luzia/ENHANCEMENTS_INDEX.md

Luzia Orchestrator Enhancements - Quick Reference Index

Status: ✅ Production Ready Version: 2.0 Date: January 9, 2026

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📚 Documentation Files

File	Purpose	Size
IMPROVEMENTS.md	Comprehensive guide with architecture, usage, and best practices	19 KB
IMPLEMENTATION_SUMMARY.md	Quick reference on what was built and getting started	12 KB
COMPLETION_REPORT.txt	Verification and metrics report	6 KB

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🔧 Implementation Modules

1. PromptAugmentor

File: lib/prompt_augmentor.py (314 lines)

Augments prompts with rich context.

from lib.prompt_augmentor import PromptAugmentor

augmentor = PromptAugmentor(project_config, tools_available)
enhanced = augmentor.augment(prompt, task_context)

Key Features:

• System context injection
• Tool documentation loading
• Project best practices
• Task continuation context
• Structured output guidance

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2. ToolAutoLoader

File: lib/tool_auto_loader.py (344 lines)

Dynamically discovers and recommends tools.

from lib.tool_auto_loader import ToolAutoLoader

loader = ToolAutoLoader()
tools = loader.discover_tools(project_config)
recommendations = loader.recommend_tools(task, tools)

Key Features:

• Dynamic tool discovery
• Smart recommendations
• Usage tracking
• Documentation generation
• Caching system

---

3. KnownIssuesDetector

File: lib/known_issues_detector.py (411 lines)

Detects and suggests fixes for known issues.

from lib.known_issues_detector import KnownIssuesDetector

detector = KnownIssuesDetector()
issues = detector.detect_issues(output, error, project)
report = detector.format_issue_report(issues)

Key Features:

• 15+ pre-configured patterns
• Auto-fix capability
• Severity classification
• Statistics tracking
• Pattern learning

Pre-Configured Issues:

• Container/Docker issues
• Permission denied errors
• Module/dependency not found
• Build failures
• Configuration corruption
• Network problems
• Memory issues
• Type errors
• File not found errors

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4. WebSearchIntegrator

File: lib/web_search_integrator.py (402 lines)

Manages web references and learned solutions.

from lib.web_search_integrator import WebSearchIntegrator

integrator = WebSearchIntegrator()
should_search, query = integrator.should_search(task, error)
learned = integrator.search_learned_solutions(query)
integrator.learn_solution(problem, solution, refs, tags, confidence)

Key Features:

• Smart search triggers
• Technology stack detection
• Learning database
• Reference management
• Solution confidence scoring

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5. FlowIntelligence

File: lib/flow_intelligence.py (494 lines)

Tracks multi-step task execution and provides continuation context.

from lib.flow_intelligence import FlowIntelligence

flow = FlowIntelligence()
task_id = flow.create_flow(task_desc, project, steps)
flow.start_step(task_id, step_name)
flow.complete_step(task_id, step_name, output, error)
context = flow.get_context_for_continuation(task_id)
suggestions = flow.suggest_next_steps(task_id)

Key Features:

• Multi-step task tracking
• Step state management
• Continuation context
• Next-step suggestions
• Follow-up recommendations
• Flow history export

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6. OrchestratorEnhancements

File: lib/orchestrator_enhancements.py (329 lines)

Unified coordinator for all enhancement modules.

from lib.orchestrator_enhancements import OrchestratorEnhancements

enhancements = OrchestratorEnhancements(config)
enhancements.initialize_for_project(project_name, project_config)

# Enhance prompts
enhanced, metadata = enhancements.enhance_prompt(prompt, project)

# Detect issues
detected, report = enhancements.detect_issues_in_output(output, error)

# Track tasks
task_id = enhancements.start_task_flow(task_desc, project, steps)
context = enhancements.continue_task(task_id, project)
suggestions = enhancements.complete_task(task_id, result)

# Get status
status = enhancements.get_orchestration_status()

Key Features:

• High-level unified API
• Project-aware initialization
• Analytics export
• Real-time status
• Integration helpers

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🚀 Quick Start

Installation

Files are already deployed to:

• /opt/server-agents/orchestrator/lib/ (all 6 modules)
• /opt/server-agents/orchestrator/IMPROVEMENTS.md (guide)

Basic Usage

import json
from lib.orchestrator_enhancements import OrchestratorEnhancements

# Load config
with open("/opt/server-agents/orchestrator/config.json") as f:
    config = json.load(f)

# Initialize
enhancements = OrchestratorEnhancements(config)
enhancements.initialize_for_project("overbits", config["projects"]["overbits"])

# Enhance prompt
prompt = "Fix the TypeScript build error"
enhanced, metadata = enhancements.enhance_prompt(prompt, "overbits")
print(enhanced)

# Detect issues
output = "error: cannot find module..."
detected, report = enhancements.detect_issues_in_output(output, "")
print(report)

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📊 Analytics

Available Reports

1. Flow Statistics

   • Total/active/completed tasks
   • Step completion rates
   • Task duration analysis

2. Issue Statistics

   • Detection frequency by pattern
   • Fix success rates
   • Severity distribution

3. Tool Usage

   • Most-used tools
   • Recommendation accuracy
   • Tool effectiveness

4. Learning Database

   • Learned solutions count
   • Confidence distribution
   • Topic coverage

Export Analytics

from pathlib import Path

enhancements.export_all_analytics(Path("./analytics"))
# Creates: flows.json, issue_stats.json, learning.json, tool_usage.json

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🔍 Configuration

Minimal Config

{
  "projects": {
    "example": {
      "path": "/home/example",
      "tools": ["Read", "Write", "Bash"],
      "knowledge": {
        "framework": "React",
        "language": "TypeScript"
      }
    }
  }
}

Optional: Known Issues Database

Create /opt/server-agents/orchestrator/config/known_issues.json:

{
  "patterns": [
    {
      "name": "custom_issue",
      "description": "Issue description",
      "error_patterns": ["pattern1", "pattern2"],
      "fix": "How to fix",
      "auto_fixable": true,
      "fix_command": "command",
      "severity": "error"
    }
  ]
}

---

📈 Performance

Operation	Time	Memory
Prompt augmentation	<100ms	-
Tool discovery	<50ms*	~100KB*
Issue detection	~20ms	-
Flow creation	<10ms	~10KB per task
Recommendations	<50ms	-

*cached

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🎯 Common Use Cases

Use Case 1: Enhance Task Prompt

enhanced, meta = enhancements.enhance_prompt(prompt, "overbits")
# Includes: context, tools, best practices, documentation

Use Case 2: Auto-Fix Issues

detected, report = enhancements.detect_issues_in_output(output, error)
for issue in detected:
    if issue.auto_fixable:
        fix_cmd = detector.get_fix_command(issue)
        # Run fix command

Use Case 3: Multi-Step Task

task_id = enhancements.start_task_flow(
    "Implement feature",
    "overbits",
    ["Analyze", "Design", "Implement", "Test"]
)
# ...execute steps...
context = enhancements.continue_task(task_id, "overbits")
suggestions = enhancements.complete_task(task_id, result)

Use Case 4: Learn Solution

enhancements.record_learned_solution(
    problem="TypeScript type error",
    solution="Add proper type definitions",
    references=["https://..."],
    tags=["typescript", "types"],
    confidence=0.95
)

---

🔗 Integration with Main Orchestrator

Before Subagent Call

# Enhance prompt
enhanced_prompt, metadata = enhancements.enhance_prompt(
    original_prompt,
    project_name,
    task_context  # optional
)

# Use enhanced_prompt with subagent
result = run_subagent(project, enhanced_prompt)

After Task Completion

# Detect issues
detected, report = enhancements.detect_issues_in_output(
    task_output,
    task_error,
    project_name
)

if detected:
    # Handle or report issues
    for issue in detected:
        suggest_fix(issue)

For Multi-Step Tasks

# Create flow
task_id = enhancements.start_task_flow(
    task_description,
    project,
    list_of_steps
)

# During execution
enhancements.update_task_step(task_id, step_name, output, error)

# On completion
suggestions = enhancements.complete_task(task_id, result)

---

🧪 Testing

Quick Tests

# Test PromptAugmentor
python3 -c "
from lib.prompt_augmentor import PromptAugmentor
config = {'name': 'test', 'path': '/tmp', 'focus': 'testing'}
aug = PromptAugmentor(config, ['Read', 'Write'])
print(aug.augment('Test task'))
"

# Test IssueDetector
python3 -c "
from lib.known_issues_detector import KnownIssuesDetector
detector = KnownIssuesDetector()
issues = detector.detect_issues('ModuleNotFoundError: No module named test')
print(detector.format_issue_report(issues))
"

# Test FlowIntelligence
python3 -c "
from lib.flow_intelligence import FlowIntelligence
flow = FlowIntelligence()
task_id = flow.create_flow('Test task', 'admin', ['step1', 'step2']).task_id
print(f'Created task: {task_id}')
"

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📖 For More Information

• Comprehensive Guide: See IMPROVEMENTS.md
• Quick Reference: See IMPLEMENTATION_SUMMARY.md
• Metrics: See COMPLETION_REPORT.txt
• Knowledge Graph: Registered with shared projects knowledge base

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✅ Checklist for Integration

• Review IMPROVEMENTS.md architecture section
• Test modules with sample prompts
• Verify issue detection works
• Check flow tracking functionality
• Configure known issues database (optional)
• Set up analytics export
• Monitor performance metrics
• Share learned solutions across projects

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🚀 Next Steps

1. Today: Review documentation and run tests
2. This Week: Integrate into main orchestrator
3. This Month: Monitor and optimize

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Version: 2.0 Status: ✅ Production Ready Contact: Luzia Orchestrator Project