# AI-generated log analysis script
#!/bin/bash
# Generated with Claude - customized for production environment

LOG_FILE="/var/log/application.log"
ALERT_THRESHOLD=10
TIME_WINDOW="5 minutes ago"

# Extract error patterns from last 5 minutes
recent_errors=$(journalctl --since="$TIME_WINDOW" | \
  grep -E "(ERROR|FATAL|Exception)" | \
  awk '{print $5, $6}' | sort | uniq -c | sort -nr)

# Check for error spike
error_count=$(echo "$recent_errors" | head -1 | awk '{print $1}')

if [[ $error_count -gt $ALERT_THRESHOLD ]]; then
    echo "ALERT: Error spike detected - $error_count errors in last 5 minutes"
    echo "Top error patterns:"
    echo "$recent_errors" | head -5

    # Send to monitoring system
    curl -X POST "$WEBHOOK_URL" -d "{\"alert\":\"error_spike\",\"count\":$error_count}"
fi

# AI-generated monitoring script for database performance
import psycopg2
import json
import requests
from datetime import datetime

def check_database_health():
    """Generated monitoring function - customized for PostgreSQL"""

    queries = {
        'long_running_queries': """
            SELECT pid, now() - pg_stat_activity.query_start AS duration, query 
            FROM pg_stat_activity 
            WHERE (now() - pg_stat_activity.query_start) > interval '5 minutes';
        """,
        'connection_count': """
            SELECT count(*) FROM pg_stat_activity;
        """,
        'blocked_queries': """
            SELECT blocked_locks.pid AS blocked_pid,
                   blocked_activity.usename AS blocked_user,
                   blocking_locks.pid AS blocking_pid,
                   blocking_activity.usename AS blocking_user,
                   blocked_activity.query AS blocked_statement
            FROM pg_catalog.pg_locks blocked_locks
            JOIN pg_catalog.pg_stat_activity blocked_activity 
                 ON blocked_activity.pid = blocked_locks.pid
            JOIN pg_catalog.pg_locks blocking_locks 
                 ON blocking_locks.locktype = blocked_locks.locktype;
        """
    }

    # Execute checks and format results
    results = {}
    for check_name, query in queries.items():
        # Implementation details...
        pass

    return results

#!/bin/bash
# AI-generated configuration sync script

SOURCE_ENV="staging"
TARGET_ENV="production"
CONFIG_PATH="/etc/application/"

# Compare configuration files
compare_configs() {
    echo "Comparing $SOURCE_ENV vs $TARGET_ENV configurations..."

    for config_file in $(find $CONFIG_PATH -name "*.conf"); do
        source_file="${SOURCE_ENV}${config_file}"
        target_file="${TARGET_ENV}${config_file}"

        if ! diff -q "$source_file" "$target_file" > /dev/null; then
            echo "DIFFERENCE: $config_file"
            echo "Run: vimdiff $source_file $target_file"
        fi
    done
}

# Validate configuration syntax
validate_config() {
    echo "Validating configuration syntax..."

    # Application-specific validation
    nginx -t -c $CONFIG_PATH/nginx.conf
    php -l $CONFIG_PATH/app.conf

    echo "Configuration validation complete"
}

# Backup before changes
backup_config() {
    timestamp=$(date +%Y%m%d_%H%M%S)
    backup_dir="/backup/config_$timestamp"

    mkdir -p "$backup_dir"
    cp -r "$CONFIG_PATH" "$backup_dir/"
    echo "Configuration backed up to $backup_dir"
}

# AI-generated deployment validation script
import requests
import time
import json

def validate_deployment(service_url, expected_version):
    """Validate service deployment with health checks"""

    checks = [
        ('health_endpoint', f"{service_url}/health"),
        ('version_endpoint', f"{service_url}/version"),
        ('metrics_endpoint', f"{service_url}/metrics")
    ]

    results = {}

    for check_name, endpoint in checks:
        try:
            response = requests.get(endpoint, timeout=10)
            results[check_name] = {
                'status': response.status_code,
                'response_time': response.elapsed.total_seconds(),
                'data': response.json() if response.headers.get('content-type') == 'application/json' else response.text[:100]
            }
        except Exception as e:
            results[check_name] = {'error': str(e)}

    # Validate version
    if 'version_endpoint' in results and 'data' in results['version_endpoint']:
        actual_version = results['version_endpoint']['data'].get('version')
        results['version_check'] = {
            'expected': expected_version,
            'actual': actual_version,
            'match': actual_version == expected_version
        }

    return results

#!/bin/bash
# AI-generated incident response script

INCIDENT_ID=$1
OUTPUT_DIR="/tmp/incident_$INCIDENT_ID"
mkdir -p "$OUTPUT_DIR"

echo "Collecting diagnostic information for incident $INCIDENT_ID..."

# System information
echo "=== System Information ===" > "$OUTPUT_DIR/system_info.txt"
uname -a >> "$OUTPUT_DIR/system_info.txt"
uptime >> "$OUTPUT_DIR/system_info.txt"
df -h >> "$OUTPUT_DIR/system_info.txt"
free -m >> "$OUTPUT_DIR/system_info.txt"

# Process information
echo "=== Process Information ===" > "$OUTPUT_DIR/processes.txt"
ps aux --sort=-%cpu | head -20 >> "$OUTPUT_DIR/processes.txt"
ps aux --sort=-%mem | head -20 >> "$OUTPUT_DIR/processes.txt"

# Network information
echo "=== Network Information ===" > "$OUTPUT_DIR/network.txt"
ss -tulpn >> "$OUTPUT_DIR/network.txt"
iptables -L >> "$OUTPUT_DIR/network.txt"

# Application logs
echo "=== Recent Application Logs ===" > "$OUTPUT_DIR/app_logs.txt"
journalctl --since="1 hour ago" --no-pager >> "$OUTPUT_DIR/app_logs.txt"

# Create incident package
tar -czf "/tmp/incident_${INCIDENT_ID}_diagnostics.tar.gz" -C /tmp "incident_$INCIDENT_ID"
echo "Diagnostic package created: /tmp/incident_${INCIDENT_ID}_diagnostics.tar.gz"

# AI-generated system utilization report
import psutil
import json
from datetime import datetime, timedelta

def generate_system_report():
    """Generate comprehensive system utilization report"""

    report = {
        'timestamp': datetime.now().isoformat(),
        'hostname': platform.node(),
        'uptime': datetime.now() - datetime.fromtimestamp(psutil.boot_time()),
        'cpu': {
            'usage_percent': psutil.cpu_percent(interval=1),
            'cores': psutil.cpu_count(),
            'load_average': os.getloadavg()
        },
        'memory': {
            'total': psutil.virtual_memory().total,
            'available': psutil.virtual_memory().available,
            'used_percent': psutil.virtual_memory().percent
        },
        'disk': {},
        'network': {},
        'processes': []
    }

    # Disk usage
    for partition in psutil.disk_partitions():
        try:
            usage = psutil.disk_usage(partition.mountpoint)
            report['disk'][partition.mountpoint] = {
                'total': usage.total,
                'used': usage.used,
                'free': usage.free,
                'percent': (usage.used / usage.total) * 100
            }
        except PermissionError:
            continue

    # Network statistics
    net_io = psutil.net_io_counters()
    report['network'] = {
        'bytes_sent': net_io.bytes_sent,
        'bytes_recv': net_io.bytes_recv,
        'packets_sent': net_io.packets_sent,
        'packets_recv': net_io.packets_recv
    }

    # Top processes by CPU and memory
    processes = sorted(psutil.process_iter(['pid', 'name', 'cpu_percent', 'memory_percent']), 
                      key=lambda p: p.info['cpu_percent'], reverse=True)[:10]

    report['processes'] = [p.info for p in processes]

    return report

#!/bin/bash
# AI-generated security audit script

AUDIT_DATE=$(date +%Y%m%d)
REPORT_FILE="/var/log/security_audit_$AUDIT_DATE.log"

echo "Security Audit Report - $AUDIT_DATE" > "$REPORT_FILE"
echo "========================================" >> "$REPORT_FILE"

# Check for users with UID 0
echo "Users with UID 0:" >> "$REPORT_FILE"
awk -F: '$3 == 0 {print $1}' /etc/passwd >> "$REPORT_FILE"

# Check for empty password fields
echo "Users with empty passwords:" >> "$REPORT_FILE"
awk -F: '$2 == "" {print $1}' /etc/shadow >> "$REPORT_FILE"

# Check for world-writable files
echo "World-writable files in system directories:" >> "$REPORT_FILE"
find /etc /usr /var -type f -perm -002 2>/dev/null >> "$REPORT_FILE"

# Check for SUID files
echo "SUID files:" >> "$REPORT_FILE"
find / -type f -perm -4000 2>/dev/null >> "$REPORT_FILE"

# Check open ports
echo "Open network ports:" >> "$REPORT_FILE"
ss -tulpn >> "$REPORT_FILE"

echo "Security audit complete. Report saved to $REPORT_FILE"

# Create AI-assisted development environment
mkdir ~/ai-scripts
cd ~/ai-scripts

# Set up version control
git init
echo "AI-generated scripts and automation tools" > README.md

# Create directory structure
mkdir -p {monitoring,deployment,backup,security,utilities}

# Set up testing environment  
mkdir test-environment
python -m venv test-env
source test-env/bin/activate
pip install pytest black flake8

# Script Generation Template
Task: [What you want to accomplish]
Environment: [OS, shell, available tools]
Requirements: [Performance, security, compatibility needs]
Input: [What data/parameters the script receives]
Output: [Expected results and format]
Error Handling: [How failures should be handled]
Dependencies: [Required tools, permissions, or services]

Example:
Task: Monitor disk usage and alert when any partition exceeds 80%
Environment: Ubuntu 20.04, Bash, systemd
Requirements: Must run as cron job, email alerts, log to syslog
Input: Configuration file with email addresses and thresholds
Output: Email alert with disk usage details, syslog entry
Error Handling: Continue checking other partitions if one fails, alert on script errors
Dependencies: mail command, write access to /var/log

# AI-generated script with security best practices

#!/bin/bash
set -euo pipefail  # Exit on error, undefined vars, pipe failures

# Validate inputs
if [[ $# -ne 2 ]]; then
    echo "Usage: $0 <config_file> <environment>"
    exit 1
fi

CONFIG_FILE="$1"
ENVIRONMENT="$2"

# Validate file permissions and existence
if [[ ! -f "$CONFIG_FILE" ]]; then
    echo "Error: Config file $CONFIG_FILE not found"
    exit 1
fi

if [[ ! -r "$CONFIG_FILE" ]]; then
    echo "Error: Cannot read config file $CONFIG_FILE"
    exit 1
fi

# Validate environment parameter
if [[ ! "$ENVIRONMENT" =~ ^(dev|staging|prod)$ ]]; then
    echo "Error: Environment must be dev, staging, or prod"
    exit 1
fi

# Use secure temporary files
TEMP_FILE=$(mktemp)
trap "rm -f $TEMP_FILE" EXIT

#!/bin/bash
# AI-generated script testing framework

SCRIPT_DIR="$(dirname "${BASH_SOURCE[0]}")"
TEST_RESULTS=()

# Test runner function
run_test() {
    local test_name="$1"
    local test_command="$2"
    local expected_exit_code="${3:-0}"

    echo "Running test: $test_name"

    if eval "$test_command"; then
        if [[ $? -eq $expected_exit_code ]]; then
            echo "PASS: $test_name"
            TEST_RESULTS+=("PASS: $test_name")
        else
            echo "FAIL: $test_name (wrong exit code)"
            TEST_RESULTS+=("FAIL: $test_name")
        fi
    else
        echo "FAIL: $test_name (execution failed)"
        TEST_RESULTS+=("FAIL: $test_name")
    fi
}

# Example tests
run_test "Script exists" "[[ -f $SCRIPT_DIR/monitor.sh ]]"
run_test "Script is executable" "[[ -x $SCRIPT_DIR/monitor.sh ]]"
run_test "Script handles missing args" "$SCRIPT_DIR/monitor.sh" 1
run_test "Script validates config file" "$SCRIPT_DIR/monitor.sh nonexistent.conf prod" 1

# Report results
echo ""
echo "Test Results:"
printf '%s\n' "${TEST_RESULTS[@]}"

Help me create a reusable automation template for [task type]:

Template Requirements:
- Configurable parameters via config file
- Standardized logging and error handling
- Idempotent operations (safe to run multiple times)
- Rollback capabilities for destructive operations
- Integration with our monitoring system

Base Template Should Include:
1. Parameter validation and configuration loading
2. Pre-flight checks and safety validations
3. Main operation with progress tracking
4. Post-operation verification
5. Cleanup and notification

Please generate both the template script and a sample configuration file.

# AI-generated standard library structure
/opt/automation/
├── lib/
│   ├── logging.sh          # Standardized logging functions
│   ├── config.sh           # Configuration file parsing
│   ├── validation.sh       # Input validation utilities
│   ├── notification.sh     # Alert and notification functions
│   └── database.sh         # Database interaction utilities
├── templates/
│   ├── monitoring-script-template.sh
│   ├── deployment-script-template.sh
│   └── backup-script-template.sh
└── scripts/
    ├── monitor-disk-usage.sh
    ├── deploy-application.sh
    └── backup-database.sh

# AI-generated GitHub Actions workflow
name: AI-Assisted Infrastructure Validation

on:
  pull_request:
    paths:
      - 'infrastructure/**'
      - 'scripts/**'

jobs:
  ai-review:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v2

      - name: AI Security Review
        run: |
          # Use AI to review infrastructure changes
          ./scripts/ai-security-review.sh ${{ github.event.pull_request.number }}

      - name: AI Documentation Check  
        run: |
          # Ensure documentation is updated
          ./scripts/ai-docs-check.sh

      - name: AI Test Generation
        run: |
          # Generate tests for new scripts
          ./scripts/ai-test-generator.sh

# AI-generated monitoring integration
def ai_assisted_alert_processing(alert_data):
    """Process alerts with AI assistance for root cause analysis"""

    # Extract relevant context
    context = {
        'alert_type': alert_data['type'],
        'affected_services': alert_data['services'],
        'timeline': alert_data['timeline'],
        'metrics': alert_data['metrics']
    }

    # Generate AI analysis prompt
    prompt = f"""
    Analyze this production alert:
    Alert: {context['alert_type']}
    Services: {context['affected_services']}
    Timeline: {context['timeline']}

    Provide:
    1. Most likely root cause
    2. Recommended investigation steps
    3. Potential quick fixes
    4. Prevention strategies
    """

    # Send to AI service (implementation depends on chosen tool)
    analysis = get_ai_analysis(prompt)

    # Integrate results into incident response
    return {
        'ai_analysis': analysis,
        'suggested_actions': extract_actions(analysis),
        'escalation_recommended': assess_severity(analysis)
    }

# AI Automation Success Template

## Problem Solved
- [Description of manual task eliminated]
- [Time investment required vs. time saved]

## AI Approach
- [Which AI tool was used and why]
- [Prompt template that worked effectively]
- [Customizations needed for our environment]

## Implementation
- [Script or automation created]
- [Testing and validation approach]
- [Integration with existing systems]

## Results
- [Time saved per week/month]
- [Quality improvements]
- [Risk reductions achieved]

## Lessons Learned
- [What worked well]
- [What needed adjustment]
- [Recommendations for similar tasks]

## Reusability
- [How this approach can be applied to other tasks]
- [Template or pattern that others can use]