# Scorer Mode CLI Commands Documentation

## Overview

The Scorer mode provides comprehensive CLI integration for quantitative evaluation, scoring, and ranking operations within the SAFLA-aiGI framework. This document details the CLI commands, workflows, and integration patterns for implementing effective scoring through command-line interfaces.

## Core CLI Command Categories

### 1. Performance Scoring Commands

#### System Performance Scoring
```bash
# Basic system performance scoring
python -m safla.cli score performance
python -m safla.cli score performance --duration=300 --include_trends=true
python -m safla.cli score performance --comprehensive=true --baseline_comparison=true

# Memory performance scoring
python -m safla.cli score memory
python -m safla.cli score memory --test_size=100MB --patterns=all
python -m safla.cli score memory --stress_test=true --detailed_analysis=true

# Vector operations performance scoring
python -m safla.cli score vector_ops
python -m safla.cli score vector_ops --vector_count=10000 --dimensions=1024
python -m safla.cli score vector_ops --gpu_acceleration=true --benchmark_mode=true

# MCP protocol performance scoring
python -m safla.cli score mcp_performance
python -m safla.cli score mcp_performance --connections=50 --payload_size=large
python -m safla.cli score mcp_performance --throughput_test=true --latency_analysis=true
```

#### Performance Comparison Commands
```bash
# Compare current performance against baselines
python -m safla.cli compare performance --baseline=production --current=staging
python -m safla.cli compare performance --historical_period=30d --trend_analysis=true
python -m safla.cli compare performance --multiple_baselines=true --statistical_analysis=true

# Performance regression detection
python -m safla.cli detect performance_regression --threshold=5 --alert_level=warning
python -m safla.cli detect performance_regression --continuous_monitoring=true --auto_alert=true

# Performance optimization scoring
python -m safla.cli score optimization_effectiveness
python -m safla.cli score optimization_effectiveness --before_after_comparison=true
python -m safla.cli score optimization_effectiveness --roi_calculation=true
```

### 2. Quality Assessment Commands

#### Code Quality Scoring
```bash
# Comprehensive code quality scoring
python -m safla.cli score code_quality
python -m safla.cli score code_quality --include_security=true --detailed=true
python -m safla.cli score code_quality --complexity_analysis=true --maintainability_check=true

# Test coverage and quality scoring
python -m safla.cli score test_coverage
python -m safla.cli score test_coverage --minimum_threshold=80 --include_integration=true
python -m safla.cli score test_coverage --quality_metrics=true --trend_analysis=true

# Documentation quality scoring
python -m safla.cli score documentation
python -m safla.cli score documentation --completeness_check=true --accuracy_validation=true
python -m safla.cli score documentation --user_feedback_integration=true
```

#### Integration and System Quality
```bash
# Integration test scoring
python -m safla.cli score integration_tests
python -m safla.cli score integration_tests --parallel_execution=true --comprehensive=true
python -m safla.cli score integration_tests --reliability_assessment=true

# System reliability scoring
python -m safla.cli score reliability
python -m safla.cli score reliability --uptime_analysis=true --error_rate_assessment=true
python -m safla.cli score reliability --recovery_time_evaluation=true

# Security assessment scoring
python -m safla.cli score security
python -m safla.cli score security --vulnerability_scan=true --compliance_check=true
python -m safla.cli score security --penetration_test_results=true
```

### 3. Goal Achievement Commands

#### Individual Goal Scoring
```bash
# Score specific goal progress
python -m safla.cli score goal --goal_id="performance_optimization_q1"
python -m safla.cli score goal --goal_id="quality_improvement" --detailed_breakdown=true
python -m safla.cli score goal --goal_id="deployment_automation" --timeline_analysis=true

# Goal completion validation
python -m safla.cli validate goal_completion --goal_id="security_upgrade"
python -m safla.cli validate goal_completion --scoring_threshold=90 --quality_check=true
python -m safla.cli validate goal_completion --stakeholder_approval=required
```

#### Portfolio Goal Scoring
```bash
# Score all active goals
python -m safla.cli score goals --status=active --detailed_breakdown=true
python -m safla.cli score goals --priority_weighted=true --portfolio_view=true
python -m safla.cli score goals --cross_dependency_analysis=true

# Goal portfolio optimization
python -m safla.cli optimize goal_portfolio --resource_constraints=true
python -m safla.cli optimize goal_portfolio --priority_rebalancing=true --timeline_adjustment=true
python -m safla.cli optimize goal_portfolio --risk_assessment=true
```

#### Goal Trend Analysis
```bash
# Analyze goal achievement trends
python -m safla.cli analyze goal_trends --time_period=90d --granularity=weekly
python -m safla.cli analyze goal_trends --predictive_modeling=true --confidence_intervals=true
python -m safla.cli analyze goal_trends --seasonal_adjustment=true
```

### 4. Strategy Effectiveness Commands

#### Strategy Performance Scoring
```bash
# Score individual strategy effectiveness
python -m safla.cli score strategy --strategy_id="optimization_v2"
python -m safla.cli score strategy --strategy_id="deployment_automation" --roi_analysis=true
python -m safla.cli score strategy --strategy_id="quality_improvement" --impact_assessment=true

# Strategy comparison and ranking
python -m safla.cli rank strategies --context=performance_optimization
python -m safla.cli rank strategies --criteria=effectiveness --weight_factors=true
python -m safla.cli rank strategies --multi_objective=true --pareto_analysis=true
```

#### Strategy Selection and Optimization
```bash
# Select optimal strategy based on scoring
python -m safla.cli select optimal_strategy --context=system_scaling
python -m safla.cli select optimal_strategy --constraints=budget,timeline --objectives=performance,quality
python -m safla.cli select optimal_strategy --risk_tolerance=moderate --confidence_threshold=85

# Strategy portfolio optimization
python -m safla.cli optimize strategy_portfolio --resource_allocation=true
python -m safla.cli optimize strategy_portfolio --synergy_analysis=true --conflict_resolution=true
python -m safla.cli optimize strategy_portfolio --dynamic_rebalancing=true
```

### 5. Learning and Adaptation Scoring Commands

#### Learning Effectiveness Scoring
```bash
# Score learning progress and effectiveness
python -m safla.cli score learning --metric_type=all --time_range=7d
python -m safla.cli score learning --accuracy_assessment=true --retention_analysis=true
python -m safla.cli score learning --adaptation_speed=true --improvement_trajectory=true

# Learning model performance scoring
python -m safla.cli score learning_model --model_id="adaptive_optimizer"
python -m safla.cli score learning_model --cross_validation=true --generalization_assessment=true
python -m safla.cli score learning_model --bias_detection=true --fairness_evaluation=true
```

#### Adaptation Pattern Scoring
```bash
# Score adaptation patterns and behavioral evolution
python -m safla.cli score adaptation --pattern_type=performance --time_window=30d
python -m safla.cli score adaptation --behavioral_consistency=true --evolution_tracking=true
python -m safla.cli score adaptation --predictive_capability=true --stability_assessment=true

# Meta-cognitive scoring
python -m safla.cli score meta_cognitive --self_awareness_level=true
python -m safla.cli score meta_cognitive --introspection_depth=true --consciousness_metrics=true
python -m safla.cli score meta_cognitive --self_improvement_capability=true
```

## Advanced CLI Workflows

### 1. Composite Scoring Workflows

#### Multi-Dimensional Performance Assessment
```bash
#!/bin/bash
# Comprehensive performance scoring workflow

echo "Starting multi-dimensional performance assessment..."

# Performance scoring components
PERF_SCORE=$(python -m safla.cli score performance --json | jq '.score')
MEMORY_SCORE=$(python -m safla.cli score memory --json | jq '.score')
VECTOR_SCORE=$(python -m safla.cli score vector_ops --json | jq '.score')
MCP_SCORE=$(python -m safla.cli score mcp_performance --json | jq '.score')

# Calculate weighted composite score
COMPOSITE_SCORE=$(echo "scale=2; ($PERF_SCORE * 0.3) + ($MEMORY_SCORE * 0.25) + ($VECTOR_SCORE * 0.25) + ($MCP_SCORE * 0.2)" | bc)

echo "Performance Scoring Results:"
echo "  System Performance: $PERF_SCORE/100"
echo "  Memory Efficiency: $MEMORY_SCORE/100"
echo "  Vector Operations: $VECTOR_SCORE/100"
echo "  MCP Communication: $MCP_SCORE/100"
echo "  Composite Score: $COMPOSITE_SCORE/100"

# Generate performance report
python -m safla.cli report performance_composite --score=$COMPOSITE_SCORE --components=all --format=detailed
```

#### Quality Assessment Portfolio
```bash
#!/bin/bash
# Comprehensive quality scoring workflow

echo "Executing comprehensive quality assessment..."

# Quality scoring dimensions
CODE_QUALITY=$(python -m safla.cli score code_quality --json | jq '.score')
TEST_COVERAGE=$(python -m safla.cli score test_coverage --json | jq '.score')
INTEGRATION_QUALITY=$(python -m safla.cli score integration_tests --json | jq '.score')
SECURITY_SCORE=$(python -m safla.cli score security --json | jq '.score')
DOCUMENTATION_SCORE=$(python -m safla.cli score documentation --json | jq '.score')

# Calculate overall quality score
OVERALL_QUALITY=$(echo "scale=2; ($CODE_QUALITY * 0.25) + ($TEST_COVERAGE * 0.2) + ($INTEGRATION_QUALITY * 0.2) + ($SECURITY_SCORE * 0.2) + ($DOCUMENTATION_SCORE * 0.15)" | bc)

echo "Quality Assessment Results:"
echo "  Code Quality: $CODE_QUALITY/100"
echo "  Test Coverage: $TEST_COVERAGE/100"
echo "  Integration Quality: $INTEGRATION_QUALITY/100"
echo "  Security Score: $SECURITY_SCORE/100"
echo "  Documentation: $DOCUMENTATION_SCORE/100"
echo "  Overall Quality: $OVERALL_QUALITY/100"

# Quality improvement recommendations
if (( $(echo "$OVERALL_QUALITY < 80" | bc -l) )); then
    python -m safla.cli recommend quality_improvements --current_score=$OVERALL_QUALITY --target_score=85
fi
```

### 2. Comparative Analysis Workflows

#### Strategy Effectiveness Comparison
```bash
#!/bin/bash
# Strategy effectiveness comparison workflow

echo "Comparing strategy effectiveness across multiple strategies..."

STRATEGIES=("optimization_v1" "optimization_v2" "optimization_v3" "hybrid_approach")
declare -A STRATEGY_SCORES

# Score each strategy
for strategy in "${STRATEGIES[@]}"; do
    SCORE=$(python -m safla.cli score strategy --strategy_id="$strategy" --json | jq '.effectiveness_score')
    STRATEGY_SCORES[$strategy]=$SCORE
    echo "Strategy $strategy: $SCORE/100"
done

# Rank strategies by effectiveness
echo "Strategy Rankings:"
for strategy in $(for s in "${!STRATEGY_SCORES[@]}"; do echo "$s ${STRATEGY_SCORES[$s]}"; done | sort -k2 -nr | cut -d' ' -f1); do
    echo "  $strategy: ${STRATEGY_SCORES[$strategy]}/100"
done

# Select optimal strategy
OPTIMAL_STRATEGY=$(python -m safla.cli select optimal_strategy --context=performance_optimization --json | jq -r '.selected_strategy')
echo "Optimal Strategy Selected: $OPTIMAL_STRATEGY"

# Generate strategy comparison report
python -m safla.cli report strategy_comparison --strategies="${STRATEGIES[*]}" --format=detailed --include_recommendations=true
```

#### Goal Achievement Benchmarking
```bash
#!/bin/bash
# Goal achievement benchmarking workflow

echo "Benchmarking goal achievement across time periods..."

TIME_PERIODS=("current_quarter" "previous_quarter" "year_to_date" "previous_year")

for period in "${TIME_PERIODS[@]}"; do
    ACHIEVEMENT_SCORE=$(python -m safla.cli score goals --time_period="$period" --json | jq '.portfolio_score')
    echo "$period Achievement Score: $ACHIEVEMENT_SCORE/100"
done

# Trend analysis
python -m safla.cli analyze goal_trends --time_period=12m --granularity=monthly --predictive_modeling=true

# Performance improvement recommendations
python -m safla.cli recommend goal_optimization --based_on=historical_trends --target_improvement=10
```

### 3. Continuous Monitoring Workflows

#### Real-Time Performance Scoring
```bash
#!/bin/bash
# Continuous performance monitoring and scoring

echo "Starting continuous performance scoring monitor..."

MONITORING_INTERVAL=300  # 5 minutes
ALERT_THRESHOLD=70

while true; do
    TIMESTAMP=$(date '+%Y-%m-%d %H:%M:%S')
    
    # Get current performance score
    CURRENT_SCORE=$(python -m safla.cli score performance --real_time=true --json | jq '.score')
    
    echo "[$TIMESTAMP] Performance Score: $CURRENT_SCORE/100"
    
    # Check for performance degradation
    if (( $(echo "$CURRENT_SCORE < $ALERT_THRESHOLD" | bc -l) )); then
        echo "ALERT: Performance score below threshold ($ALERT_THRESHOLD)"
        python -m safla.cli alert performance_degradation --score=$CURRENT_SCORE --threshold=$ALERT_THRESHOLD
        
        # Trigger performance analysis
        python -m safla.cli analyze performance_issues --immediate=true --detailed=true
    fi
    
    # Log score for trend analysis
    echo "$TIMESTAMP,$CURRENT_SCORE" >> performance_scores.log
    
    sleep $MONITORING_INTERVAL
done
```

#### Adaptive Quality Monitoring
```bash
#!/bin/bash
# Adaptive quality monitoring with dynamic thresholds

echo "Starting adaptive quality monitoring..."

# Initialize adaptive thresholds
CODE_THRESHOLD=75
TEST_THRESHOLD=80
SECURITY_THRESHOLD=85

while true; do
    # Score quality dimensions
    CODE_SCORE=$(python -m safla.cli score code_quality --json | jq '.score')
    TEST_SCORE=$(python -m safla.cli score test_coverage --json | jq '.score')
    SECURITY_SCORE=$(python -m safla.cli score security --json | jq '.score')
    
    # Adaptive threshold adjustment based on trends
    if (( $(echo "$CODE_SCORE > $CODE_THRESHOLD + 10" | bc -l) )); then
        CODE_THRESHOLD=$((CODE_THRESHOLD + 2))
        echo "Raising code quality threshold to $CODE_THRESHOLD"
    fi
    
    # Quality alerts and recommendations
    if (( $(echo "$CODE_SCORE < $CODE_THRESHOLD" | bc -l) )); then
        python -m safla.cli recommend code_improvements --current_score=$CODE_SCORE --target_score=$CODE_THRESHOLD
    fi
    
    if (( $(echo "$TEST_SCORE < $TEST_THRESHOLD" | bc -l) )); then
        python -m safla.cli recommend test_improvements --coverage_gap=$((TEST_THRESHOLD - TEST_SCORE))
    fi
    
    if (( $(echo "$SECURITY_SCORE < $SECURITY_THRESHOLD" | bc -l) )); then
        python -m safla.cli alert security_concern --score=$SECURITY_SCORE --priority=high
    fi
    
    sleep 600  # 10 minutes
done
```

## Reporting and Visualization Commands

### 1. Score Report Generation

#### Comprehensive Scoring Reports
```bash
# Generate detailed scoring reports
python -m safla.cli report scores --format=json --include_details=true
python -m safla.cli report scores --format=html --interactive=true --charts=true
python -m safla.cli report scores --format=pdf --executive_summary=true --technical_details=true

# Time-series scoring reports
python -m safla.cli report score_trends --time_range=90d --granularity=daily
python -m safla.cli report score_trends --predictive_analysis=true --confidence_intervals=true
python -m safla.cli report score_trends --seasonal_decomposition=true

# Comparative scoring reports
python -m safla.cli report score_comparison --baseline=production --current=staging
python -m safla.cli report score_comparison --multiple_environments=true --statistical_analysis=true
python -m safla.cli report score_comparison --improvement_recommendations=true
```

#### Stakeholder-Specific Reports
```bash
# Executive summary reports
python -m safla.cli report executive_scores --high_level=true --key_metrics_only=true
python -m safla.cli report executive_scores --business_impact=true --roi_analysis=true
python -m safla.cli report executive_scores --strategic_alignment=true

# Technical detailed reports
python -m safla.cli report technical_scores --detailed_metrics=true --root_cause_analysis=true
python -m safla.cli report technical_scores --performance_bottlenecks=true --optimization_opportunities=true
python -m safla.cli report technical_scores --implementation_recommendations=true

# Team performance reports
python -m safla.cli report team_scores --goal_achievement=true --quality_metrics=true
python -m safla.cli report team_scores --improvement_trends=true --recognition_highlights=true
python -m safla.cli report team_scores --learning_progress=true
```

### 2. Dashboard and Visualization

#### Interactive Dashboards
```bash
# Create comprehensive scoring dashboards
python -m safla.cli dashboard create --template=comprehensive --metrics=all
python -m safla.cli dashboard create --template=executive --high_level_metrics=true
python -m safla.cli dashboard create --template=technical --detailed_metrics=true

# Customize dashboard layouts
python -m safla.cli dashboard customize --widgets=trends,comparisons,alerts,recommendations
python -m safla.cli dashboard customize --layout=grid --responsive=true --real_time_updates=true
python -m safla.cli dashboard customize --themes=dark,professional --branding=corporate

# Dashboard management
python -m safla.cli dashboard update --refresh_interval=5m --auto_refresh=true
python -m safla.cli dashboard export --format=html --interactive=true --standalone=true
python -m safla.cli dashboard share --access_level=read_only --expiration=30d
```

#### Real-Time Visualization
```bash
# Real-time scoring visualization
python -m safla.cli visualize scores --real_time=true --update_interval=30s
python -m safla.cli visualize trends --live_updates=true --predictive_overlay=true
python -m safla.cli visualize comparisons --dynamic_baselines=true --threshold_indicators=true

# Alert visualization
python -m safla.cli visualize alerts --severity_color_coding=true --trend_indicators=true
python -m safla.cli visualize alerts --geographic_distribution=true --time_correlation=true
python -m safla.cli visualize alerts --resolution_tracking=true
```

## Integration and Automation Commands

### 1. CI/CD Integration

#### Automated Scoring in Pipelines
```bash
# CI/CD pipeline scoring integration
python -m safla.cli pipeline score --stage=build --quality_gates=true
python -m safla.cli pipeline score --stage=test --coverage_requirements=80
python -m safla.cli pipeline score --stage=deploy --performance_validation=true

# Quality gate enforcement
python -m safla.cli gate enforce --minimum_score=85 --block_on_failure=true
python -m safla.cli gate enforce --quality_dimensions=all --weighted_scoring=true
python -m safla.cli gate enforce --trend_analysis=true --regression_detection=true

# Pipeline scoring reports
python -m safla.cli pipeline report --build_id="$BUILD_ID" --detailed=true
python -m safla.cli pipeline report --trend_analysis=true --historical_comparison=true
python -m safla.cli pipeline report --failure_analysis=true --improvement_recommendations=true
```

#### Deployment Scoring Validation
```bash
# Pre-deployment scoring validation
python -m safla.cli validate deployment_readiness --environment=production
python -m safla.cli validate deployment_readiness --performance_requirements=true --security_compliance=true
python -m safla.cli validate deployment_readiness --rollback_criteria=true

# Post-deployment scoring verification
python -m safla.cli verify deployment_success --performance_baseline=true
python -m safla.cli verify deployment_success --quality_maintenance=true --user_impact_assessment=true
python -m safla.cli verify deployment_success --monitoring_setup=true
```

### 2. Automated Optimization

#### Score-Driven Optimization
```bash
# Automated optimization based on scores
python -m safla.cli optimize auto --based_on=performance_scores --target_improvement=15
python -m safla.cli optimize auto --based_on=quality_scores --focus_areas=code,tests,security
python -m safla.cli optimize auto --based_on=goal_scores --resource_constraints=true

# Optimization recommendation generation
python -m safla.cli recommend optimizations --score_analysis=true --impact_assessment=true
python -m safla.cli recommend optimizations --cost_benefit_analysis=true --implementation_priority=true
python -m safla.cli recommend optimizations --risk_assessment=true --timeline_estimation=true

# Optimization tracking and validation
python -m safla.cli track optimization_progress --optimization_id="perf_opt_2024_q1"
python -m safla.cli track optimization_progress --before_after_comparison=true --roi_calculation=true
python -m safla.cli track optimization_progress --success_metrics=true --lessons_learned=true
```

This comprehensive CLI commands documentation provides the Scorer mode with detailed command-line integration capabilities for implementing effective quantitative evaluation and scoring workflows within the SAFLA-aiGI framework.