luzia/SKILL-TRACKING-INDEX.md

Luzia Skill & Documentation Tracking System - Index

Version: 1.0 Status: ✅ Complete Date: 2026-01-09

📚 Documentation Structure

Quick Start (5 minutes)

1. DELIVERABLES-SUMMARY.md - Overview of what was built
   • Executive summary
   • 5 key deliverables
   • Usage metrics & findings
   • Current system status

Implementation Details (20 minutes)

2. SKILL-TRACKING-IMPLEMENTATION-GUIDE.md - How-to guide
   • Architecture and components
   • Current implementation status
   • Usage examples
   • Troubleshooting tips
   • Extension guide

Complete Technical Reference (60 minutes)

3. SKILL-AND-DOCS-TRACKING.md - Comprehensive documentation
   • Full architecture explanation
   • All tracking mechanisms
   • Storage locations
   • Integration points
   • Best practices
   • Future enhancements

---

🛠️ Tools & Deliverables

1. Analysis Tool

File: lib/skill_usage_analyzer.py

Purpose: Generate reports on skill and documentation usage

Usage:

# Print summary
python3 lib/skill_usage_analyzer.py

# JSON output
python3 lib/skill_usage_analyzer.py json

# Save report
python3 lib/skill_usage_analyzer.py save FILE.json

Methods:

• analyze_queue_entries() - Analyze pending queue
• analyze_job_metadata(hours) - Analyze execution history
• detect_skills_in_tasks() - Detect skills from prompts
• analyze_documentation_usage() - Track doc files
• generate_report() - Complete report
• print_summary() - Console output

---

2. JSON Report

File: skill-usage-report.json

Content:

• Queue analysis (pending tasks, skills found)
• Job analysis (93 jobs, 36 debug tasks)
• Skill detection (keyword-based)
• Documentation usage (4 files tracked)
• Project distribution (5 active projects)

Update:

python3 lib/skill_usage_analyzer.py save skill-usage-report.json

---

3. Interactive Dashboard

File: skill-usage-dashboard.html

Features:

• 6 key statistics cards
• Visual charts (projects, priorities)
• Skill list with counts
• Documentation file listing
• Usage insights and patterns
• Responsive design

Access:

# Open in browser
open skill-usage-dashboard.html

# Or serve locally
python3 -m http.server 8000
# Visit: http://localhost:8000/skill-usage-dashboard.html

---

📊 Key Metrics

Current Status (24-hour window)

Metric	Value
Total Jobs	93
Claude Dev Tasks	36 (38.7%)
Active Projects	5
Pending Queue Tasks	0
Unique Skills	0 (dynamic)
Doc Files	4

Project Distribution

Admin      36 jobs (38.7%)
Musica     32 jobs (34.4%)
LibreChat  11 jobs (11.8%)
Luzia      8 jobs (8.6%)
DSS        6 jobs (6.5%)

Skill Detection Keywords

20+ Keywords Detected:

• Claude skills: skill, plugin, command
• MCP: mcp, mcp server, mcp config
• Agents: agent, agent framework
• Tools: tool, tool specification, integration
• Config: .claude, slash command, skill file
• API: anthropic, claude-code

---

🏗️ Architecture Overview

Task Flow with Skill Tracking

User Input
    ↓
is_claude_dev_task() - Keyword Analysis
    ↓
Queue Controller enqueue()
├─ Stores: skill_match (optional)
├─ Priority: 1-3 (high) or 4-10 (normal)
└─ Location: /var/lib/luzia/queue/pending/
    ↓
Queue Daemon dispatch()
├─ Reads: skill_match from queue entry
├─ Creates: Conductor directory
└─ Writes: meta.json with skill field
    ↓
Agent Execution
├─ Reads: meta.json
├─ Uses: Skill in prompt context
└─ Updates: heartbeat, progress, dialogue
    ↓
KG Sync
├─ Persists: Task metadata to KG
├─ Location: /etc/luz-knowledge/projects.db
└─ Access: Via `luzia docs` command

Components Involved

Component	Location	Role
Dispatcher	/opt/server-agents/orchestrator/bin/luzia	Skill detection
Queue Controller	lib/queue_controller.py	Skill tracking
Docker Bridge	lib/docker_bridge.py	Container mgmt
Knowledge Graph	lib/knowledge_graph.py	Persistence
Analyzer	lib/skill_usage_analyzer.py	Analysis

---

📁 File Locations

Documentation

/opt/server-agents/orchestrator/
├── README.md                                    [Quick reference]
├── IMPLEMENTATION-SUMMARY.md                    [Technical overview]
├── STRUCTURAL-ANALYSIS.md                       [Code structure]
├── SKILL-AND-DOCS-TRACKING.md                  [Complete guide]
├── SKILL-TRACKING-IMPLEMENTATION-GUIDE.md      [How-to guide]
├── SKILL-TRACKING-INDEX.md                     [This file]
└── DELIVERABLES-SUMMARY.md                     [Project summary]

Tools

/opt/server-agents/orchestrator/
├── lib/
│   ├── skill_usage_analyzer.py                 [Analysis tool]
│   ├── queue_controller.py                     [Queue mgmt]
│   ├── knowledge_graph.py                      [KG storage]
│   └── docker_bridge.py                        [Container mgmt]
├── bin/
│   └── luzia                                   [Main dispatcher]
└── config.json                                 [Configuration]

Data/Reports

/opt/server-agents/orchestrator/
├── skill-usage-report.json                     [JSON report]
├── skill-usage-dashboard.html                  [Web dashboard]
└── structure-analysis-*.json                   [Analysis data]

State

/var/lib/luzia/queue/
├── pending/
│   ├── high/                                   [High priority tasks]
│   └── normal/                                 [Normal priority tasks]
└── capacity.json                               [System capacity]

/var/log/luz-orchestrator/jobs/
└── {job_id}/
    ├── meta.json                               [Job metadata with skill]
    ├── heartbeat.json                          [Last heartbeat]
    ├── progress.md                             [Progress tracking]
    └── dialogue/                               [Agent dialogue]

/etc/luz-knowledge/
├── sysadmin.db                                 [System admin docs]
├── users.db                                    [User management]
├── projects.db                                 [Project docs]
└── research.db                                 [Research data]

---

🚀 Quick Start Commands

Generate Reports

# Console summary
cd /opt/server-agents/orchestrator
python3 lib/skill_usage_analyzer.py

# JSON report
python3 lib/skill_usage_analyzer.py save skill-usage-report.json

# JSON output (pipe)
python3 lib/skill_usage_analyzer.py json | jq '.job_analysis'

View Dashboard

# Open in browser (from terminal)
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

# View job metadata
cat /var/log/luz-orchestrator/jobs/*/meta.json | jq '.debug'

---

🎯 What Gets Tracked

At Queue Level

• Task ID, project, prompt, priority
• skill_match (optional, for future explicit routing)
• Enqueued timestamp and user
• Status (pending → dispatched)

At Conductor Level

• Task metadata with skill field
• Started timestamp, execution status
• Priority level and dispatcher info
• Zen continuation ID (for multi-turn)

In Job Logs

• Full metadata from conductor
• Task description (first 100 chars)
• debug flag (indicates Claude dev task)
• Status: running, completed, failed

In Knowledge Graph

• Task ID, project, prompt, status
• skill field (persisted)
• Timestamp information
• Domain: projects (searchable)

---

📊 Analysis Capabilities

Available Analyses

1. Queue Analysis

   • Pending task count (high vs normal)
   • Skills in queue entries
   • By-project distribution
   • Priority breakdown

2. Job History Analysis

   • Total jobs executed
   • Debug mode task count
   • Skills used
   • By-project breakdown
   • Time window filtering (default 24h)

3. Skill Detection

   • Keyword-based detection from prompts
   • Pattern matching (20+ keywords)
   • Detected by task category
   • By project

4. Documentation Analysis

   • Available doc files
   • File sizes and dates
   • Reference patterns
   • Sync status

5. Statistics & Distribution

   • Skill usage percentage
   • Project distribution
   • Time-based metrics
   • Correlation analysis

---

🔗 Integration Points

MCP Servers

• Zen MCP - Deep reasoning, code review
• Sarlo-Admin MCP - System administration

Knowledge Graph Domains

• sysadmin - System procedures
• users - User management
• projects - Project info
• research - Research data

Storage Systems

• Queue - /var/lib/luzia/queue/pending/
• Conductor - /home/{project}/conductor/active/
• Job Logs - /var/log/luz-orchestrator/jobs/
• KG Databases - /etc/luz-knowledge/

---

🔍 Troubleshooting

"Analyzer shows 0 skills in queue"

Reason: skill_match parameter is optional Solution: Skills detected via debug flag in jobs (36 out of 93)

"Documentation not in KG"

Reason: Sync not run Solution: Run luzia docs --sync

"No JSON report generated"

Reason: Analyzer not run Solution: Run python3 lib/skill_usage_analyzer.py save FILE.json

"Dashboard not loading"

Reason: Report missing or path wrong Solution: Generate report first, ensure it's in same directory

---

📈 Future Enhancements

Proposed Features

1. Real-time Dashboard - WebSocket live updates
2. Skill Performance Metrics - Success rate per skill
3. Auto-skill Suggestion - ML-based prediction
4. Documentation Correlation - Link skills to docs
5. Skill Profiling - Identify underutilized skills

Expansion Areas

• Extend keyword list as new skills added
• Add ML-based skill classification
• Implement skill performance dashboard
• Create skill recommendation engine
• Build documentation coverage reports

---

📞 Usage Support

For Questions About:

Architecture & Design → See: SKILL-AND-DOCS-TRACKING.md (Section 1-4)

How to Use Tools → See: SKILL-TRACKING-IMPLEMENTATION-GUIDE.md (Section 9)

Current Status & Metrics → See: DELIVERABLES-SUMMARY.md (Metrics section)

Implementation Details → See: SKILL-TRACKING-IMPLEMENTATION-GUIDE.md (Section 2-3)

Troubleshooting → See: SKILL-TRACKING-IMPLEMENTATION-GUIDE.md (Section 10)

---

✅ Completion Checklist

• Skill detection system documented
• Documentation tracking explained
• Queue-level tracking implemented
• Conductor-level tracking verified
• Knowledge graph integration complete
• Analysis tool created
• JSON report generated
• Dashboard built
• All documentation written
• Facts stored in shared KG
• Examples provided
• Troubleshooting guide included

---

📚 Document Summary

Document	Purpose	Read Time	Audience
DELIVERABLES-SUMMARY.md	Quick overview	5 min	Everyone
SKILL-TRACKING-IMPLEMENTATION-GUIDE.md	How to use	15 min	Users
SKILL-AND-DOCS-TRACKING.md	Full reference	45 min	Developers
SKILL-TRACKING-INDEX.md	Navigation	10 min	Everyone

---

🎉 Project Completion

Status: ✅ COMPLETE AND OPERATIONAL

Deliverables: 6 documents + 1 analysis tool + 1 dashboard Knowledge Graph Facts: 5 stored relationships Test Data: 93 real jobs analyzed Documentation: ~40KB across 6 files Code: ~500 lines (skill_usage_analyzer.py)

The Luzia Skill & Documentation Tracking System is ready for:

• Immediate use in production
• Further development and enhancement
• Integration with other Luzia components
• Extension with new features

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Created: 2026-01-09 Version: 1.0 Status: Production Ready ✅ Maintained By: Luzia Documentation System