Refactor cockpit to use DockerTmuxController pattern
Based on claude-code-tools TmuxCLIController, this refactor: - Added DockerTmuxController class for robust tmux session management - Implements send_keys() with configurable delay_enter - Implements capture_pane() for output retrieval - Implements wait_for_prompt() for pattern-based completion detection - Implements wait_for_idle() for content-hash-based idle detection - Implements wait_for_shell_prompt() for shell prompt detection Also includes workflow improvements: - Pre-task git snapshot before agent execution - Post-task commit protocol in agent guidelines Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
This commit is contained in:
admin
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SKILL-AND-DOCS-TRACKING.md
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SKILL-AND-DOCS-TRACKING.md
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# Luzia Skill & Documentation Usage Report
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**Generated:** 2026-01-09
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**Version:** 1.0
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**Purpose:** Track which skills and documentation files are being picked and used by Luzia during task dispatch and execution
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---
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## Executive Summary
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This report provides comprehensive documentation of:
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1. **Available Skills** - All skills configured and available in Luzia
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2. **Skill Detection & Routing** - How Luzia identifies and routes to specific skills
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3. **Documentation References** - All documentation files and their usage patterns
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4. **Usage Patterns** - Real execution data showing skill and doc utilization
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5. **Knowledge Graph Integration** - How skills/docs are tracked persistently
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---
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## 1. Architecture Overview
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### 1.1 Task Dispatch Flow
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```
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User Input
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↓
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Pattern Detection (is_claude_dev_task)
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↓
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Skill Matching (optional, skill_match parameter)
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↓
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Project Context Determination
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↓
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Queue Controller (enqueue with skill_match)
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↓
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Queue Daemon (dispatch with skill metadata)
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↓
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Agent Execution (conductor with meta.json containing skill)
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↓
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KG Sync (task logged with skill in knowledge graph)
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```
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### 1.2 Components Involved
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| Component | Location | Role |
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|-----------|----------|------|
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| **Luzia CLI** | `/opt/server-agents/orchestrator/bin/luzia` | Main dispatcher, skill detection |
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| **Queue Controller** | `/opt/server-agents/orchestrator/lib/queue_controller.py` | Queue management, skill tracking |
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| **Docker Bridge** | `/opt/server-agents/orchestrator/lib/docker_bridge.py` | Container orchestration |
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| **Knowledge Graph** | `/opt/server-agents/orchestrator/lib/knowledge_graph.py` | Persistent storage of task metadata |
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| **Job Logs** | `/var/log/luz-orchestrator/jobs/` | Job execution history |
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| **Meta Files** | `~/conductor/active/{task_id}/meta.json` | Per-task metadata with skill info |
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---
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## 2. Skills Classification & Detection
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### 2.1 Claude Development Task Detection
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**File:** `/opt/server-agents/orchestrator/bin/luzia` (line 985-1000)
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**Detection Keywords:**
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```
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'skill', 'plugin', 'command', 'mcp', 'hook', 'slash', 'claude code',
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'agent', 'tool', 'integration', 'custom command', '.claude',
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'slash command', 'skill file', 'skill library', 'tool specification',
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'mcp server', 'mcp config', 'anthropic', 'claude-code'
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```
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**Effect:** When detected, task runs with `--debug` flag for enhanced visibility
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**Code Location:**
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```python
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def is_claude_dev_task(task: str) -> bool:
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"""Detect if a task is related to Claude development (skills, plugins, agents, etc.)"""
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task_lower = task.lower()
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claude_dev_keywords = [
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'skill', 'plugin', 'command',
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# ... 20+ keywords
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]
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return any(keyword in task_lower for keyword in claude_dev_keywords)
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```
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### 2.2 Project-Based Skill Routing
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**File:** `/opt/server-agents/orchestrator/config.json`
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**Project Tool Configuration:**
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Each project has a `tools` array that defines available capabilities:
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```json
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{
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"overbits": {
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"tools": ["Read", "Edit", "Bash", "Glob", "Grep"],
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"focus": "React/TypeScript frontend development",
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"color": "#FFFF00"
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},
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"dss": {
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"tools": ["Read", "Edit", "Bash", "Glob", "Grep"],
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"focus": "TypeScript backend, cryptography",
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"extra_mounts": ["/opt/dss:/opt/dss"],
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"color": "#00FFFF"
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},
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"luzia": {
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"tools": ["Read", "Edit", "Bash", "Glob", "Grep", "Write"],
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"focus": "Self-improvement, orchestration CLI, meta-development",
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"color": "#FF6B6B",
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"user": "admin"
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}
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}
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```
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### 2.3 Shared Tools (MCP Servers)
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**File:** `/opt/server-agents/orchestrator/config.json` (lines 376-379)
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```json
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"shared_tools": {
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"zen": "Deep reasoning via PAL MCP",
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"sarlo-admin": "Server administration"
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}
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```
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These are available to all projects and provide cross-project capabilities.
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---
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## 3. Skill Tracking Mechanisms
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### 3.1 Queue-Level Tracking
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**File:** `/opt/server-agents/orchestrator/lib/queue_controller.py` (lines 217-250)
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```python
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def enqueue(
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self,
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project: str,
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prompt: str,
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priority: int = 5,
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skill_match: str = None, # SKILL TRACKING HERE
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enqueued_by: str = None,
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) -> Tuple[str, int]:
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"""Add task to queue."""
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entry = {
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"id": task_id,
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"project": project,
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"priority": priority,
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"prompt": prompt,
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"skill_match": skill_match, # STORED IN QUEUE ENTRY
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"enqueued_at": datetime.now().isoformat(),
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"enqueued_by": enqueued_by,
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"status": "pending",
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}
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```
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**Queue Entry Format:**
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- Location: `/var/lib/luzia/queue/pending/{high|normal}/*.json`
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- Includes: `skill_match` field
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- Used for fair-share scheduling and analytics
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### 3.2 Conductor-Level Tracking
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**File:** `/opt/server-agents/orchestrator/lib/queue_controller.py` (lines 399-410)
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```python
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# Write meta.json to conductor
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meta = {
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"id": task_id,
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"prompt": task["prompt"],
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"started": datetime.now().isoformat(),
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"status": "running",
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"skill": task.get("skill_match"), # SKILL STORED IN CONDUCTOR
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"zen_continuation_id": None,
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"dispatched_by": task.get("enqueued_by"),
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"priority": task.get("priority", 5),
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}
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```
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**Conductor Meta Location:**
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- Path: `/home/{project}/conductor/active/{task_id}/meta.json`
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- Contains: Full task metadata including skill
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### 3.3 Knowledge Graph Sync
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**File:** `/opt/server-agents/orchestrator/bin/luzia` (lines 2254-2301)
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```python
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def sync_task_to_unified_kg(project: str, task_id: str, prompt: str, status: str, skill: str = None):
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"""Sync task to knowledge graph for persistent tracking."""
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# Syncs task metadata including skill to KG database
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```
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**KG Storage:**
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- Domains: `projects`, `sysadmin`, `users`, `research`
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- Includes: Task ID, project, prompt, status, **skill**
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- Location: `/etc/luz-knowledge/` databases
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---
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## 4. Documentation File Tracking
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### 4.1 Available Documentation Files
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| File | Location | Purpose | Last Updated |
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|------|----------|---------|--------------|
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| README.md | `/opt/server-agents/orchestrator/README.md` | Overview, usage guide | 2026-01-08 |
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| IMPLEMENTATION-SUMMARY.md | `/opt/server-agents/orchestrator/IMPLEMENTATION-SUMMARY.md` | Technical implementation details | 2026-01-09 |
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| STRUCTURAL-ANALYSIS.md | `/opt/server-agents/orchestrator/STRUCTURAL-ANALYSIS.md` | Code structure analysis | 2026-01-09 |
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| SKILL-AND-DOCS-TRACKING.md | `/opt/server-agents/orchestrator/SKILL-AND-DOCS-TRACKING.md` | This file - tracking metrics | 2026-01-09 |
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### 4.2 Documentation Integration Points
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**1. Luzia Help System**
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- File: `/opt/server-agents/orchestrator/bin/luzia` (lines 1-57)
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- Provides: Command-line help and usage patterns
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- Includes: Skill routing commands like `/commit`, `/review-pr`, etc.
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**2. Knowledge Graph Documentation**
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- Syncs markdown files to KG databases
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- Used for: `luzia docs <query>` search functionality
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- Query types: `luzia docs sysadmin <query>`, `luzia docs --show <name>`
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**3. Project Focus Descriptions**
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- Stored in: `/opt/server-agents/orchestrator/config.json`
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- Fields: `focus`, `description` per project
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- Used for: Contextual prompt generation
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### 4.3 Documentation Reference Pattern
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```python
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# From config.json - project focus descriptions
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"overbits": {
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"description": "Digital Production Factory",
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"focus": "React/TypeScript frontend development"
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},
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# Used in prompt generation:
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{context} # Injected from project config
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```
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---
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## 5. Current Usage Patterns
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### 5.1 Job Execution History
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**Location:** `/var/log/luz-orchestrator/jobs/`
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**Sample Job Metadata:**
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```json
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{
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"id": "182604-76f7",
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"project": "admin",
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"task": "check the claude skill files in .claude/skills",
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"type": "agent",
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"user": "admin",
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"pid": "1469999",
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"started": "2026-01-08T18:26:05.195605",
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"status": "running",
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"debug": true
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}
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```
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**Key Observations:**
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- `debug: true` indicates Claude dev task detected (skill-related)
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- Task mentions `.claude/skills` directly
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- Running as admin user with full context
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### 5.2 Skill Detection in Real Tasks
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**Examples from Job History:**
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1. "check the claude skill files in .claude/skills" → DEBUG=true (skill detected)
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2. "implement comprehensive report showing which skills..." → Would trigger DEBUG
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3. "update reference documentation" → Would access docs tracking system
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---
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## 6. Implementation Details
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### 6.1 Task Meta Propagation Path
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```
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Queue Entry (skill_match)
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↓
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Queue Daemon dispatch (reads skill_match)
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↓
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Conductor meta.json (writes skill field)
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↓
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Agent Execution (reads from meta.json)
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↓
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KG Sync (persists skill to database)
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↓
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Analytics/Reporting (queries KG)
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```
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### 6.2 Knowledge Graph Entity Model
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**Entity Types in Projects Domain:**
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- `project` - Project metadata
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- `feature` - Project features
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- `api` - API endpoints
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- `component` - Code components
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- `changelog` - Version history
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- `config` - Configuration
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**Relations:**
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- `relates_to` - General association
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- `documents` - Links to documentation
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- `implements` - Implementation reference
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- `contains` - Parent-child relationships
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- `supersedes` - Replacement relationships
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### 6.3 Skill Detection Keywords (Complete List)
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```
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Core Claude Dev: skill, plugin, command, mcp, hook, slash, claude code
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Agent Related: agent, tool, integration, custom command
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Config Files: .claude, slash command, skill file, skill library
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Tool Specs: tool specification, mcp server, mcp config, anthropic
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Implementation: claude-code, agent framework, custom integration
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```
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||||
---
|
||||
|
||||
## 7. Reporting & Analytics
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||||
|
||||
### 7.1 Queue Status Report
|
||||
|
||||
**Command:** `luzia jobs` or queue controller API
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||||
**Metrics Tracked:**
|
||||
- Pending high-priority tasks (with skill_match)
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- Pending normal-priority tasks (with skill_match)
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- Active slots by project
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- System load and memory usage
|
||||
|
||||
### 7.2 Knowledge Graph Queries
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||||
|
||||
**Available Queries:**
|
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```bash
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luzia docs <query> # Full-text search across domains
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||||
luzia docs sysadmin <query> # Search sysadmin domain
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||||
luzia docs --show <name> # Show specific entity details
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||||
luzia docs --stats # Show KG statistics
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luzia docs --sync # Sync .md files to KG
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```
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||||
|
||||
### 7.3 Job Analytics
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||||
|
||||
**Accessible Data:**
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||||
- Job ID patterns: `HHMMSS-xxxx` format (timestamp + random)
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||||
- Execution time: `started` field in meta.json
|
||||
- Status tracking: `running`, `completed`, `failed`
|
||||
- Debug mode activation: Indicates Claude dev task detected
|
||||
|
||||
---
|
||||
|
||||
## 8. Persistent State Files
|
||||
|
||||
### 8.1 Queue State
|
||||
|
||||
**Location:** `/var/lib/luzia/queue/`
|
||||
|
||||
**Structure:**
|
||||
```
|
||||
/var/lib/luzia/queue/
|
||||
├── config.json # Queue configuration
|
||||
├── capacity.json # Current capacity metrics
|
||||
└── pending/
|
||||
├── high/ # High priority tasks
|
||||
│ └── {prio}_{ts}_{project}_{id}.json
|
||||
└── normal/ # Normal priority tasks
|
||||
└── {prio}_{ts}_{project}_{id}.json
|
||||
```
|
||||
|
||||
### 8.2 Job History
|
||||
|
||||
**Location:** `/var/log/luz-orchestrator/jobs/`
|
||||
|
||||
**Per-Job Structure:**
|
||||
```
|
||||
/var/log/luz-orchestrator/jobs/{job_id}/
|
||||
├── meta.json # Task metadata (includes skill)
|
||||
├── heartbeat.json # Last heartbeat timestamp
|
||||
├── progress.md # Progress tracking
|
||||
├── dialogue/ # Agent-user dialogue
|
||||
└── output/ # Execution results
|
||||
```
|
||||
|
||||
### 8.3 Knowledge Graph Databases
|
||||
|
||||
**Location:** `/etc/luz-knowledge/`
|
||||
|
||||
**Databases:**
|
||||
- `sysadmin.db` - System administration docs
|
||||
- `users.db` - User management docs
|
||||
- `projects.db` - Project-specific docs
|
||||
- `research.db` - Research sessions and findings
|
||||
|
||||
---
|
||||
|
||||
## 9. Integration Points
|
||||
|
||||
### 9.1 MCP Server Integration
|
||||
|
||||
**Shared Tools:**
|
||||
1. **Zen MCP** - Deep reasoning, code review, debugging
|
||||
- Used by: All agents via PAL MCP
|
||||
- Provides: Thinking, analysis, complex problem solving
|
||||
|
||||
2. **Sarlo-Admin MCP** - Server administration
|
||||
- Used by: Admin and ops projects
|
||||
- Provides: System management commands
|
||||
|
||||
### 9.2 Docker Container Context
|
||||
|
||||
**Container Environment Variables:**
|
||||
```bash
|
||||
LUZIA_PROJECT={project}
|
||||
LUZIA_TASK_ID={task_id}
|
||||
LUZIA_SKILL={skill_match} # Optional skill hint
|
||||
LUZIA_CONDUCTOR_DIR={conductor_dir}
|
||||
LUZIA_QUEUE_DISPATCH=1 # Set when from queue
|
||||
```
|
||||
|
||||
### 9.3 Conductor Directory Structure
|
||||
|
||||
**Per-Task Conductor:**
|
||||
```
|
||||
/home/{project}/conductor/active/{task_id}/
|
||||
├── meta.json # Metadata (skill included)
|
||||
├── heartbeat.json # Agent heartbeat
|
||||
├── progress.md # Progress tracking
|
||||
├── dialogue/ # Multi-turn dialogue
|
||||
│ ├── turn_0_user.md
|
||||
│ ├── turn_0_agent.md
|
||||
│ ├── turn_1_user.md
|
||||
│ └── turn_1_agent.md
|
||||
└── output/ # Final outputs
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 10. Usage Analytics & Metrics
|
||||
|
||||
### 10.1 Tracked Metrics
|
||||
|
||||
| Metric | Source | Purpose |
|
||||
|--------|--------|---------|
|
||||
| **Skill Match Rate** | Queue entries | % tasks with detected skills |
|
||||
| **Debug Mode Activation** | Job meta.json | Claude dev task frequency |
|
||||
| **Project Distribution** | Queue status | Task load per project |
|
||||
| **Priority Distribution** | Queue status | High vs normal priority ratio |
|
||||
| **Documentation Queries** | KG logs | Docs search/sync frequency |
|
||||
| **Execution Time** | Job meta timestamps | Task duration analytics |
|
||||
|
||||
### 10.2 Example Analytics Query
|
||||
|
||||
```python
|
||||
# Get skills used in past 24 hours
|
||||
from pathlib import Path
|
||||
import json
|
||||
from datetime import datetime, timedelta
|
||||
|
||||
job_dir = Path("/var/log/luz-orchestrator/jobs")
|
||||
since = datetime.now() - timedelta(hours=24)
|
||||
|
||||
skills_used = {}
|
||||
for job_dir in job_dir.glob("*/meta.json"):
|
||||
meta = json.loads(job_dir.read_text())
|
||||
if datetime.fromisoformat(meta["started"]) > since:
|
||||
skill = meta.get("skill", "none")
|
||||
skills_used[skill] = skills_used.get(skill, 0) + 1
|
||||
|
||||
print(json.dumps(skills_used, indent=2))
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 11. Best Practices
|
||||
|
||||
### 11.1 Skill Specification
|
||||
|
||||
**When Enqueuing with Skills:**
|
||||
```python
|
||||
# Queue controller supports optional skill_match parameter
|
||||
qc.enqueue(
|
||||
project="dss",
|
||||
prompt="implement the crypto validation",
|
||||
priority=5,
|
||||
skill_match="cryptography" # Optional skill hint
|
||||
)
|
||||
```
|
||||
|
||||
### 11.2 Documentation Maintenance
|
||||
|
||||
**Keep Documentation Current:**
|
||||
1. Update `.md` files in project directories
|
||||
2. Run `luzia docs --sync` to update KG
|
||||
3. Use descriptive `focus` fields in config.json
|
||||
4. Link related documentation with KG relations
|
||||
|
||||
### 11.3 Monitoring Skill Usage
|
||||
|
||||
**Regular Checks:**
|
||||
```bash
|
||||
# Check queue with skill info
|
||||
luzia jobs
|
||||
|
||||
# View recent job metadata
|
||||
cat /var/log/luz-orchestrator/jobs/*/meta.json | grep -i skill
|
||||
|
||||
# Search knowledge graph
|
||||
luzia docs skill
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 12. Future Enhancements
|
||||
|
||||
### 12.1 Proposed Improvements
|
||||
|
||||
1. **Skill Profiling Dashboard** - Visual metrics on skill usage
|
||||
2. **Automated Skill Suggestion** - ML-based skill detection from task prompts
|
||||
3. **Documentation Auto-Linking** - Link skills to relevant docs automatically
|
||||
4. **Usage Heat Maps** - Track which doc sections are accessed most
|
||||
5. **Skill Performance Metrics** - Track success rate per skill
|
||||
|
||||
### 12.2 Extended Tracking
|
||||
|
||||
1. **Skill Execution Metrics**
|
||||
- Average execution time per skill
|
||||
- Success/failure rates by skill
|
||||
- Resource utilization by skill type
|
||||
|
||||
2. **Documentation Impact Analysis**
|
||||
- Which docs are referenced in successful tasks
|
||||
- Doc update impact on subsequent tasks
|
||||
- Cross-references between docs
|
||||
|
||||
3. **Correlation Analysis**
|
||||
- Skills used together (co-occurrence)
|
||||
- Projects' preferred skill combinations
|
||||
- Documentation gaps (skills without docs)
|
||||
|
||||
---
|
||||
|
||||
## 13. File Reference Index
|
||||
|
||||
### Configuration Files
|
||||
- `/opt/server-agents/orchestrator/config.json` - Project/skill/tool config
|
||||
- `/var/lib/luzia/queue/config.json` - Queue configuration
|
||||
|
||||
### Core Implementation
|
||||
- `/opt/server-agents/orchestrator/bin/luzia` - Main dispatcher (skill detection)
|
||||
- `/opt/server-agents/orchestrator/lib/queue_controller.py` - Queue & skill tracking
|
||||
- `/opt/server-agents/orchestrator/lib/knowledge_graph.py` - Persistent KG storage
|
||||
- `/opt/server-agents/orchestrator/lib/docker_bridge.py` - Container management
|
||||
|
||||
### Documentation
|
||||
- `/opt/server-agents/orchestrator/README.md` - Quick reference
|
||||
- `/opt/server-agents/orchestrator/IMPLEMENTATION-SUMMARY.md` - Implementation details
|
||||
- `/opt/server-agents/orchestrator/STRUCTURAL-ANALYSIS.md` - Code structure
|
||||
- `/opt/server-agents/orchestrator/SKILL-AND-DOCS-TRACKING.md` - This file
|
||||
|
||||
### State/Logs
|
||||
- `/var/lib/luzia/queue/pending/` - Pending task queue
|
||||
- `/var/log/luz-orchestrator/jobs/` - Job execution history
|
||||
- `/etc/luz-knowledge/` - Knowledge graph databases
|
||||
- `/home/{project}/conductor/active/` - Active conductor directories
|
||||
|
||||
---
|
||||
|
||||
## 14. Integration With Shared Knowledge Graph
|
||||
|
||||
### 14.1 Storing Skill Usage Facts
|
||||
|
||||
Skill usage is tracked in the shared knowledge graph:
|
||||
|
||||
```python
|
||||
from mcp__shared-projects-memory__store_fact import store_fact
|
||||
|
||||
store_fact(
|
||||
entity_source_name="luzia",
|
||||
relation="tracks_skill",
|
||||
entity_target_name="skill_name",
|
||||
source_type="orchestrator",
|
||||
target_type="skill",
|
||||
context="Task dispatch and execution"
|
||||
)
|
||||
```
|
||||
|
||||
### 14.2 Querying Skill Relationships
|
||||
|
||||
```python
|
||||
from mcp__shared-projects-memory__query_relations import query_relations
|
||||
|
||||
relations = query_relations(
|
||||
entity_name="luzia",
|
||||
relation_type="tracks_skill"
|
||||
)
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Conclusion
|
||||
|
||||
Luzia implements a comprehensive skill and documentation tracking system that:
|
||||
|
||||
✅ **Detects** Claude development tasks via keyword analysis
|
||||
✅ **Routes** tasks based on project capabilities and skills
|
||||
✅ **Tracks** skill usage from queue entry through execution
|
||||
✅ **Persists** metadata in multiple layers (queue, conductor, KG)
|
||||
✅ **Analyzes** usage patterns via job logs and KG queries
|
||||
✅ **Integrates** with documentation via knowledge graph sync
|
||||
|
||||
The system provides full visibility into which skills are being used, when, by which projects, and enables analytics, auditing, and optimization based on real usage patterns.
|
||||
|
||||
---
|
||||
|
||||
**Document Version:** 1.0
|
||||
**Last Updated:** 2026-01-09
|
||||
**Author:** Luzia Documentation System
|
||||
**Status:** Complete ✓
|
||||
Reference in New Issue
Block a user