 107 - 106,Data Catalog,104 108 - 107,Master Data Management,92 109 - 108,Reference Data,107 110 - 109,Real-Time Query,23|65 111 - 110,Query Latency,36|109 112 - 111,Response Time,110 113 - 112,Performance Metric,36 114 - 113,Scalability,112 115 - 114,Horizontal Scaling,113 116 - 115,Vertical Scaling,113 117 - 116,Graph Complexity,42|55 118 - 117,Graph Density,116 119 - 118,Node Degree,43 120 - 119,In-Degree,118|56 121 - 120,Out-Degree,118|56 122 - 121,Graph Metric,116 123 - 122,Observability, 124 - 123,Monitoring,122 125 - 124,Telemetry,123 126 - 125,OpenTelemetry,124 127 - 126,eBPF,124 128 - 127,Extended Berkeley Packet Filter,126 129 - 128,Automated Discovery,123 130 - 129,Auto-Discovery,128 131 - 130,Network Topology,79 132 - 131,Service Topology,77|82 133 - 132,Dynamic Topology,130|131 134 - 133,Configuration Drift,2|14 135 - 134,Drift Detection,133 136 - 135,Compliance, 137 - 136,Regulatory Compliance,135 138 - 137,HIPAA,136 139 - 138,Health Insurance Portability,137 140 - 139,GDPR,136 141 - 140,General Data Protection Regulation,139 142 - 141,DORA,136 143 - 142,Digital Operational Resilience Act,141 144 - 143,Audit Trail,2|135 145 - 144,Compliance Reporting,136|143 146 - 145,Risk Management,135 147 - 146,Risk Assessment,145 148 - 147,Vendor Management, 149 - 148,ServiceNow,3 150 - 149,Dynatrace,123 151 - 150,Atlassian,147 152 - 151,Vendor Evaluation,147 153 - 152,Technology Selection,151 154 - 153,Build vs Buy,152 155 - 154,Total Cost of Ownership,147 156 - 155,TCO,154 157 - 156,Return on Investment,154 158 - 157,ROI,156 159 - 158,Business Case,157 160 - 159,Digital Transformation,85|158 161 - 160,IT Modernization,159 162 - 161,Legacy Migration,88|160 163 - 162,Migration Strategy,161 164 - 163,Data Migration,162|92 165 - 164,System Cutover,162 166 - 165,Artificial Intelligence, 167 - 166,Machine Learning,165 168 - 167,AI-Assisted Curation,166|92 169 - 168,Graph RAG,166|41 170 - 169,Retrieval Augmented Generation,168 171 - 170,Knowledge Graph,41|104 172 - 171,Semantic Model,170 173 - 172,Ontology,171 174 - 173,Taxonomy,172 175 - 174,Classification System,173 176 - 175,Exception Reporting,90|144 177 - 176,Anomaly Detection,166|90 178 - 177,Data Validation,90 179 - 178,Validation Rule,177 180 - 179,Business Rule,178 181 - 180,Policy Enforcement,179|91 182 - 181,Access Control,91 183 - 182,Role-Based Access Control,181 184 - 183,RBAC,182 185 - 184,Security Model,181 186 - 185,Incident Response,11|123 187 - 186,Mean Time to Detect,185 188 - 187,MTTD,186 189 - 188,Mean Time to Resolve,185 190 - 189,MTTR,188 191 - 190,Service Level Agreement,80 192 - 191,SLA,190 193 - 192,Key Performance Indicator,112 194 - 193,KPI,192 195 - 194,Operational Excellence,112|91 196 - 195,Continuous Improvement,194 197 - 196,Best Practice,194 198 - 197,Industry Standard,196 199 - 198,Framework Adoption,6|93 200 - 199,Process Maturity,198 201 - 200,Capability Model,199 2 + 1,Configuration Item,ITIL,ITIL 3 + 2,Configuration Management,1,ITIL 4 + 3,Configuration Management Database,2,ITIL 5 + 4,CMDB,3,ITIL 6 + 5,Information Technology Infrastructure Library,,ITIL 7 + 6,ITIL,5,ITIL 8 + 7,ITIL Version 1,6,ITIL 9 + 8,Service Support,6,ITIL 10 + 9,Service Delivery,6,ITIL 11 + 10,Change Management,2|8,ITIL 12 + 11,Incident Management,8,OPS 13 + 12,Problem Management,8|11,OPS 14 + 13,Release Management,8|10,ITIL 15 + 14,Configuration Baseline,2,ITIL 16 + 15,Configuration Audit,2|14,ITIL 17 + 16,Military-Spec Configuration,2,ITIL 18 + 17,Asset Management,1,ASSET 19 + 18,IT Asset,17,ASSET 20 + 19,Hardware Asset,18,ASSET 21 + 20,Software Asset,18,ASSET 22 + 21,Relational Database,,RDBMS 23 + 22,RDBMS,21,RDBMS 24 + 23,Structured Query Language,22,RDBMS 25 + 24,SQL,23,RDBMS 26 + 25,Database Schema,22,RDBMS 27 + 26,Table,25,RDBMS 28 + 27,Column,26,RDBMS 29 + 28,Row,26,RDBMS 30 + 29,Primary Key,26,RDBMS 31 + 30,Foreign Key,26|29,RDBMS 32 + 31,Join Operation,26|30,RDBMS 33 + 32,Inner Join,31,RDBMS 34 + 33,Outer Join,31,RDBMS 35 + 34,Transitive Dependency,30,RDBMS 36 + 35,Multi-Hop Query,31|34,RDBMS 37 + 36,Query Performance,23,QPERF 38 + 37,Database Index,26|36,RDBMS 39 + 38,Query Optimization,36|37,QPERF 40 + 39,Schema Rigidity,25,RDBMS 41 + 40,Schema Evolution,25|39,RDBMS 42 + 41,Graph Database,21,GRAPH 43 + 42,Graph Theory,,GRAPH 44 + 43,Node,42,GRAPH 45 + 44,Edge,42|43,GRAPH 46 + 45,Vertex,43,GRAPH 47 + 46,Relationship,44,GRAPH 48 + 47,Property Graph,41|43|44,GRAPH 49 + 48,Node Property,43|47,GRAPH 50 + 49,Edge Property,44|47,GRAPH 51 + 50,Graph Traversal,43|44,GOPS 52 + 51,Depth-First Search,50,GOPS 53 + 52,Breadth-First Search,50,GOPS 54 + 53,Path Finding,50,GOPS 55 + 54,Shortest Path,53,GOPS 56 + 55,Graph Algorithm,50,GOPS 57 + 56,Directed Graph,42|44,GRAPH 58 + 57,Undirected Graph,42|44,GRAPH 59 + 58,Directed Acyclic Graph,56,GRAPH 60 + 59,DAG,58,GRAPH 61 + 60,Cycle Detection,56,GOPS 62 + 61,Native Graph Storage,41,GRAPH 63 + 62,Graph Layer,41|22,GRAPH 64 + 63,Neo4j,41,GRAPH 65 + 64,Cypher Query Language,63,GRAPH 66 + 65,Graph Query,41|50,GOPS 67 + 66,Pattern Matching,65,GOPS 68 + 67,Dependency Tracing,50|46,GOPS 69 + 68,Upstream Dependency,67,GOPS 70 + 69,Downstream Dependency,67,GOPS 71 + 70,Blast Radius,67|69,GOPS 72 + 71,Impact Analysis,67|70,GOPS 73 + 72,Root Cause Analysis,67|68,GOPS 74 + 73,Change Impact Assessment,71|10,GOPS 75 + 74,Dependency Chain,67,GOPS 76 + 75,Dependency Map,74,GOPS 77 + 76,Circular Dependency,60|74,GOPS 78 + 77,Service Dependency,74,BIZS 79 + 78,Application Dependency,74,ASSET 80 + 79,Infrastructure Dependency,74,ASSET 81 + 80,Business Service,,BIZS 82 + 81,Technical Service,80,BIZS 83 + 82,Service Mapping,77|81,BIZS 84 + 83,Business Service Mapping,82|80,BIZS 85 + 84,Application Portfolio,20,ASSET 86 + 85,Digital Estate,18|84,ASSET 87 + 86,IT Portfolio,85,ASSET 88 + 87,Technical Debt,85,TRANS 89 + 88,Legacy System,87,TRANS 90 + 89,System Integration,81,ASSET 91 + 90,Data Quality,,DATA 92 + 91,Data Governance,90,DATA 93 + 92,Data Management,90,DATA 94 + 93,DMBOK,92,DATA 95 + 94,Data Quality Dimension,90,DATA 96 + 95,Accuracy,94,DATA 97 + 96,Completeness,94,DATA 98 + 97,Consistency,94,DATA 99 + 98,Timeliness,94,DATA 100 + 99,Validity,94,DATA 101 + 100,Fitness for Purpose,90,DATA 102 + 101,Data Steward,91,DATA 103 + 102,Data Owner,91,DATA 104 + 103,Data Custodian,91,DATA 105 + 104,Metadata,92,DATA 106 + 105,Data Lineage,92|104,DATA 107 + 106,Data Catalog,104,DATA 108 + 107,Master Data Management,92,DATA 109 + 108,Reference Data,107,DATA 110 + 109,Real-Time Query,23|65,QPERF 111 + 110,Query Latency,36|109,QPERF 112 + 111,Response Time,110,QPERF 113 + 112,Performance Metric,36,QPERF 114 + 113,Scalability,112,QPERF 115 + 114,Horizontal Scaling,113,QPERF 116 + 115,Vertical Scaling,113,QPERF 117 + 116,Graph Complexity,42|55,QPERF 118 + 117,Graph Density,116,QPERF 119 + 118,Node Degree,43,QPERF 120 + 119,In-Degree,118|56,QPERF 121 + 120,Out-Degree,118|56,QPERF 122 + 121,Graph Metric,116,QPERF 123 + 122,Observability,,OBSRV 124 + 123,Monitoring,122,OBSRV 125 + 124,Telemetry,123,OBSRV 126 + 125,OpenTelemetry,124,OBSRV 127 + 126,eBPF,124,OBSRV 128 + 127,Extended Berkeley Packet Filter,126,OBSRV 129 + 128,Automated Discovery,123,OBSRV 130 + 129,Auto-Discovery,128,OBSRV 131 + 130,Network Topology,79,ASSET 132 + 131,Service Topology,77|82,BIZS 133 + 132,Dynamic Topology,130|131,OBSRV 134 + 133,Configuration Drift,2|14,OBSRV 135 + 134,Drift Detection,133,OBSRV 136 + 135,Compliance,,COMP 137 + 136,Regulatory Compliance,135,COMP 138 + 137,HIPAA,136,COMP 139 + 138,Health Insurance Portability,137,COMP 140 + 139,GDPR,136,COMP 141 + 140,General Data Protection Regulation,139,COMP 142 + 141,DORA,136,COMP 143 + 142,Digital Operational Resilience Act,141,COMP 144 + 143,Audit Trail,2|135,COMP 145 + 144,Compliance Reporting,136|143,COMP 146 + 145,Risk Management,135,COMP 147 + 146,Risk Assessment,145,COMP 148 + 147,Vendor Management,,TRANS 149 + 148,ServiceNow,3,TRANS 150 + 149,Dynatrace,123,TRANS 151 + 150,Atlassian,147,TRANS 152 + 151,Vendor Evaluation,147,TRANS 153 + 152,Technology Selection,151,TRANS 154 + 153,Build vs Buy,152,TRANS 155 + 154,Total Cost of Ownership,147,TRANS 156 + 155,TCO,154,TRANS 157 + 156,Return on Investment,154,TRANS 158 + 157,ROI,156,TRANS 159 + 158,Business Case,157,TRANS 160 + 159,Digital Transformation,85|158,TRANS 161 + 160,IT Modernization,159,TRANS 162 + 161,Legacy Migration,88|160,TRANS 163 + 162,Migration Strategy,161,TRANS 164 + 163,Data Migration,162|92,TRANS 165 + 164,System Cutover,162,TRANS 166 + 165,Artificial Intelligence,,AI 167 + 166,Machine Learning,165,AI 168 + 167,AI-Assisted Curation,166|92,AI 169 + 168,Graph RAG,166|41,AI 170 + 169,Retrieval Augmented Generation,168,AI 171 + 170,Knowledge Graph,41|104,AI 172 + 171,Semantic Model,170,AI 173 + 172,Ontology,171,AI 174 + 173,Taxonomy,172,AI 175 + 174,Classification System,173,VALID 176 + 175,Exception Reporting,90|144,VALID 177 + 176,Anomaly Detection,166|90,AI 178 + 177,Data Validation,90,VALID 179 + 178,Validation Rule,177,VALID 180 + 179,Business Rule,178,VALID 181 + 180,Policy Enforcement,179|91,DATA 182 + 181,Access Control,91,COMP 183 + 182,Role-Based Access Control,181,COMP 184 + 183,RBAC,182,COMP 185 + 184,Security Model,181,COMP 186 + 185,Incident Response,11|123,OPS 187 + 186,Mean Time to Detect,185,OPS 188 + 187,MTTD,186,OPS 189 + 188,Mean Time to Resolve,185,OPS 190 + 189,MTTR,188,OPS 191 + 190,Service Level Agreement,80,BIZS 192 + 191,SLA,190,BIZS 193 + 192,Key Performance Indicator,112,OPS 194 + 193,KPI,192,OPS 195 + 194,Operational Excellence,112|91,OPS 196 + 195,Continuous Improvement,194,OPS 197 + 196,Best Practice,194,OPS 198 + 197,Industry Standard,196,OPS 199 + 198,Framework Adoption,6|93,OPS 200 + 199,Process Maturity,198,OPS 201 + 200,Capability Model,199,OPS

⏺ I notice there's an error in the first row. Let me fix it:

⏺ Update(learning-graph.csv) ⎿ Updated learning-graph.csv with 1 addition and 1 removal 1 ConceptID,ConceptLabel,Dependencies,TaxonomyID 2 - 1,Configuration Item,ITIL,ITIL 2 + 1,Configuration Item,,ITIL 3 2,Configuration Management,1,ITIL 4 3,Configuration Management Database,2,ITIL 5 4,CMDB,3,ITIL

⏺ Now I'll create the metadata.json file for the learning graph:

⏺ Write(metadata.json) ⎿ Wrote 11 lines to metadata.json
{ "title": "IT Management Graphs - From Legacy CMDB to Modern Graph-Based Solutions", "description": "This graduate-level course examines the evolution of IT configuration management from traditional relational database approaches to modern graph-based solutions. Students explore why legacy CMDB implementations have failed and how graph databases revolutionize IT management through real-time multi-hop transitive dependency analysis.", "creator": "Dan McCreary", "date": "2025-11-03", "version": "1.0", "format": "Learning Graph JSON v1.0", "schema": "https://raw.githubusercontent.com/dmccreary/learning-graphs/refs/heads/main/src/schema/learning-g raph-schema.json", "license": "CC BY-NC-SA 4.0 DEED" } … +1 line (ctrl+o to expand)

⏺ Bash(python /Users/danmccreary/Documents/ws/it-management-graph/docs/learning-graph/csv-to-json.py /Users/danmccreary/Documents/ws/it-management-graph/docs/learning-gr…) ⎿ 📋 Loaded color config from: /Users/danmccreary/Documents/ws/it-management-graph/docs/learning-graph/metadata.j son ✅ JSON graph created: /Users/danmccreary/Documents/ws/it-management-graph/docs/learning-graph/learning-graph.js … +30 lines (ctrl+o to expand)

⏺ Read(csv-to-json.py) ⎿ Read 80 lines

⏺ Read(csv-to-json.py) ⎿ Read 50 lines

⏺ Read(csv-to-json.py) ⎿ Read 41 lines

⏺ I see the issue - the metadata.json was loaded as color_config instead of metadata. Let me create a proper color config file for our taxonomies and run the command with both files in the correct order:

⏺ Write(color-config.json) ⎿ Wrote 17 lines to color-config.json { "ITIL": "red", "RDBMS": "orange", "GRAPH": "gold", "GOPS": "green", "QPERF": "cyan", "DATA": "blue", "OBSRV": "purple", "COMP": "brown", "BIZS": "pink", … +7 lines (ctrl+o to expand)

⏺ Bash(python /Users/danmccreary/Documents/ws/it-management-graph/docs/learning-graph/csv-to-json.py /Users/danmccreary/Documents/ws/it-management-graph/docs/learning-gr…) ⎿ 📋 Loaded color config from: /Users/danmccreary/Documents/ws/it-management-graph/docs/learning-graph/color-conf ig.json 📋 Loaded metadata from: /Users/danmccreary/Documents/ws/it-management-graph/docs/learning-graph/metadata.json … +34 lines (ctrl+o to expand)

⏺ Perfect! The learning-graph.json has been successfully created with all 14 taxonomies and 200 concepts. Now let me generate the taxonomy distribution report:

⏺ Bash(python /Users/danmccreary/Documents/ws/it-management-graph/docs/learning-graph/taxonomy-distribution.py /Users/danmccreary/Documents/ws/it-management-graph/docs/l…) ⎿ ✅ Taxonomy distribution report generated:
/Users/danmccreary/Documents/ws/it-management-graph/docs/learning-gra ph/taxonomy-distribution.md

⏺ Read(taxonomy-distribution.md) ⎿ Read 360 lines

⏺ Perfect! The taxonomy distribution shows excellent balance across all 14 categories. Now let me create the index.md file from the template:

⏺ Read(~/.claude/skills/learning-graph-generator/index-template.md) ⎿ Read 93 lines

⏺ Write(index.md) ⎿ Wrote 96 lines to index.md
# Learning Graph for IT Management Graphs This section contains the learning graph for this textbook. A learning graph is a graph of concepts used in this textbook. Each concept is represented by a node in a network graph. Concepts are connected by directed edges that indicate what concepts each node depends on before that concept is understood by the student. A learning graph is the foundational data structure for intelligent textbooks that can recommend learning paths. A learning graph is like a roadmap of concepts to help students arrive at their learning goals. … +86 lines (ctrl+o to expand)

⏺ Read(~/Documents/ws/it-management-graph/mkdocs.yml) ⎿ Read 63 lines

⏺ Update(~/Documents/ws/it-management-graph/mkdocs.yml) ⎿ Updated ../../mkdocs.yml with 7 additions
9 nav: 10 - Home: index.md 11 - Course Description: course-description.md 12 + - Learning Graph: 13 + - Introduction: learning-graph/index.md 14 + - Course Description Assessment: learning-graph/course-description-assessment.md 15 + - Concept List: learning-graph/concept-list.md 16 + - Graph Quality Analysis: learning-graph/quality-metrics.md 17 + - Concept Taxonomy: learning-graph/concept-taxonomy.md 18 + - Taxonomy Distribution: learning-graph/taxonomy-distribution.md 19 - Checklist: checklist.md 20 - Code Highlight Test: code-highlight-test.md 21 - Glossary: glossary.md

⏺ Learning Graph Generation Complete!

Congratulations! I've successfully generated a comprehensive learning graph for your IT Management Graphs course. Here's what has been created:

Files Created

In /docs/learning-graph/:

course-description-assessment.md - Quality assessment (Score: 100/100)
concept-list.md - 200 carefully curated concepts
learning-graph.csv - Dependency graph with taxonomy IDs
learning-graph.json - Complete vis-network format with metadata, groups, nodes, and edges
metadata.json - Dublin Core metadata for the learning graph
color-config.json - Color scheme for the 14 taxonomy categories
quality-metrics.md - Graph quality validation report
concept-taxonomy.md - 14 taxonomy categories with descriptions
taxonomy-distribution.md - Distribution analysis across categories
index.md - Introduction page for the learning graph section

Graph Statistics

200 concepts organized into 14 balanced taxonomies
242 dependency edges creating meaningful learning pathways
10 foundational concepts (entry points with no prerequisites)
Single connected component (all concepts properly connected)
No cycles detected (valid DAG structure)
Excellent taxonomy balance (2.5% - 10% per category)

Taxonomy Categories (14)

ITIL • RDBMS • GRAPH • GOPS • QPERF • DATA • OBSRV • COMP • BIZS • ASSET • TRANS • AI • VALID • OPS

Navigation Updated

The learning graph section has been added to your mkdocs.yml navigation structure, making all reports and data files easily accessible.

Quality Scores

Course Description: 100/100
Graph Balance: Excellent (7.5% spread)
Connectivity: All concepts in single graph
DAG Validation: No cycles detected

Next Steps

You can now: 1. Build and preview your MkDocs site to view the learning graph documentation 2. Use the learning-graph.json file with vis-network.js for interactive visualization 3. Proceed with other intelligent textbook features (glossary, quizzes, FAQs, MicroSims)

Your learning graph is ready for use in creating an intelligent, interactive textbook! Good luck with your course materials and educational content development!

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