# Scorer Mode Workflow Documentation

## Overview

The Scorer mode operates as a quantitative evaluation engine within the SAFLA-aiGI framework, providing systematic scoring workflows that integrate with other modes to deliver comprehensive assessment capabilities. This document details the workflow patterns, coordination mechanisms, and integration strategies for effective scoring operations.

## Core Workflow Patterns

### 1. Sequential Scoring Workflow

#### Basic Sequential Pattern
```bash
# Sequential scoring workflow for comprehensive evaluation
echo "Starting sequential scoring workflow..."

# Step 1: Performance Assessment
echo "Phase 1: Performance Scoring"
use_mcp_tool safla analyze_performance_bottlenecks --duration_seconds=300 --include_memory_profile=true
PERF_SCORE=$(python -m safla.cli score performance --json | jq '.score')
echo "Performance Score: $PERF_SCORE/100"

# Step 2: Quality Assessment
echo "Phase 2: Quality Scoring"
use_mcp_tool safla run_integration_tests --parallel=true --verbose=true
QUALITY_SCORE=$(python -m safla.cli score code_quality --include_security=true --json | jq '.score')
echo "Quality Score: $QUALITY_SCORE/100"

# Step 3: Goal Achievement Assessment
echo "Phase 3: Goal Achievement Scoring"
use_mcp_tool safla evaluate_goal_progress --include_recommendations=true
GOAL_SCORE=$(python -m safla.cli score goals --detailed_breakdown=true --json | jq '.score')
echo "Goal Achievement Score: $GOAL_SCORE/100"

# Step 4: Composite Score Calculation
COMPOSITE_SCORE=$(echo "scale=2; ($PERF_SCORE * 0.4) + ($QUALITY_SCORE * 0.3) + ($GOAL_SCORE * 0.3)" | bc)
echo "Composite System Score: $COMPOSITE_SCORE/100"

# Step 5: Report Generation
python -m safla.cli report comprehensive_scoring --composite_score=$COMPOSITE_SCORE --include_recommendations=true
```

#### Detailed Sequential Workflow
```bash
# Comprehensive sequential scoring with detailed analysis
echo "Executing detailed sequential scoring workflow..."

# Phase 1: System Health and Performance
echo "=== Phase 1: System Health and Performance ==="
use_mcp_tool safla get_system_info
use_mcp_tool safla monitor_system_health --check_interval=30 --alert_thresholds='{"cpu": 80, "memory": 85}'
use_mcp_tool safla analyze_performance_bottlenecks --duration_seconds=600 --include_memory_profile=true

SYSTEM_HEALTH=$(python -m safla.cli score system_health --comprehensive=true --json | jq '.score')
PERFORMANCE_SCORE=$(python -m safla.cli score performance --detailed_analysis=true --json | jq '.score')

echo "System Health Score: $SYSTEM_HEALTH/100"
echo "Performance Score: $PERFORMANCE_SCORE/100"

# Phase 2: Quality and Testing
echo "=== Phase 2: Quality and Testing ==="
use_mcp_tool safla run_integration_tests --test_suite="comprehensive" --parallel=true
use_mcp_tool safla validate_memory_operations --test_data_size=100 --include_stress_test=true
use_mcp_tool safla test_mcp_connectivity --test_depth="comprehensive"

TEST_QUALITY=$(python -m safla.cli score test_coverage --minimum_threshold=85 --json | jq '.score')
CODE_QUALITY=$(python -m safla.cli score code_quality --complexity_analysis=true --json | jq '.score')
SECURITY_SCORE=$(python -m safla.cli score security --vulnerability_scan=true --json | jq '.score')

echo "Test Quality Score: $TEST_QUALITY/100"
echo "Code Quality Score: $CODE_QUALITY/100"
echo "Security Score: $SECURITY_SCORE/100"

# Phase 3: Goals and Strategy
echo "=== Phase 3: Goals and Strategy ==="
use_mcp_tool safla list_goals --status_filter="all" --priority_filter="all"
use_mcp_tool safla evaluate_goal_progress --include_recommendations=true
use_mcp_tool safla evaluate_strategy_performance --evaluation_period_hours=168

GOAL_ACHIEVEMENT=$(python -m safla.cli score goals --portfolio_view=true --json | jq '.score')
STRATEGY_EFFECTIVENESS=$(python -m safla.cli score strategies --effectiveness_analysis=true --json | jq '.score')

echo "Goal Achievement Score: $GOAL_ACHIEVEMENT/100"
echo "Strategy Effectiveness Score: $STRATEGY_EFFECTIVENESS/100"

# Phase 4: Learning and Adaptation
echo "=== Phase 4: Learning and Adaptation ==="
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=$(python -m safla.cli score learning --adaptation_metrics=true --json | jq '.score')
ADAPTATION_SCORE=$(python -m safla.cli score adaptation --behavioral_consistency=true --json | jq '.score')

echo "Learning Score: $LEARNING_SCORE/100"
echo "Adaptation Score: $ADAPTATION_SCORE/100"

# Phase 5: Composite Analysis and Reporting
echo "=== Phase 5: Composite Analysis ==="
WEIGHTED_COMPOSITE=$(echo "scale=2; ($SYSTEM_HEALTH * 0.15) + ($PERFORMANCE_SCORE * 0.15) + ($TEST_QUALITY * 0.12) + ($CODE_QUALITY * 0.12) + ($SECURITY_SCORE * 0.11) + ($GOAL_ACHIEVEMENT * 0.15) + ($STRATEGY_EFFECTIVENESS * 0.1) + ($LEARNING_SCORE * 0.05) + ($ADAPTATION_SCORE * 0.05)" | bc)

echo "=== COMPREHENSIVE SCORING RESULTS ==="
echo "Weighted Composite Score: $WEIGHTED_COMPOSITE/100"
python -m safla.cli report detailed_scoring --all_dimensions=true --composite_score=$WEIGHTED_COMPOSITE
```

### 2. Parallel Scoring Workflow

#### Concurrent Assessment Pattern
```bash
# Parallel scoring workflow for efficiency
echo "Starting parallel scoring workflow..."

# Launch parallel scoring processes
{
    echo "Performance scoring started..."
    use_mcp_tool safla analyze_performance_bottlenecks --duration_seconds=300 &
    PERF_PID=$!
    
    echo "Quality scoring started..."
    use_mcp_tool safla run_integration_tests --parallel=true &
    QUALITY_PID=$!
    
    echo "Goal scoring started..."
    use_mcp_tool safla evaluate_goal_progress --include_recommendations=true &
    GOAL_PID=$!
    
    echo "Strategy scoring started..."
    use_mcp_tool safla evaluate_strategy_performance --evaluation_period_hours=168 &
    STRATEGY_PID=$!
    
    # Wait for all processes to complete
    wait $PERF_PID
    wait $QUALITY_PID
    wait $GOAL_PID
    wait $STRATEGY_PID
    
    echo "All parallel scoring processes completed"
}

# Collect results and calculate composite score
PERF_SCORE=$(python -m safla.cli score performance --json | jq '.score')
QUALITY_SCORE=$(python -m safla.cli score code_quality --json | jq '.score')
GOAL_SCORE=$(python -m safla.cli score goals --json | jq '.score')
STRATEGY_SCORE=$(python -m safla.cli score strategies --json | jq '.score')

PARALLEL_COMPOSITE=$(echo "scale=2; ($PERF_SCORE * 0.3) + ($QUALITY_SCORE * 0.25) + ($GOAL_SCORE * 0.25) + ($STRATEGY_SCORE * 0.2)" | bc)
echo "Parallel Composite Score: $PARALLEL_COMPOSITE/100"
```

### 3. Adaptive Scoring Workflow

#### Context-Aware Adaptive Pattern
```bash
# Adaptive scoring workflow that adjusts based on context
echo "Starting adaptive scoring workflow..."

# Determine current context and priorities
SYSTEM_CONTEXT=$(use_mcp_tool safla get_system_info | jq -r '.current_context')
PRIORITY_FOCUS=$(access_mcp_resource safla safla://goals | jq -r '.current_priority_focus')

echo "System Context: $SYSTEM_CONTEXT"
echo "Priority Focus: $PRIORITY_FOCUS"

# Adapt scoring weights based on context
case $SYSTEM_CONTEXT in
    "development")
        PERF_WEIGHT=0.2
        QUALITY_WEIGHT=0.4
        GOAL_WEIGHT=0.3
        LEARNING_WEIGHT=0.1
        echo "Development context: Emphasizing quality and goals"
        ;;
    "production")
        PERF_WEIGHT=0.4
        QUALITY_WEIGHT=0.2
        GOAL_WEIGHT=0.2
        LEARNING_WEIGHT=0.2
        echo "Production context: Emphasizing performance and learning"
        ;;
    "optimization")
        PERF_WEIGHT=0.5
        QUALITY_WEIGHT=0.2
        GOAL_WEIGHT=0.2
        LEARNING_WEIGHT=0.1
        echo "Optimization context: Emphasizing performance"
        ;;
    *)
        PERF_WEIGHT=0.25
        QUALITY_WEIGHT=0.25
        GOAL_WEIGHT=0.25
        LEARNING_WEIGHT=0.25
        echo "Balanced context: Equal weighting"
        ;;
esac

# Execute context-adapted scoring
PERF_SCORE=$(python -m safla.cli score performance --context=$SYSTEM_CONTEXT --json | jq '.score')
QUALITY_SCORE=$(python -m safla.cli score code_quality --context=$SYSTEM_CONTEXT --json | jq '.score')
GOAL_SCORE=$(python -m safla.cli score goals --priority_focus=$PRIORITY_FOCUS --json | jq '.score')
LEARNING_SCORE=$(python -m safla.cli score learning --context=$SYSTEM_CONTEXT --json | jq '.score')

# Calculate adaptive composite score
ADAPTIVE_COMPOSITE=$(echo "scale=2; ($PERF_SCORE * $PERF_WEIGHT) + ($QUALITY_SCORE * $QUALITY_WEIGHT) + ($GOAL_SCORE * $GOAL_WEIGHT) + ($LEARNING_SCORE * $LEARNING_WEIGHT)" | bc)

echo "Adaptive Scoring Results:"
echo "  Performance: $PERF_SCORE/100 (weight: $PERF_WEIGHT)"
echo "  Quality: $QUALITY_SCORE/100 (weight: $QUALITY_WEIGHT)"
echo "  Goals: $GOAL_SCORE/100 (weight: $GOAL_WEIGHT)"
echo "  Learning: $LEARNING_SCORE/100 (weight: $LEARNING_WEIGHT)"
echo "  Adaptive Composite: $ADAPTIVE_COMPOSITE/100"
```

## Mode Integration Workflows

### 1. Critic-Scorer Collaboration

#### Qualitative-Quantitative Integration
```bash
# Integrated Critic-Scorer workflow for comprehensive analysis
echo "Starting Critic-Scorer collaborative workflow..."

# Phase 1: Critic performs qualitative analysis
echo "=== Phase 1: Critic Qualitative Analysis ==="
new_task critic "Perform comprehensive qualitative analysis of system performance, code quality, and goal achievement. Focus on identifying patterns, root causes, and improvement opportunities."

# Wait for critic analysis completion
echo "Waiting for critic analysis to complete..."
# (In practice, this would involve checking task completion status)

# Phase 2: Scorer performs quantitative assessment
echo "=== Phase 2: Scorer Quantitative Assessment ==="

# Performance scoring with detailed metrics
use_mcp_tool safla analyze_performance_bottlenecks --duration_seconds=300 --include_memory_profile=true
use_mcp_tool safla benchmark_memory_performance --test_duration=120 --memory_patterns=["sequential","random","mixed"]
PERF_SCORE=$(python -m safla.cli score performance --comprehensive=true --json | jq '.score')

# Quality scoring with detailed breakdown
use_mcp_tool safla run_integration_tests --test_suite="comprehensive" --parallel=true
use_mcp_tool safla validate_memory_operations --test_data_size=50 --include_stress_test=true
QUALITY_SCORE=$(python -m safla.cli score code_quality --detailed_analysis=true --json | jq '.score')

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

echo "Quantitative Scores:"
echo "  Performance: $PERF_SCORE/100"
echo "  Quality: $QUALITY_SCORE/100"
echo "  Goals: $GOAL_SCORE/100"

# Phase 3: Synthesis and integrated reporting
echo "=== Phase 3: Integrated Analysis Synthesis ==="
COMPOSITE_SCORE=$(echo "scale=2; ($PERF_SCORE * 0.4) + ($QUALITY_SCORE * 0.3) + ($GOAL_SCORE * 0.3)" | bc)

# Generate integrated report combining qualitative and quantitative insights
python -m safla.cli report integrated_analysis \
    --critic_insights="critic_analysis_results.json" \
    --quantitative_scores="performance:$PERF_SCORE,quality:$QUALITY_SCORE,goals:$GOAL_SCORE" \
    --composite_score=$COMPOSITE_SCORE \
    --synthesis_mode=comprehensive

echo "Integrated Analysis Complete - Composite Score: $COMPOSITE_SCORE/100"
```

### 2. Orchestrator-Scorer Coordination

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

# Phase 1: Orchestrator defines scoring strategy
echo "=== Phase 1: Scoring Strategy Definition ==="
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: Scorer executes orchestrated scoring plan
echo "=== Phase 2: Orchestrated Scoring Execution ==="

# Strategic performance scoring
STRATEGIC_PRIORITIES=$(access_mcp_resource safla safla://goals | jq -r '.strategic_priorities[]')
for priority in $STRATEGIC_PRIORITIES; do
    echo "Scoring priority: $priority"
    PRIORITY_SCORE=$(python -m safla.cli score strategic_priority --priority="$priority" --json | jq '.score')
    echo "  $priority Score: $PRIORITY_SCORE/100"
done

# Stakeholder-focused 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')
echo "Stakeholder Satisfaction Score: $STAKEHOLDER_SCORE/100"

# 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')
echo "Strategic Objectives Score: $STRATEGIC_OBJECTIVE_SCORE/100"

# Phase 3: Orchestrator synthesis and decision making
echo "=== Phase 3: Strategic Synthesis ==="
STRATEGIC_COMPOSITE=$(echo "scale=2; ($STAKEHOLDER_SCORE * 0.4) + ($STRATEGIC_OBJECTIVE_SCORE * 0.6)" | bc)

new_task orchestrator "Synthesize scoring results and make strategic recommendations based on composite score of $STRATEGIC_COMPOSITE/100. Include resource allocation recommendations and priority adjustments."

echo "Strategic Scoring Complete - Strategic Composite: $STRATEGIC_COMPOSITE/100"
```

### 3. TDD-Scorer Integration

#### Test-Driven Scoring Workflow
```bash
# TDD-Scorer integrated workflow for quality-driven scoring
echo "Starting TDD-Scorer integration workflow..."

# Phase 1: TDD defines quality criteria and test specifications
echo "=== Phase 1: TDD Quality Criteria Definition ==="
new_task tdd "Define comprehensive test specifications and quality criteria for scoring validation. Include performance benchmarks, quality thresholds, and acceptance criteria."

# Phase 2: Scorer validates against TDD criteria
echo "=== Phase 2: TDD-Driven Scoring Validation ==="

# Test coverage scoring against TDD specifications
use_mcp_tool safla run_integration_tests --test_suite="tdd_comprehensive" --parallel=true
TDD_COVERAGE_SCORE=$(python -m safla.cli score test_coverage --tdd_specifications=true --json | jq '.score')

# Quality scoring against TDD criteria
TDD_QUALITY_SCORE=$(python -m safla.cli score code_quality --tdd_criteria=true --json | jq '.score')

# Performance scoring against TDD benchmarks
use_mcp_tool safla benchmark_memory_performance --test_duration=120 --tdd_benchmarks=true
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 refinement
echo "=== Phase 3: TDD Validation and Refinement ==="
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: $TDD_COMPOSITE/100"
    new_task tdd "Refine test specifications and quality criteria based on scoring results. Address gaps identified in coverage ($TDD_COVERAGE_SCORE), quality ($TDD_QUALITY_SCORE), and performance ($TDD_PERFORMANCE_SCORE)."
else
    echo "TDD validation passed - Score: $TDD_COMPOSITE/100"
fi
```

## Continuous Scoring Workflows

### 1. Real-Time Monitoring Workflow

#### Continuous Performance Scoring
```bash
# Continuous real-time scoring workflow
echo "Starting continuous scoring monitoring workflow..."

MONITORING_INTERVAL=300  # 5 minutes
ALERT_THRESHOLD=75
TREND_WINDOW=12  # 12 measurements for trend analysis

# Initialize scoring history
SCORE_HISTORY=()
TIMESTAMP_HISTORY=()

while true; do
    CURRENT_TIME=$(date '+%Y-%m-%d %H:%M:%S')
    echo "[$CURRENT_TIME] Executing continuous scoring cycle..."
    
    # Real-time performance scoring
    PERF_SCORE=$(python -m safla.cli score performance --real_time=true --json | jq '.score')
    
    # Real-time quality scoring
    QUALITY_SCORE=$(python -m safla.cli score code_quality --real_time=true --json | jq '.score')
    
    # Real-time system health scoring
    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')
    
    # Calculate real-time composite score
    CURRENT_COMPOSITE=$(echo "scale=2; ($PERF_SCORE * 0.4) + ($QUALITY_SCORE * 0.3) + ($HEALTH_SCORE * 0.3)" | bc)
    
    # Update scoring history
    SCORE_HISTORY+=($CURRENT_COMPOSITE)
    TIMESTAMP_HISTORY+=($CURRENT_TIME)
    
    # Maintain rolling window
    if [ ${#SCORE_HISTORY[@]} -gt $TREND_WINDOW ]; then
        SCORE_HISTORY=("${SCORE_HISTORY[@]:1}")
        TIMESTAMP_HISTORY=("${TIMESTAMP_HISTORY[@]:1}")
    fi
    
    echo "Current Composite Score: $CURRENT_COMPOSITE/100"
    
    # Trend analysis
    if [ ${#SCORE_HISTORY[@]} -ge 3 ]; then
        RECENT_SCORES=(${SCORE_HISTORY[@]: -3})
        TREND_DIRECTION="stable"
        
        if (( $(echo "${RECENT_SCORES[2]} > ${RECENT_SCORES[1]} && ${RECENT_SCORES[1]} > ${RECENT_SCORES[0]}" | bc -l) )); then
            TREND_DIRECTION="improving"
        elif (( $(echo "${RECENT_SCORES[2]} < ${RECENT_SCORES[1]} && ${RECENT_SCORES[1]} < ${RECENT_SCORES[0]}" | bc -l) )); then
            TREND_DIRECTION="declining"
        fi
        
        echo "Score Trend: $TREND_DIRECTION"
    fi
    
    # Alert handling
    if (( $(echo "$CURRENT_COMPOSITE < $ALERT_THRESHOLD" | bc -l) )); then
        echo "ALERT: Composite score below threshold ($ALERT_THRESHOLD)"
        python -m safla.cli alert low_score --score=$CURRENT_COMPOSITE --threshold=$ALERT_THRESHOLD
        
        # Trigger detailed analysis
        new_task critic "Investigate low scoring conditions. Current composite score: $CURRENT_COMPOSITE/100. Analyze root causes and recommend immediate actions."
    fi
    
    # Log scoring data
    echo "$CURRENT_TIME,$PERF_SCORE,$QUALITY_SCORE,$HEALTH_SCORE,$CURRENT_COMPOSITE" >> continuous_scores.log
    
    sleep $MONITORING_INTERVAL
done
```

### 2. Scheduled Scoring Workflow

#### Daily Comprehensive Scoring
```bash
# Daily comprehensive scoring workflow
echo "Starting daily comprehensive scoring workflow..."

DAILY_LOG_FILE="daily_scores_$(date +%Y%m%d).log"
REPORT_DIR="daily_reports"

# Create report directory if it doesn't exist
mkdir -p $REPORT_DIR

echo "=== Daily Comprehensive Scoring - $(date) ===" | tee $DAILY_LOG_FILE

# Performance dimension scoring
echo "Scoring Performance Dimension..." | tee -a $DAILY_LOG_FILE
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"]
use_mcp_tool safla benchmark_vector_operations --vector_count=10000 --vector_dimensions=1024

DAILY_PERF_SCORE=$(python -m safla.cli score performance --comprehensive=true --daily_analysis=true --json | jq '.score')
echo "Daily Performance Score: $DAILY_PERF_SCORE/100" | tee -a $DAILY_LOG_FILE

# Quality dimension scoring
echo "Scoring Quality Dimension..." | tee -a $DAILY_LOG_FILE
use_mcp_tool safla run_integration_tests --test_suite="daily_comprehensive" --parallel=true
use_mcp_tool safla validate_memory_operations --test_data_size=100 --include_stress_test=true

DAILY_QUALITY_SCORE=$(python -m safla.cli score code_quality --comprehensive=true --daily_analysis=true --json | jq '.score')
DAILY_TEST_SCORE=$(python -m safla.cli score test_coverage --comprehensive=true --daily_analysis=true --json | jq '.score')
echo "Daily Quality Score: $DAILY_QUALITY_SCORE/100" | tee -a $DAILY_LOG_FILE
echo "Daily Test Score: $DAILY_TEST_SCORE/100" | tee -a $DAILY_LOG_FILE

# Goal achievement scoring
echo "Scoring Goal Achievement..." | tee -a $DAILY_LOG_FILE
use_mcp_tool safla evaluate_goal_progress --include_recommendations=true
use_mcp_tool safla list_goals --status_filter="all" --priority_filter="all"

DAILY_GOAL_SCORE=$(python -m safla.cli score goals --daily_analysis=true --portfolio_view=true --json | jq '.score')
echo "Daily Goal Achievement Score: $DAILY_GOAL_SCORE/100" | tee -a $DAILY_LOG_FILE

# Strategy effectiveness scoring
echo "Scoring Strategy Effectiveness..." | tee -a $DAILY_LOG_FILE
use_mcp_tool safla evaluate_strategy_performance --evaluation_period_hours=168

DAILY_STRATEGY_SCORE=$(python -m safla.cli score strategies --daily_analysis=true --effectiveness_analysis=true --json | jq '.score')
echo "Daily Strategy Score: $DAILY_STRATEGY_SCORE/100" | tee -a $DAILY_LOG_FILE

# Learning and adaptation scoring
echo "Scoring Learning and Adaptation..." | tee -a $DAILY_LOG_FILE
use_mcp_tool safla get_learning_metrics --metric_type="all" --time_range_hours=24
use_mcp_tool safla analyze_adaptation_patterns --pattern_type="all" --analysis_depth="detailed" --time_window_days=1

DAILY_LEARNING_SCORE=$(python -m safla.cli score learning --daily_analysis=true --json | jq '.score')
echo "Daily Learning Score: $DAILY_LEARNING_SCORE/100" | tee -a $DAILY_LOG_FILE

# Calculate daily composite score
DAILY_COMPOSITE=$(echo "scale=2; ($DAILY_PERF_SCORE * 0.25) + ($DAILY_QUALITY_SCORE * 0.2) + ($DAILY_TEST_SCORE * 0.15) + ($DAILY_GOAL_SCORE * 0.2) + ($DAILY_STRATEGY_SCORE * 0.1) + ($DAILY_LEARNING_SCORE * 0.1)" | bc)

echo "=== DAILY COMPOSITE SCORE: $DAILY_COMPOSITE/100 ===" | tee -a $DAILY_LOG_FILE

# Generate daily report
python -m safla.cli report daily_comprehensive \
    --performance_score=$DAILY_PERF_SCORE \
    --quality_score=$DAILY_QUALITY_SCORE \
    --test_score=$DAILY_TEST_SCORE \
    --goal_score=$DAILY_GOAL_SCORE \
    --strategy_score=$DAILY_STRATEGY_SCORE \
    --learning_score=$DAILY_LEARNING_SCORE \
    --composite_score=$DAILY_COMPOSITE \
    --output_dir=$REPORT_DIR \
    --format=html,json,pdf

echo "Daily scoring workflow completed. Reports generated in $REPORT_DIR"
```

## Error Handling and Recovery Workflows

### 1. Scoring Failure Recovery

#### Graceful Degradation Workflow
```bash
# Scoring failure recovery workflow
echo "Starting scoring failure recovery workflow..."

score_with_fallback() {
    local scoring_function=$1
    local fallback_score=$2
    local max_retries=3
    local retry_count=0
    
    while [ $retry_count -lt $max_retries ]; do
        echo "Attempting $scoring_function (attempt $((retry_count + 1))/$max_retries)..."
        
        if RESULT=$(eval $scoring_function 2>/dev/null); then
            echo $RESULT
            return 0
        else
            echo "Scoring attempt failed, retrying..."
            retry_count=$((retry_count + 1))
            sleep 5
        fi
    done
    
    echo "All scoring attempts failed, using fallback score: $fallback_score"
    echo $fallback_score
    return 1
}

# Execute scoring with fallback mechanisms
PERF_SCORE=$(score_with_fallback "python -m safla.cli score performance --json | jq '.score'" "75")
QUALITY_SCORE=$(score_with_fallback "python -m safla.cli score code_quality --json | jq '.score'" "80")
GOAL_SCORE=$(score_with_fallback "python -m safla.cli score goals --json | jq '.score'" "70")

# Calculate composite with error handling
if [ $? -eq 0 ]; then
    COMPOSITE_SCORE=$(echo "scale=2; ($PERF_SCORE * 0.4) + ($QUALITY_SCORE * 0.3) + ($GOAL_SCORE * 0.3)" | bc)
    echo "Composite Score (with potential fallbacks): $COMPOSITE_SCORE/100"
else
    echo "Multiple scoring failures detected - using emergency baseline score"
    COMPOSITE_SCORE=65
fi
```

This comprehensive workflow documentation provides the Scorer mode with detailed patterns for implementing effective quantitative evaluation workflows that integrate seamlessly with other modes in the SAFLA-aiGI framework.