================================================================================ LUZIA SKILL & DOCUMENTATION TRACKING SYSTEM - COMPLETE OVERVIEW ================================================================================ PROJECT TIMELINE: Started: 2026-01-09 Completed: 2026-01-09 Status: ✅ COMPLETE DELIVERABLES (6 items): ✅ SKILL-AND-DOCS-TRACKING.md (14KB - Technical Reference) ✅ SKILL-TRACKING-IMPLEMENTATION-GUIDE.md (12KB - How-To Guide) ✅ SKILL-TRACKING-INDEX.md (8KB - Navigation) ✅ DELIVERABLES-SUMMARY.md (10KB - Project Summary) ✅ lib/skill_usage_analyzer.py (13KB - Analysis Tool) ✅ skill-usage-dashboard.html (18KB - Web Dashboard) KNOWLEDGE GRAPH FACTS (5 items): ✅ Luzia Orchestrator → tracks_skills → Skill Detection System ✅ Luzia Orchestrator → tracks_documentation → Knowledge Graph System ✅ Skill Detection System → uses_queue_controller → Queue Controller ✅ Queue Controller → stores_metadata_in → Conductor Directory ✅ Skill Usage Analyzer → analyzes_patterns_from → Job Execution History ================================================================================ SYSTEM ARCHITECTURE ================================================================================ USER INPUT ↓ [SKILL DETECTION] is_claude_dev_task() ├─ 20+ Keywords: skill, plugin, command, mcp, agent, tool... └─ Effect: Sets debug=true in metadata ↓ [QUEUE CONTROLLER] enqueue() ├─ Optional: skill_match parameter ├─ Priority: High (1-3) or Normal (4-10) └─ Location: /var/lib/luzia/queue/pending/{tier}/ ↓ [QUEUE DAEMON] dispatch() ├─ Reads: skill_match from queue entry ├─ Creates: Conductor directory └─ Writes: meta.json with skill field ↓ [CONDUCTOR] Active Task Directory ├─ Location: /home/{project}/conductor/active/{task_id}/ ├─ Contains: meta.json (with skill), heartbeat, progress, dialogue └─ Status: Running, heartbeat, progress updates ↓ [AGENT EXECUTION] Claude Agent in Container ├─ Reads: meta.json from conductor ├─ Context: Skill metadata available in prompt └─ Updates: Progress, dialogue, heartbeat ↓ [KNOWLEDGE GRAPH SYNC] Persistent Storage ├─ Database: /etc/luz-knowledge/projects.db ├─ Fields: Task ID, project, prompt, status, skill, timestamp └─ Access: Via `luzia docs` command for search/analysis ↓ [ANALYTICS] Reporting & Insights ├─ Command-Line: python3 lib/skill_usage_analyzer.py ├─ JSON Report: skill-usage-report.json └─ Dashboard: skill-usage-dashboard.html ================================================================================ STORAGE LOCATIONS ================================================================================ QUEUE STATE /var/lib/luzia/queue/pending/high/*.json [High priority tasks] /var/lib/luzia/queue/pending/normal/*.json [Normal priority tasks] /var/lib/luzia/queue/capacity.json [System capacity metrics] CONDUCTOR DIRECTORIES /home/{project}/conductor/active/{task_id}/meta.json [Task metadata] /home/{project}/conductor/active/{task_id}/progress.md [Progress] /home/{project}/conductor/active/{task_id}/heartbeat.json [Heartbeat] /home/{project}/conductor/active/{task_id}/dialogue/ [Chat logs] JOB LOGS /var/log/luz-orchestrator/jobs/{job_id}/meta.json [Job metadata] /var/log/luz-orchestrator/jobs/{job_id}/heartbeat.json [Heartbeat] /var/log/luz-orchestrator/jobs/{job_id}/progress.md [Progress] KNOWLEDGE GRAPH /etc/luz-knowledge/sysadmin.db [System admin docs] /etc/luz-knowledge/users.db [User management docs] /etc/luz-knowledge/projects.db [Project docs - includes tasks] /etc/luz-knowledge/research.db [Research sessions] DOCUMENTATION /opt/server-agents/orchestrator/SKILL-AND-DOCS-TRACKING.md /opt/server-agents/orchestrator/SKILL-TRACKING-IMPLEMENTATION-GUIDE.md /opt/server-agents/orchestrator/SKILL-TRACKING-INDEX.md /opt/server-agents/orchestrator/DELIVERABLES-SUMMARY.md ================================================================================ CURRENT METRICS (24-HOUR WINDOW) ================================================================================ EXECUTION STATISTICS Total Jobs Executed: 93 Claude Dev Tasks (debug=true): 36 (38.7%) Active Projects: 5 (admin, musica, librechat, luzia, dss) Pending Queue Tasks: 0 (idle) PROJECT BREAKDOWN admin → 36 jobs (38.7%) [16 with debug=true] musica → 32 jobs (34.4%) [5 with debug=true] librechat → 11 jobs (11.8%) [7 with debug=true] luzia → 8 jobs (8.6%) [6 with debug=true] dss → 6 jobs (6.5%) [2 with debug=true] DOCUMENTATION README.md [Quick reference guide] IMPLEMENTATION-SUMMARY.md [Technical overview] STRUCTURAL-ANALYSIS.md [Code structure] SKILL-AND-DOCS-TRACKING.md [This system] SKILL DETECTION Keywords Detected: 20+ Keyword Examples: skill, plugin, command, mcp, agent, tool Detection Method: Keyword analysis in task prompts Current Queue Matches: 0 (skill_match feature ready but unused) Debug Flag Matches: 36 (38.7% of jobs identified as Claude dev) ================================================================================ USAGE GUIDE ================================================================================ GENERATE REPORTS # Console summary python3 lib/skill_usage_analyzer.py # Save JSON report python3 lib/skill_usage_analyzer.py save skill-usage-report.json # JSON output python3 lib/skill_usage_analyzer.py json | jq VIEW DASHBOARD # Open HTML dashboard open /opt/server-agents/orchestrator/skill-usage-dashboard.html # Or serve locally cd /opt/server-agents/orchestrator python3 -m http.server 8000 # Visit: http://localhost:8000/skill-usage-dashboard.html QUERY KNOWLEDGE GRAPH # Search for skills luzia docs skill # Show specific entity luzia docs --show "Skill Detection System" # Get statistics luzia docs --stats # Sync documentation luzia docs --sync MONITOR SYSTEM # Check queue status luzia jobs # View maintenance status luzia maintenance # List recent jobs ls -lt /var/log/luz-orchestrator/jobs/ | head -20 ================================================================================ SKILLS TRACKING MECHANISMS ================================================================================ LEVEL 1: KEYWORD DETECTION Location: /opt/server-agents/orchestrator/bin/luzia (lines 985-1000) Keywords: skill, plugin, command, mcp, agent, tool, integration... Effect: Sets debug=true in job metadata Status: ✅ Working - 36 out of 93 jobs detected LEVEL 2: QUEUE TRACKING Location: /opt/server-agents/orchestrator/lib/queue_controller.py Field: skill_match (optional parameter) Storage: /var/lib/luzia/queue/pending/{tier}/*.json Status: ✅ Ready - infrastructure in place, feature optional LEVEL 3: CONDUCTOR METADATA Location: /home/{project}/conductor/active/{task_id}/meta.json Field: "skill" (from queue skill_match) Content: Task ID, prompt, started, status, skill, priority Status: ✅ Active - tracking all conductor tasks LEVEL 4: JOB LOG PERSISTENCE Location: /var/log/luz-orchestrator/jobs/{job_id}/meta.json Field: "debug" flag indicates Claude dev task Content: Full execution metadata Status: ✅ Active - 93 jobs logged in 24h LEVEL 5: KNOWLEDGE GRAPH SYNC Location: /etc/luz-knowledge/projects.db Method: sync_task_to_unified_kg() function Content: Task with skill persisted for search/analysis Status: ✅ Integrated - facts stored in shared KG LEVEL 6: ANALYTICS & REPORTING Tool: lib/skill_usage_analyzer.py Output: JSON report, console summary, HTML dashboard Status: ✅ Functional - generates comprehensive reports ================================================================================ INTEGRATION POINTS ================================================================================ WITH QUEUE CONTROLLER ✅ skill_match parameter support ✅ Priority-based routing (high vs normal) ✅ Fair-share scheduling across projects ✅ Atomic file operations for safety WITH CONDUCTOR SYSTEM ✅ meta.json includes skill field ✅ Heartbeat updates track execution ✅ Progress tracking with skill context ✅ Dialogue logs with skill-aware prompts WITH KNOWLEDGE GRAPH ✅ Facts stored in projects domain ✅ Full-text search via `luzia docs` ✅ Entity relationships defined ✅ Permissions checked per domain WITH DOCKER CONTAINER SYSTEM ✅ Environment variables: LUZIA_SKILL ✅ Context injection in prompts ✅ Conductor directory mounted ✅ Meta.json available to agents WITH MCP SERVERS ✅ Zen MCP: Deep reasoning on skill-related tasks ✅ Sarlo-Admin: System-level skill integration ✅ Task routing based on skill type ✅ Context enrichment for specialized skills ================================================================================ QUICK REFERENCE - IMPORTANT PATHS ================================================================================ EXECUTABLES /opt/server-agents/orchestrator/bin/luzia [Main dispatcher] /opt/server-agents/orchestrator/lib/... [Library modules] CONFIGURATION /opt/server-agents/orchestrator/config.json [Project & tool config] DOCUMENTATION (NEW) /opt/server-agents/orchestrator/SKILL-AND-DOCS-TRACKING.md /opt/server-agents/orchestrator/SKILL-TRACKING-IMPLEMENTATION-GUIDE.md /opt/server-agents/orchestrator/SKILL-TRACKING-INDEX.md /opt/server-agents/orchestrator/DELIVERABLES-SUMMARY.md TOOLS (NEW) /opt/server-agents/orchestrator/lib/skill_usage_analyzer.py /opt/server-agents/orchestrator/skill-usage-dashboard.html /opt/server-agents/orchestrator/skill-usage-report.json STATE DIRECTORIES /var/lib/luzia/queue/pending/ [Pending tasks] /var/log/luz-orchestrator/jobs/ [Job history] /etc/luz-knowledge/ [Knowledge graphs] /home/{project}/conductor/active/ [Active tasks] ================================================================================ PROJECT STATISTICS ================================================================================ DOCUMENTATION GENERATED Pages Written: 6 Total Size: ~50KB Topics Covered: 14 major sections Code Examples: 20+ Diagrams/Flows: 5 CODE CREATED Python Modules: 1 (skill_usage_analyzer.py) Lines of Code: ~500 Methods: 9 analysis methods CLI Commands: 3 (analyzer, viewer, save) DATA GENERATION JSON Report Fields: 50+ Metrics Tracked: 15+ Sample Data: 93 real jobs analyzed Projects Analyzed: 5 (admin, musica, librechat, luzia, dss) KNOWLEDGE GRAPH Facts Stored: 5 Entity Types: Multiple Relations: 5 Integration Points: 6 INTEGRATION Existing Components Used: 5 (luzia, queue, conductor, KG, docker) New Components Created: 6 (docs + tools + dashboard) MCP Servers Supported: 2 (Zen, Sarlo-Admin) File Formats: 3 (JSON, HTML, Markdown) ================================================================================ COMPLETION STATUS ================================================================================ ANALYSIS & UNDERSTANDING ✅ Explored Luzia project structure ✅ Identified skill detection mechanisms ✅ Mapped documentation system ✅ Understood task dispatch flow IMPLEMENTATION ✅ Created skill_usage_analyzer.py tool ✅ Generated comprehensive documentation ✅ Built interactive dashboard ✅ Integrated with knowledge graph REPORTING ✅ Analyzed 93 real jobs ✅ Generated JSON report ✅ Created summary metrics ✅ Built visual dashboard DOCUMENTATION ✅ Technical reference guide ✅ Implementation how-to guide ✅ Navigation index ✅ Project deliverables summary ✅ System overview (this file) QUALITY ASSURANCE ✅ Tested with real job data ✅ Verified KG integration ✅ Validated report generation ✅ Tested dashboard rendering KNOWLEDGE GRAPH ✅ Stored 5 facts ✅ Created relationships ✅ Enabled querying ✅ Documented integration PROJECT STATUS: ✅ COMPLETE AND OPERATIONAL Ready for: → Immediate production use → Further enhancement → Ecosystem integration → Feature expansion ================================================================================ For detailed information, see: • Quick Start: DELIVERABLES-SUMMARY.md • How To Use: SKILL-TRACKING-IMPLEMENTATION-GUIDE.md • Full Ref: SKILL-AND-DOCS-TRACKING.md • Navigation: SKILL-TRACKING-INDEX.md Generated: 2026-01-09 System Version: 1.0 Status: ✅ Complete ================================================================================