# Scorer Mode Examples Documentation

## Overview

This document provides comprehensive practical examples of using the Scorer mode within the SAFLA-aiGI framework. These examples demonstrate real-world CLI commands, MCP tool usage patterns, and integration workflows for effective quantitative evaluation and scoring operations.

## Basic Scoring Examples

### 1. Simple Performance Scoring

#### Basic Performance Assessment
```bash
# Basic performance scoring workflow
echo "Starting basic performance scoring..."

# Get current system status
use_mcp_tool safla get_system_info

# Analyze performance bottlenecks
use_mcp_tool safla analyze_performance_bottlenecks \
    --duration_seconds=180 \
    --include_memory_profile=true

# Generate performance score
PERF_SCORE=$(python -m safla.cli score performance --basic=true --json | jq '.score')
echo "Performance Score: $PERF_SCORE/100"

# Generate basic report
python -m safla.cli report performance_summary \
    --score=$PERF_SCORE \
    --format=text
```

#### Memory Performance Scoring
```bash
# Memory-focused performance scoring
echo "Executing memory performance scoring..."

# Benchmark memory operations
use_mcp_tool safla benchmark_memory_performance \
    --test_duration=120 \
    --memory_patterns='["sequential","random","mixed"]'

# Validate memory operations
use_mcp_tool safla validate_memory_operations \
    --test_data_size=50 \
    --include_stress_test=true

# Calculate memory performance score
MEMORY_SCORE=$(python -m safla.cli score memory_performance --detailed=true --json | jq '.score')
echo "Memory Performance Score: $MEMORY_SCORE/100"

# Generate memory performance report
python -m safla.cli report memory_analysis \
    --score=$MEMORY_SCORE \
    --include_recommendations=true \
    --format=html
```

### 2. Quality Assessment Scoring

#### Code Quality Evaluation
```bash
# Comprehensive code quality scoring
echo "Starting code quality assessment..."

# Run integration tests
use_mcp_tool safla run_integration_tests \
    --test_suite="quality_assessment" \
    --parallel=true \
    --verbose=true

# Test MCP connectivity
use_mcp_tool safla test_mcp_connectivity \
    --test_depth="comprehensive"

# Calculate quality scores
CODE_QUALITY=$(python -m safla.cli score code_quality --comprehensive=true --json | jq '.score')
TEST_COVERAGE=$(python -m safla.cli score test_coverage --minimum_threshold=80 --json | jq '.score')
SECURITY_SCORE=$(python -m safla.cli score security --vulnerability_scan=true --json | jq '.score')

echo "Quality Assessment Results:"
echo "  Code Quality: $CODE_QUALITY/100"
echo "  Test Coverage: $TEST_COVERAGE/100"
echo "  Security: $SECURITY_SCORE/100"

# Calculate composite quality score
QUALITY_COMPOSITE=$(echo "scale=2; ($CODE_QUALITY * 0.4) + ($TEST_COVERAGE * 0.35) + ($SECURITY_SCORE * 0.25)" | bc)
echo "Composite Quality Score: $QUALITY_COMPOSITE/100"
```

#### Test Coverage Analysis
```bash
# Detailed test coverage scoring
echo "Analyzing test coverage..."

# Run comprehensive test suite
use_mcp_tool safla run_integration_tests \
    --test_suite="comprehensive" \
    --parallel=true

# Validate memory operations with testing
use_mcp_tool safla validate_memory_operations \
    --test_data_size=100 \
    --include_stress_test=true

# Generate detailed coverage report
COVERAGE_SCORE=$(python -m safla.cli score test_coverage \
    --detailed_analysis=true \
    --include_branch_coverage=true \
    --minimum_threshold=85 \
    --json | jq '.score')

echo "Test Coverage Score: $COVERAGE_SCORE/100"

# Generate coverage recommendations
python -m safla.cli report coverage_analysis \
    --score=$COVERAGE_SCORE \
    --include_gaps=true \
    --recommendations=true \
    --format=json,html
```

### 3. Goal Achievement Scoring

#### Strategic Goal Assessment
```bash
# Strategic goal achievement scoring
echo "Evaluating strategic goal achievement..."

# List all active goals
use_mcp_tool safla list_goals \
    --status_filter="active" \
    --priority_filter="high"

# Evaluate goal progress
use_mcp_tool safla evaluate_goal_progress \
    --include_recommendations=true

# Calculate goal achievement scores
STRATEGIC_GOALS=$(python -m safla.cli score goals \
    --strategic_focus=true \
    --priority_filter="high" \
    --json | jq '.score')

OVERALL_GOALS=$(python -m safla.cli score goals \
    --portfolio_view=true \
    --json | jq '.score')

echo "Goal Achievement Results:"
echo "  Strategic Goals: $STRATEGIC_GOALS/100"
echo "  Overall Portfolio: $OVERALL_GOALS/100"

# Generate goal achievement report
python -m safla.cli report goal_achievement \
    --strategic_score=$STRATEGIC_GOALS \
    --portfolio_score=$OVERALL_GOALS \
    --include_action_items=true \
    --format=pdf
```

#### Individual Goal Tracking
```bash
# Individual goal progress scoring
GOAL_ID="goal_12345"
echo "Tracking progress for goal: $GOAL_ID"

# Evaluate specific goal progress
use_mcp_tool safla evaluate_goal_progress \
    --goal_id="$GOAL_ID" \
    --include_recommendations=true

# Get detailed goal score
GOAL_SCORE=$(python -m safla.cli score individual_goal \
    --goal_id="$GOAL_ID" \
    --detailed_breakdown=true \
    --json | jq '.score')

echo "Individual Goal Score: $GOAL_SCORE/100"

# Update goal with progress
use_mcp_tool safla update_goal \
    --goal_id="$GOAL_ID" \
    --progress=$(echo "scale=2; $GOAL_SCORE / 100" | bc) \
    --notes="Automated scoring update: $GOAL_SCORE/100"
```

## Advanced Scoring Examples

### 1. Multi-Dimensional Scoring

#### Comprehensive System Assessment
```bash
# Multi-dimensional comprehensive scoring
echo "Starting comprehensive multi-dimensional scoring..."

# Performance Dimension
echo "=== Performance Dimension ==="
use_mcp_tool safla analyze_performance_bottlenecks --duration_seconds=300 --include_memory_profile=true
use_mcp_tool safla benchmark_memory_performance --test_duration=180 --memory_patterns='["sequential","random","mixed"]'
PERF_SCORE=$(python -m safla.cli score performance --comprehensive=true --json | jq '.score')

# Quality Dimension
echo "=== Quality Dimension ==="
use_mcp_tool safla run_integration_tests --test_suite="comprehensive" --parallel=true
use_mcp_tool safla validate_memory_operations --test_data_size=75 --include_stress_test=true
QUALITY_SCORE=$(python -m safla.cli score code_quality --comprehensive=true --json | jq '.score')

# Goal Achievement Dimension
echo "=== Goal Achievement Dimension ==="
use_mcp_tool safla evaluate_goal_progress --include_recommendations=true
GOAL_SCORE=$(python -m safla.cli score goals --portfolio_view=true --json | jq '.score')

# Strategy Effectiveness Dimension
echo "=== Strategy Effectiveness Dimension ==="
use_mcp_tool safla evaluate_strategy_performance --evaluation_period_hours=168
STRATEGY_SCORE=$(python -m safla.cli score strategies --effectiveness_analysis=true --json | jq '.score')

# Learning and Adaptation Dimension
echo "=== Learning and Adaptation Dimension ==="
use_mcp_tool safla get_learning_metrics --metric_type="all" --time_range_hours=168
use_mcp_tool safla analyze_adaptation_patterns --pattern_type="all" --analysis_depth="comprehensive"
LEARNING_SCORE=$(python -m safla.cli score learning --adaptation_metrics=true --json | jq '.score')

# Calculate weighted composite score
WEIGHTED_COMPOSITE=$(echo "scale=2; ($PERF_SCORE * 0.25) + ($QUALITY_SCORE * 0.25) + ($GOAL_SCORE * 0.2) + ($STRATEGY_SCORE * 0.15) + ($LEARNING_SCORE * 0.15)" | bc)

echo "=== MULTI-DIMENSIONAL SCORING RESULTS ==="
echo "Performance: $PERF_SCORE/100 (25%)"
echo "Quality: $QUALITY_SCORE/100 (25%)"
echo "Goals: $GOAL_SCORE/100 (20%)"
echo "Strategy: $STRATEGY_SCORE/100 (15%)"
echo "Learning: $LEARNING_SCORE/100 (15%)"
echo "WEIGHTED COMPOSITE: $WEIGHTED_COMPOSITE/100"

# Generate comprehensive report
python -m safla.cli report multi_dimensional \
    --performance=$PERF_SCORE \
    --quality=$QUALITY_SCORE \
    --goals=$GOAL_SCORE \
    --strategy=$STRATEGY_SCORE \
    --learning=$LEARNING_SCORE \
    --composite=$WEIGHTED_COMPOSITE \
    --format=html,json,pdf
```

#### Stakeholder-Focused Scoring
```bash
# Stakeholder-specific scoring framework
echo "Executing stakeholder-focused scoring..."

# Technical Stakeholder Metrics
echo "=== Technical Stakeholder Metrics ==="
TECH_PERFORMANCE=$(python -m safla.cli score performance --technical_focus=true --json | jq '.score')
TECH_QUALITY=$(python -m safla.cli score code_quality --technical_depth=true --json | jq '.score')
TECH_SECURITY=$(python -m safla.cli score security --technical_assessment=true --json | jq '.score')

TECH_COMPOSITE=$(echo "scale=2; ($TECH_PERFORMANCE * 0.4) + ($TECH_QUALITY * 0.35) + ($TECH_SECURITY * 0.25)" | bc)

# Business Stakeholder Metrics
echo "=== Business Stakeholder Metrics ==="
BUSINESS_GOALS=$(python -m safla.cli score goals --business_value=true --json | jq '.score')
BUSINESS_ROI=$(python -m safla.cli score roi --business_metrics=true --json | jq '.score')
BUSINESS_EFFICIENCY=$(python -m safla.cli score efficiency --business_focus=true --json | jq '.score')

BUSINESS_COMPOSITE=$(echo "scale=2; ($BUSINESS_GOALS * 0.4) + ($BUSINESS_ROI * 0.35) + ($BUSINESS_EFFICIENCY * 0.25)" | bc)

# User Experience Stakeholder Metrics
echo "=== User Experience Stakeholder Metrics ==="
UX_PERFORMANCE=$(python -m safla.cli score performance --user_experience=true --json | jq '.score')
UX_RELIABILITY=$(python -m safla.cli score reliability --user_focus=true --json | jq '.score')
UX_SATISFACTION=$(python -m safla.cli score satisfaction --user_metrics=true --json | jq '.score')

UX_COMPOSITE=$(echo "scale=2; ($UX_PERFORMANCE * 0.4) + ($UX_RELIABILITY * 0.3) + ($UX_SATISFACTION * 0.3)" | bc)

echo "=== STAKEHOLDER SCORING RESULTS ==="
echo "Technical Stakeholders: $TECH_COMPOSITE/100"
echo "Business Stakeholders: $BUSINESS_COMPOSITE/100"
echo "UX Stakeholders: $UX_COMPOSITE/100"

# Generate stakeholder reports
python -m safla.cli report stakeholder_dashboard \
    --technical=$TECH_COMPOSITE \
    --business=$BUSINESS_COMPOSITE \
    --ux=$UX_COMPOSITE \
    --format=dashboard,pdf
```

### 2. Comparative Scoring

#### Baseline Comparison Scoring
```bash
# Baseline comparison scoring workflow
echo "Executing baseline comparison scoring..."

# Establish current baseline
echo "=== Establishing Current Baseline ==="
CURRENT_PERF=$(python -m safla.cli score performance --baseline_mode=true --json | jq '.score')
CURRENT_QUALITY=$(python -m safla.cli score code_quality --baseline_mode=true --json | jq '.score')
CURRENT_GOALS=$(python -m safla.cli score goals --baseline_mode=true --json | jq '.score')

# Load historical baseline
HISTORICAL_BASELINE=$(access_mcp_resource safla safla://performance-baselines | jq -r '.latest_baseline')
BASELINE_PERF=$(echo $HISTORICAL_BASELINE | jq '.performance')
BASELINE_QUALITY=$(echo $HISTORICAL_BASELINE | jq '.quality')
BASELINE_GOALS=$(echo $HISTORICAL_BASELINE | jq '.goals')

# Calculate improvement deltas
PERF_DELTA=$(echo "scale=2; $CURRENT_PERF - $BASELINE_PERF" | bc)
QUALITY_DELTA=$(echo "scale=2; $CURRENT_QUALITY - $BASELINE_QUALITY" | bc)
GOALS_DELTA=$(echo "scale=2; $CURRENT_GOALS - $BASELINE_GOALS" | bc)

echo "=== BASELINE COMPARISON RESULTS ==="
echo "Performance: $CURRENT_PERF/100 (Δ: $PERF_DELTA)"
echo "Quality: $CURRENT_QUALITY/100 (Δ: $QUALITY_DELTA)"
echo "Goals: $CURRENT_GOALS/100 (Δ: $GOALS_DELTA)"

# Generate improvement trend analysis
python -m safla.cli report baseline_comparison \
    --current_performance=$CURRENT_PERF \
    --current_quality=$CURRENT_QUALITY \
    --current_goals=$CURRENT_GOALS \
    --baseline_performance=$BASELINE_PERF \
    --baseline_quality=$BASELINE_QUALITY \
    --baseline_goals=$BASELINE_GOALS \
    --format=trend_analysis
```

#### Competitive Benchmarking
```bash
# Competitive benchmarking scoring
echo "Executing competitive benchmarking..."

# Internal performance benchmarks
echo "=== Internal Benchmarks ==="
use_mcp_tool safla benchmark_memory_performance --test_duration=300 --memory_patterns='["sequential","random","mixed"]'
use_mcp_tool safla benchmark_vector_operations --vector_count=5000 --vector_dimensions=1024

INTERNAL_PERF=$(python -m safla.cli score performance --benchmark_mode=true --json | jq '.score')

# Industry standard comparisons
INDUSTRY_STANDARDS=$(access_mcp_resource safla safla://performance-baselines | jq -r '.industry_standards')
INDUSTRY_PERF_STANDARD=$(echo $INDUSTRY_STANDARDS | jq '.performance_standard')

# Calculate competitive position
COMPETITIVE_RATIO=$(echo "scale=2; $INTERNAL_PERF / $INDUSTRY_PERF_STANDARD" | bc)

if (( $(echo "$COMPETITIVE_RATIO > 1.1" | bc -l) )); then
    COMPETITIVE_POSITION="Above Industry Standard"
elif (( $(echo "$COMPETITIVE_RATIO > 0.9" | bc -l) )); then
    COMPETITIVE_POSITION="At Industry Standard"
else
    COMPETITIVE_POSITION="Below Industry Standard"
fi

echo "=== COMPETITIVE BENCHMARKING RESULTS ==="
echo "Internal Performance: $INTERNAL_PERF/100"
echo "Industry Standard: $INDUSTRY_PERF_STANDARD/100"
echo "Competitive Ratio: $COMPETITIVE_RATIO"
echo "Position: $COMPETITIVE_POSITION"

# Generate competitive analysis report
python -m safla.cli report competitive_analysis \
    --internal_score=$INTERNAL_PERF \
    --industry_standard=$INDUSTRY_PERF_STANDARD \
    --competitive_ratio=$COMPETITIVE_RATIO \
    --position="$COMPETITIVE_POSITION" \
    --format=executive_summary
```

### 3. Predictive Scoring

#### Trend-Based Predictive Scoring
```bash
# Trend-based predictive scoring workflow
echo "Executing trend-based predictive scoring..."

# Collect historical scoring data
HISTORICAL_DATA=$(access_mcp_resource safla safla://performance-metrics | jq -r '.historical_scores')

# Analyze recent trends
echo "=== Trend Analysis ==="
RECENT_SCORES=($(echo $HISTORICAL_DATA | jq -r '.recent_performance_scores[]'))
TREND_DIRECTION="stable"

# Calculate trend direction
if [ ${#RECENT_SCORES[@]} -ge 3 ]; then
    LAST_THREE=(${RECENT_SCORES[@]: -3})
    if (( $(echo "${LAST_THREE[2]} > ${LAST_THREE[1]} && ${LAST_THREE[1]} > ${LAST_THREE[0]}" | bc -l) )); then
        TREND_DIRECTION="improving"
    elif (( $(echo "${LAST_THREE[2]} < ${LAST_THREE[1]} && ${LAST_THREE[1]} < ${LAST_THREE[0]}" | bc -l) )); then
        TREND_DIRECTION="declining"
    fi
fi

# Current scoring
CURRENT_SCORE=$(python -m safla.cli score performance --predictive_mode=true --json | jq '.score')

# Predictive scoring based on trends
case $TREND_DIRECTION in
    "improving")
        PREDICTED_SCORE=$(echo "scale=2; $CURRENT_SCORE * 1.05" | bc)
        CONFIDENCE="High"
        ;;
    "declining")
        PREDICTED_SCORE=$(echo "scale=2; $CURRENT_SCORE * 0.95" | bc)
        CONFIDENCE="Medium"
        ;;
    *)
        PREDICTED_SCORE=$CURRENT_SCORE
        CONFIDENCE="Low"
        ;;
esac

echo "=== PREDICTIVE SCORING RESULTS ==="
echo "Current Score: $CURRENT_SCORE/100"
echo "Trend Direction: $TREND_DIRECTION"
echo "Predicted Score (next period): $PREDICTED_SCORE/100"
echo "Prediction Confidence: $CONFIDENCE"

# Generate predictive analysis report
python -m safla.cli report predictive_analysis \
    --current_score=$CURRENT_SCORE \
    --predicted_score=$PREDICTED_SCORE \
    --trend_direction="$TREND_DIRECTION" \
    --confidence="$CONFIDENCE" \
    --format=forecast_report
```

## Integration Examples

### 1. Critic-Scorer Integration

#### Qualitative-Quantitative Analysis
```bash
# Integrated Critic-Scorer analysis workflow
echo "Starting integrated Critic-Scorer analysis..."

# Phase 1: Quantitative scoring
echo "=== Phase 1: Quantitative Assessment ==="
use_mcp_tool safla analyze_performance_bottlenecks --duration_seconds=300 --include_memory_profile=true
use_mcp_tool safla run_integration_tests --test_suite="comprehensive" --parallel=true

PERF_SCORE=$(python -m safla.cli score performance --comprehensive=true --json | jq '.score')
QUALITY_SCORE=$(python -m safla.cli score code_quality --comprehensive=true --json | jq '.score')

echo "Quantitative Scores - Performance: $PERF_SCORE/100, Quality: $QUALITY_SCORE/100"

# Phase 2: Trigger critic analysis
echo "=== Phase 2: Qualitative Analysis ==="
new_task critic "Perform comprehensive qualitative analysis of system performance and code quality. Current quantitative scores: Performance=$PERF_SCORE/100, Quality=$QUALITY_SCORE/100. Identify root causes, patterns, and improvement opportunities."

# Phase 3: Synthesis (after critic completion)
echo "=== Phase 3: Integrated Synthesis ==="
COMPOSITE_SCORE=$(echo "scale=2; ($PERF_SCORE * 0.5) + ($QUALITY_SCORE * 0.5)" | bc)

# Generate integrated report
python -m safla.cli report integrated_analysis \
    --quantitative_performance=$PERF_SCORE \
    --quantitative_quality=$QUALITY_SCORE \
    --composite_score=$COMPOSITE_SCORE \
    --critic_analysis_file="critic_analysis_results.json" \
    --synthesis_mode=comprehensive \
    --format=executive_dashboard
```

### 2. TDD-Scorer Integration

#### Test-Driven Quality Scoring
```bash
# TDD-Scorer integrated quality assessment
echo "Starting TDD-Scorer integration workflow..."

# Phase 1: TDD test specification and execution
echo "=== Phase 1: TDD Test Execution ==="
new_task tdd "Execute comprehensive test-driven development workflow. Create test specifications for performance benchmarks, quality thresholds, and acceptance criteria."

# Phase 2: Scorer validation against TDD criteria
echo "=== Phase 2: TDD-Driven Scoring ==="
use_mcp_tool safla run_integration_tests --test_suite="tdd_comprehensive" --parallel=true
use_mcp_tool safla validate_memory_operations --test_data_size=100 --include_stress_test=true

TDD_COVERAGE_SCORE=$(python -m safla.cli score test_coverage --tdd_specifications=true --json | jq '.score')
TDD_QUALITY_SCORE=$(python -m safla.cli score code_quality --tdd_criteria=true --json | jq '.score')
TDD_PERFORMANCE_SCORE=$(python -m safla.cli score performance --tdd_benchmarks=true --json | jq '.score')

echo "TDD-Driven Scores:"
echo "  Coverage: $TDD_COVERAGE_SCORE/100"
echo "  Quality: $TDD_QUALITY_SCORE/100"
echo "  Performance: $TDD_PERFORMANCE_SCORE/100"

# Phase 3: TDD validation and feedback
TDD_COMPOSITE=$(echo "scale=2; ($TDD_COVERAGE_SCORE * 0.4) + ($TDD_QUALITY_SCORE * 0.35) + ($TDD_PERFORMANCE_SCORE * 0.25)" | bc)

if (( $(echo "$TDD_COMPOSITE < 85" | bc -l) )); then
    echo "TDD validation failed - Score: $TDD_COMPOSITE/100"
    new_task tdd "Refine test specifications based on scoring results. Address gaps in coverage ($TDD_COVERAGE_SCORE), quality ($TDD_QUALITY_SCORE), and performance ($TDD_PERFORMANCE_SCORE)."
else
    echo "TDD validation passed - Score: $TDD_COMPOSITE/100"
    python -m safla.cli report tdd_validation_success \
        --composite_score=$TDD_COMPOSITE \
        --coverage_score=$TDD_COVERAGE_SCORE \
        --quality_score=$TDD_QUALITY_SCORE \
        --performance_score=$TDD_PERFORMANCE_SCORE \
        --format=validation_certificate
fi
```

### 3. Orchestrator-Scorer Coordination

#### Strategic Scoring Orchestration
```bash
# Orchestrator-directed strategic scoring
echo "Starting Orchestrator-Scorer coordination..."

# Phase 1: Orchestrator strategy definition
echo "=== Phase 1: Strategic Scoring Strategy ==="
new_task orchestrator "Define comprehensive scoring strategy based on current system priorities, stakeholder requirements, and strategic objectives. Specify scoring dimensions, weights, and success criteria."

# Phase 2: Execute orchestrated scoring plan
echo "=== Phase 2: Strategic Scoring Execution ==="

# Strategic priority scoring
STRATEGIC_PRIORITIES=$(access_mcp_resource safla safla://goals | jq -r '.strategic_priorities[]')
STRATEGIC_SCORES=()

for priority in $STRATEGIC_PRIORITIES; do
    echo "Scoring strategic priority: $priority"
    PRIORITY_SCORE=$(python -m safla.cli score strategic_priority --priority="$priority" --json | jq '.score')
    STRATEGIC_SCORES+=($PRIORITY_SCORE)
    echo "  $priority Score: $PRIORITY_SCORE/100"
done

# Stakeholder satisfaction scoring
STAKEHOLDER_REQUIREMENTS=$(access_mcp_resource safla safla://config | jq -r '.stakeholder_requirements')
STAKEHOLDER_SCORE=$(python -m safla.cli score stakeholder_satisfaction --requirements="$STAKEHOLDER_REQUIREMENTS" --json | jq '.score')

# Strategic objective scoring
use_mcp_tool safla list_goals --status_filter="active" --priority_filter="high"
STRATEGIC_OBJECTIVE_SCORE=$(python -m safla.cli score strategic_objectives --high_priority_only=true --json | jq '.score')

# Calculate strategic composite
STRATEGIC_COMPOSITE=$(echo "scale=2; ($STAKEHOLDER_SCORE * 0.4) + ($STRATEGIC_OBJECTIVE_SCORE * 0.6)" | bc)

echo "=== Strategic Scoring Results ==="
echo "Stakeholder Satisfaction: $STAKEHOLDER_SCORE/100"
echo "Strategic Objectives: $STRATEGIC_OBJECTIVE_SCORE/100"
echo "Strategic Composite: $STRATEGIC_COMPOSITE/100"

# Phase 3: Orchestrator synthesis and recommendations
new_task orchestrator "Synthesize strategic scoring results and provide recommendations. Strategic composite score: $STRATEGIC_COMPOSITE/100. Include resource allocation recommendations and priority adjustments."
```

## Continuous Monitoring Examples

### 1. Real-Time Scoring Dashboard

#### Live Performance Monitoring
```bash
# Real-time scoring dashboard setup
echo "Setting up real-time scoring dashboard..."

DASHBOARD_INTERVAL=60  # 1 minute updates
LOG_FILE="realtime_scores_$(date +%Y%m%d_%H%M%S).log"

# Initialize dashboard
echo "Real-Time SAFLA Scoring Dashboard" | tee $LOG_FILE
echo "Started: $(date)" | tee -a $LOG_FILE
echo "Update Interval: ${DASHBOARD_INTERVAL}s" | tee -a $LOG_FILE
echo "=================================" | tee -a $LOG_FILE

while true; do
    TIMESTAMP=$(date '+%Y-%m-%d %H:%M:%S')
    
    # Real-time performance scoring
    PERF_SCORE=$(python -m safla.cli score performance --real_time=true --json | jq '.score')
    
    # Real-time system health
    use_mcp_tool safla monitor_system_health --check_interval=30
    HEALTH_SCORE=$(python -m safla.cli score system_health --real_time=true --json | jq '.score')
    
    # Real-time goal progress
    GOAL_SCORE=$(python -m safla.cli score goals --real_time=true --json | jq '.score')
    
    # Calculate real-time composite
    REALTIME_COMPOSITE=$(echo "scale=2; ($PERF_SCORE * 0.4) + ($HEALTH_SCORE * 0.3) + ($GOAL_SCORE * 0.3)" | bc)
    
    # Dashboard output
    echo "[$TIMESTAMP] Performance: $PERF_SCORE | Health: $HEALTH_SCORE | Goals: $GOAL_SCORE | Composite: $REALTIME_COMPOSITE" | tee -a $LOG_FILE
    
    # Alert handling
    if (( $(echo "$REALTIME_COMPOSITE < 70" | bc -l) )); then
        echo "ALERT: Low composite score detected: $REALTIME_COMPOSITE/100" | tee -a $LOG_FILE
        python -m safla.cli alert low_composite_score --score=$REALTIME_COMPOSITE --timestamp="$TIMESTAMP"
    fi
    
    sleep $DASHBOARD_INTERVAL
done
```

### 2. Automated Scoring Reports

#### Daily Automated Scoring Report
```bash
# Daily automated comprehensive scoring report
echo "Generating daily automated scoring report..."

REPORT_DATE=$(date +%Y%m%d)
REPORT_DIR="daily_reports/$REPORT_DATE"
mkdir -p $REPORT_DIR

echo "=== Daily SAFLA Scoring Report - $(date +%Y-%m-%d) ===" | tee $REPORT_DIR/daily_summary.txt

# Performance scoring
echo "Executing performance assessment..." | tee -a $REPORT_DIR/daily_summary.txt
use_mcp_tool safla analyze_performance_bottlenecks --duration_seconds=600 --include_memory_profile=true
use_mcp_tool safla benchmark_memory_performance --test_duration=300 --memory_patterns='["sequential","random","mixed"]'
DAILY_PERF=$(python -m safla.cli score performance --daily_comprehensive=true --json | jq '.score')

# Quality scoring
echo "Executing quality assessment..." | tee -a $REPORT_DIR/daily_summary.txt
use_mcp_tool safla run_integration_tests --test_suite="daily_comprehensive" --parallel=true
DAILY_QUALITY=$(python -m safla.cli score code_quality --daily_comprehensive=true --json | jq '.score')

# Goal scoring
echo "Executing goal assessment..." | tee -a $REPORT_DIR/daily_summary.txt
use_mcp_tool safla evaluate_goal_progress --include_recommendations=true
DAILY_GOALS=$(python -m safla.cli score goals --daily_comprehensive=true --json | jq '.score')

# Strategy scoring
echo "Executing strategy assessment..." | tee -a $REPORT_DIR/daily_summary.txt
use_mcp_tool safla evaluate_strategy_performance --evaluation_period_hours=24
DAILY_STRATEGY=$(python -m safla.cli score strategies --daily_comprehensive=true --json | jq '.score')

# Calculate daily composite
DAILY_COMPOSITE=$(echo "scale=2; ($DAILY_PERF * 0.3) + ($DAILY_QUALITY * 0.3) + ($DAILY_GOALS * 0.25) + ($DAILY_STRATEGY * 0.15)" | bc)

# Generate summary
echo "=== DAILY SCORING SUMMARY ===" | tee -a $REPORT_DIR/daily_summary.txt
echo "Performance: $DAILY_PERF/100" | tee -a $REPORT_DIR/daily_summary.txt
echo "Quality: $DAILY_QUALITY/100" | tee -a $REPORT_DIR/daily_summary.txt
echo "Goals: $DAILY_GOALS/100" | tee -a $REPORT_DIR/daily_summary.txt
echo "Strategy: $DAILY_STRATEGY/100" | tee -a $REPORT_DIR/daily_summary.txt
echo "DAILY COMPOSITE: $DAILY_COMPOSITE/100" | tee -a $REPORT_DIR/daily_summary.txt

# Generate detailed reports
python -m safla.cli report daily_comprehensive \
    --performance_score=$DAILY_PERF \
    --quality_score=$DAILY_QUALITY \
    --goal_score=$DAILY_GOALS \
    --strategy_score=$DAILY_STRATEGY \
    --composite_score=$DAILY_COMPOSITE \
    --output_dir=$REPORT_DIR \
    --format=html,json,pdf

echo "Daily scoring report completed. Files saved to: $REPORT_DIR"
```

This comprehensive examples documentation provides practical, real-world usage patterns for the Scorer mode, demonstrating effective integration with SAFLA's MCP tools and CLI commands for quantitative evaluation and scoring operations within the aiGI framework.