xps

Now that you have the foundation, let's explore advanced patterns.

Pattern 1: Multi-Task Automation

Add multiple tasks to your automation handler:

# In automation-handler.sh, under "6pm wake" section:

# Task 1: Backup critical files
log_message "[6pm] Running backup..."
rsync -av ~/Documents/ ~/Backups/Documents/ >> "$LOG_FILE" 2>&1

# Task 2: Process research data
log_message "[6pm] Processing research data..."
python3 ~/scripts/process-research.py >> "$LOG_FILE" 2>&1

# Task 3: Generate reports
log_message "[6pm] Generating reports..."
Rscript ~/scripts/generate-reports.R >> "$LOG_FILE" 2>&1

log_message "[6pm] All tasks complete"

Pattern 2: Error Handling with Notifications

Add macOS notifications when tasks fail:

# Add to wake-utils.sh:

send_notification() {
    local title="$1"
    local message="$2"

    osascript -e "display notification \"$message\" with title \"$title\""
}

run_task_with_error_handling() {
    local task_name="$1"
    local task_command="$2"

    log_message "[$task_name] Starting..."

    if eval "$task_command"; then
        log_message "[$task_name] Success"
        return 0
    else
        log_message "[$task_name] FAILED"
        send_notification "Automation Error" "$task_name failed"
        return 1
    fi
}

# Usage in automation-handler.sh:
run_task_with_error_handling "Backup" "rsync -av ~/Documents/ ~/Backups/Documents/"

Pattern 3: Conditional Execution

Run tasks only under certain conditions:

# Add to automation-handler.sh:

should_run_backup() {
    # Only backup on weekdays
    local day=$(date +%u)  # 1-7, Monday-Sunday
    [ "$day" -le 5 ]  # Returns true if weekday
}

should_run_heavy_processing() {
    # Only run heavy tasks if plugged in
    pmstat -g batt | grep -q "AC Power"
}

# In main execution:
if should_run_backup; then
    run_task_with_error_handling "Backup" "rsync -av ~/Documents/ ~/Backups/Documents/"
fi

if should_run_heavy_processing; then
    run_task_with_error_handling "ML Training" "python3 ~/ml/train-model.py"
fi

Pattern 4: Dynamic Scheduling

Adjust wake times based on workload:

# Add to automation-handler.sh:

calculate_next_wake_time() {
    local pending_tasks=$(count_pending_tasks)

    if [ "$pending_tasks" -gt 100 ]; then
        # Heavy workload: wake every 2 hours
        echo "$(date -v+2H +%H)"
    elif [ "$pending_tasks" -gt 10 ]; then
        # Medium workload: wake every 4 hours
        echo "$(date -v+4H +%H)"
    else
        # Light workload: wake once daily
        echo "18"  # 6pm tomorrow
    fi
}

# In 6pm wake section:
local next_hour=$(calculate_next_wake_time)
schedule_next_wake "$next_hour"

Pattern 5: Integration with Claude Code

Run Claude Code as part of your automation:

# Create claude-automation-task.sh:

#!/bin/bash

PROMPT="Analyze today's research papers and create a summary"
OUTPUT_FILE="$HOME/automation-projects/claude-pipeline/outputs/summary-$(date +%Y%m%d).md"

# Use Claude API (requires API key)
curl https://api.anthropic.com/v1/messages \
  -H "Content-Type: application/json" \
  -H "x-api-key: $ANTHROPIC_API_KEY" \
  -H "anthropic-version: 2023-06-01" \
  -d "{
    \"model\": \"claude-sonnet-4.5-20250929\",
    \"max_tokens\": 4096,
    \"messages\": [{
      \"role\": \"user\",
      \"content\": \"$PROMPT\"
    }]
  }" > "$OUTPUT_FILE"

echo "Claude automation complete: $OUTPUT_FILE"

Add to your automation handler:

# In automation-handler.sh, 6pm section:
log_message "[6pm] Running Claude research automation..."
bash ~/automation-projects/claude-pipeline/scripts/claude-automation-task.sh >> "$LOG_FILE" 2>&1

Pattern 6: Metrics and Optimization

Track automation performance:

# Create metrics-tracker.sh:

#!/bin/bash

METRICS_FILE="$HOME/automation-projects/claude-pipeline/logs/metrics.csv"

# Initialize CSV if doesn't exist
if [ ! -f "$METRICS_FILE" ]; then
    echo "timestamp,task,duration_seconds,status,cpu_avg,memory_mb" > "$METRICS_FILE"
fi

track_task() {
    local task_name="$1"
    local start_time=$(date +%s)
    local start_cpu=$(ps aux | awk '{sum+=$3} END {print sum}')
    local start_mem=$(ps aux | awk '{sum+=$4} END {print sum}')

    # Run the task
    eval "$2"
    local status=$?

    local end_time=$(date +%s)
    local duration=$((end_time - start_time))
    local end_cpu=$(ps aux | awk '{sum+=$3} END {print sum}')
    local end_mem=$(ps aux | awk '{sum+=$4} END {print sum}')
    local avg_cpu=$(awk "BEGIN {print ($start_cpu + $end_cpu) / 2}")
    local avg_mem=$(awk "BEGIN {print ($start_mem + $end_mem) / 2}")

    # Log to CSV
    echo "$(date -Iseconds),$task_name,$duration,$status,$avg_cpu,$avg_mem" >> "$METRICS_FILE"

    return $status
}

# Usage:
track_task "Backup" "rsync -av ~/Documents/ ~/Backups/Documents/"
track_task "DataProcessing" "python3 ~/scripts/process.py"

Analyze metrics:

# View average task durations
awk -F, 'NR>1 {sum[$2]+=$3; count[$2]++} END {for (task in sum) print task, sum[task]/count[task]}' metrics.csv

# View tasks by failure rate
awk -F, 'NR>1 {total[$2]++; if($4!=0) fail[$2]++} END {for (task in total) print task, (fail[task]+0)/total[task]*100"%"}' metrics.csv

Chapter 5: Advanced Techniques