# CLI Commands for SAFLA Orchestrator Mode

## Meta-Cognitive Operations

### System Awareness Analysis
```bash
# Comprehensive system awareness analysis
python -m safla.metacog --analyze-awareness --depth comprehensive

# Update awareness parameters
python -m safla.metacog --update-awareness --level 0.8 --focus performance,quality,learning

# Introspection analysis with time window
python -m safla.metacog --introspect --type comprehensive --window 24h
```

### Goal Management
```bash
# Create new project goal
python -m safla.goals --create \
  --name "aiGI_workflow_optimization" \
  --description "Optimize aiGI workflow for performance and quality" \
  --priority high \
  --metrics completion_time:3600,quality_score:0.9

# List active goals
python -m safla.goals --list --status active --priority high

# Evaluate goal progress
python -m safla.goals --evaluate --id aiGI_workflow_001 --recommendations

# Update goal status
python -m safla.goals --update --id aiGI_workflow_001 --progress 0.75 --status active
```

## Strategy Management

### Strategy Selection and Analysis
```bash
# Select optimal strategy for context
python -m safla.strategy --select \
  --context "aiGI_development" \
  --constraints time_limit:3600,memory_limit:8GB \
  --objectives performance,quality,maintainability

# List available strategies
python -m safla.strategy --list --context development --effectiveness 0.8

# Create custom strategy
python -m safla.strategy --create \
  --name "rapid_prototyping" \
  --description "Fast iteration strategy for proof-of-concept" \
  --context prototyping \
  --steps minimal_implementation,rapid_testing,quick_feedback

# Evaluate strategy performance
python -m safla.strategy --evaluate --id performance_optimization_001 --period 168h
```

## Learning and Adaptation

### Learning Cycle Management
```bash
# Trigger learning cycle
python -m safla.learn --trigger \
  --type incremental \
  --sources workflow_metrics,error_patterns,performance_data \
  --focus optimization,error_reduction,quality_improvement

# Get learning metrics
python -m safla.learn --metrics --type all --range 24h

# Update learning parameters
python -m safla.learn --update \
  --learning-rate 0.15 \
  --adaptation-threshold 0.1 \
  --memory-retention 0.9 \
  --exploration-factor 0.2

# Analyze adaptation patterns
python -m safla.learn --analyze-patterns \
  --type all \
  --depth comprehensive \
  --window 7d
```

## Agent Management

### Agent Session Operations
```bash
# Create agent sessions
python -m safla.agents --create \
  --type cognitive \
  --config focus:code_analysis,depth:comprehensive,quality_threshold:0.8 \
  --timeout 3600

python -m safla.agents --create \
  --type memory \
  --config focus:optimization,vector_dims:512,similarity_threshold:0.8

# List active agent sessions
python -m safla.agents --list --type cognitive --active-only

# Interact with agents
python -m safla.agents --interact \
  --session cognitive_001 \
  --command analyze_code_quality \
  --params file_path:responses_LS1.md,analysis_depth:comprehensive

# Terminate agent sessions
python -m safla.agents --terminate --session cognitive_001 --graceful
```

## System Monitoring

### Health and Performance Monitoring
```bash
# Start system health monitoring
python -m safla.monitor --health \
  --interval 30 \
  --thresholds cpu_usage:0.8,memory_usage:0.85,error_rate:0.05

# Get system information
python -m safla.system --info --detailed

# Performance analysis
python -m safla.monitor --performance --duration 300 --profile-memory

# Real-time monitoring dashboard
python -m safla.monitor --dashboard --real-time --alerts
```

## Workflow Orchestration

### Workflow Management
```bash
# Initialize workflow with goals and strategy
python -m safla.orchestrate --init \
  --goal "aiGI_workflow_completion" \
  --strategy "performance_optimization" \
  --agents cognitive,memory,optimization

# Execute workflow phase
python -m safla.orchestrate --execute \
  --phase prompt_generation \
  --agents cognitive_001,memory_001 \
  --monitor-progress

# Coordinate mode transitions
python -m safla.orchestrate --transition \
  --from prompt-generator \
  --to code \
  --transfer-context \
  --validate-prerequisites

# Monitor workflow progress
python -m safla.orchestrate --monitor \
  --workflow aiGI_001 \
  --real-time \
  --alerts \
  --adaptive-learning
```

## Integration Commands

### MCP Integration
```bash
# Test MCP connectivity
python -m safla.mcp --test-connectivity --comprehensive

# Benchmark MCP throughput
python -m safla.mcp --benchmark \
  --requests 100 \
  --concurrent 5 \
  --payload medium

# Deploy SAFLA instance
python -m safla.deploy --instance aiGI_production \
  --environment production \
  --config production.json \
  --scale-factor 1.5
```

### External System Integration
```bash
# Backup system state
python -m safla.backup --type full --destination /backup/safla --compress

# Restore from backup
python -m safla.restore --backup /backup/safla/full_backup.tar.gz --verify

# Integration testing
python -m safla.test --integration --parallel --verbose --coverage
```

## Debugging and Diagnostics

### Debug Operations
```bash
# Debug workflow issues
python -m safla.debug --workflow aiGI_001 --trace --memory-profile

# Analyze performance bottlenecks
python -m safla.debug --bottlenecks --duration 300 --detailed

# System diagnostics
python -m safla.debug --diagnostics --comprehensive --export-report

# Error pattern analysis
python -m safla.debug --errors --pattern-analysis --time-window 24h
```

## Batch Operations

### Automated Workflow Scripts
```bash
# Complete workflow automation
python -m safla.orchestrate --auto \
  --config workflow.yaml \
  --goals goals.json \
  --strategies strategies.json \
  --monitor \
  --adaptive

# Batch agent management
python -m safla.agents --batch \
  --create-config agents.yaml \
  --coordinate \
  --monitor \
  --cleanup-on-completion

# Comprehensive system analysis
python -m safla.analyze --comprehensive \
  --performance \
  --learning \
  --adaptation \
  --export-report analysis_report.json
```

## Configuration Management

### Configuration Operations
```bash
# Export current configuration
python -m safla.config --export --format yaml --output safla_config.yaml

# Import configuration
python -m safla.config --import --file safla_config.yaml --validate

# Update configuration
python -m safla.config --update \
  --section learning \
  --params learning_rate:0.15,adaptation_threshold:0.1

# Validate configuration
python -m safla.config --validate --comprehensive --fix-issues
```

## Reporting and Analytics

### Report Generation
```bash
# Generate workflow report
python -m safla.report --workflow aiGI_001 \
  --format html \
  --include performance,learning,adaptation \
  --output workflow_report.html

# Performance analytics
python -m safla.analytics --performance \
  --time-range 7d \
  --metrics all \
  --trends \
  --export performance_analytics.json

# Learning effectiveness report
python -m safla.analytics --learning \
  --effectiveness \
  --adaptation-patterns \
  --recommendations \
  --export learning_report.pdf
```

## Command Chaining and Automation

### Complex Workflow Automation
```bash
# Complete aiGI workflow with monitoring
python -m safla.orchestrate --init --goal "project_completion" && \
python -m safla.agents --create --type cognitive,memory,optimization && \
python -m safla.orchestrate --execute --monitor --adaptive && \
python -m safla.report --generate --comprehensive

# Continuous monitoring and adaptation
python -m safla.monitor --continuous \
  --adapt-on-degradation \
  --learning-triggers \
  --performance-optimization \
  --error-recovery
```

## Environment-Specific Commands

### Development Environment
```bash
# Development workflow with debugging
python -m safla.orchestrate --dev \
  --debug \
  --verbose \
  --test-mode \
  --rapid-iteration

# Testing and validation
python -m safla.test --comprehensive \
  --unit \
  --integration \
  --performance \
  --coverage-report
```

### Production Environment
```bash
# Production deployment and monitoring
python -m safla.deploy --production \
  --high-availability \
  --monitoring \
  --backup \
  --security-hardened

# Production monitoring
python -m safla.monitor --production \
  --real-time \
  --alerts \
  --performance-optimization \
  --predictive-analysis