Scripting & Automation

🤖 Scripting & Automation Guide

Learn how to automate NaviDuck, integrate it into your workflows, and create powerful scripts that extend its capabilities.

📋 Table of Contents

• Quick Start
• Command-Line Automation
• Python API & Integration
• Scheduled Tasks
• Web Automation
• Data Extraction & Analysis
• System Integration
• Troubleshooting
• Pro Tips

---

⚡ Quick Start

Automate in 5 Minutes

1. Basic Script Example

Create search_script.sh:

#!/bin/bash
# Search for a term and save results
python naviduck.py << 'EOF'
s "$1"
# Results automatically displayed
quit
EOF

Run it:

chmod +x search_script.sh
./search_script.sh "python tutorial"

2. Python One-Liner

# Quick search from Python
import subprocess
result = subprocess.run(
    ['python', 'naviduck.py'],
    input=b's python tutorial\nquit\n',
    capture_output=True,
    text=True
)
print(result.stdout)

3. Scheduled Daily Search

# Add to crontab
crontab -e
# Add: 0 9 * * * /path/to/search_script.sh "daily news"

---

💻 Command-Line Automation

Basic Input Redirection

Simple Search Script

#!/bin/bash
# search_term.sh - Search and capture results
TERM="$1"
python naviduck.py << EOF
s $TERM
quit
EOF

Multiple Commands

#!/bin/bash
# research_script.sh - Complete research workflow
TOPIC="$1"

python naviduck.py << EOF
clear
s $TOPIC
1          # Open first result
b          # Bookmark it
h          # History
q          # Back to main
ai explain $TOPIC
quit
EOF

Capture Output to File

#!/bin/bash
# search_to_file.sh
QUERY="$1"
OUTPUT_FILE="${2:-results.txt}"

python naviduck.py << EOF | tee "$OUTPUT_FILE"
s $QUERY
quit
EOF

echo "Results saved to $OUTPUT_FILE"

Advanced Shell Scripting

Interactive Script

#!/bin/bash
# interactive_search.sh
echo "What would you like to search for?"
read -r query

python naviduck.py << EOF
s $query

# Pause for user to view results
echo "Press Enter to continue..."
read -r

quit
EOF

Batch Processing

#!/bin/bash
# batch_search.sh
SEARCHES=("python tutorial" "machine learning" "web development")

for search in "${SEARCHES[@]}"; do
    echo "=== Searching: $search ==="
    python naviduck.py << EOF
s $search
sleep 2  # Wait for results display
quit
EOF
    echo ""  # Blank line
done

Error Handling

#!/bin/bash
# robust_search.sh
set -e  # Exit on error

search_term="$1"

if [ -z "$search_term" ]; then
    echo "Error: No search term provided"
    exit 1
fi

if ! command -v python &> /dev/null; then
    echo "Error: Python not found"
    exit 1
fi

# Timeout after 30 seconds
timeout 30 python naviduck.py << EOF
s $search_term
quit
EOF

if [ $? -eq 124 ]; then
    echo "Error: Search timed out"
    exit 1
fi

Using Expect for Complex Automation

Expect Script Example

#!/usr/bin/expect -f
# naviduck_automation.exp
set timeout 30
set query [lindex $argv 0]

spawn python naviduck.py

expect "naviduck>"
send "s $query\r"

expect {
    "Select an option:" {
        send "1\r"  # Open first result
        exp_continue
    }
    "Page Actions:" {
        send "b\r"  # Bookmark
        exp_continue
    }
    "naviduck>" {
        send "quit\r"
    }
    timeout {
        send_user "Timeout occurred\n"
        exit 1
    }
}

expect eof

Install and Use Expect

# Install expect
sudo apt install expect  # Ubuntu/Debian
sudo yum install expect  # RHEL/CentOS
brew install expect      # macOS

# Run expect script
expect naviduck_automation.exp "python tutorial"

---

🐍 Python API & Integration

Direct Module Import

Import NaviDuck Components

# naviduck_integration.py
import sys
import os

# Add NaviDuck to path
sys.path.insert(0, '/path/to/naviduck')

# Import core components
from naviduck import BrowserState, NetworkManager, SearchManager, PageLoader

# Initialize components
state = BrowserState()
network = NetworkManager(state)
search_mgr = SearchManager(state, network)
page_loader = PageLoader(state, network)

# Use components directly
results = search_mgr.search("python tutorial")
for result in results[:3]:
    print(f"Title: {result['title']}")
    print(f"URL: {result['url']}")
    print()

Custom Search Function

def search_and_analyze(query, max_results=5):
    """Search and return analyzed results"""
    results = search_mgr.search(query)
    
    analysis = {
        'query': query,
        'total_results': len(results),
        'engines_used': list(set(r['engine'] for r in results)),
        'domains': list(set(urlparse(r['url']).netloc for r in results)),
        'top_results': results[:max_results]
    }
    
    return analysis

# Usage
analysis = search_and_analyze("artificial intelligence")
print(f"Found {analysis['total_results']} results")
for result in analysis['top_results']:
    print(f"- {result['title']}")

Subprocess Integration

Wrapper Class

# naviduck_wrapper.py
import subprocess
import json
import tempfile
from pathlib import Path

class NaviDuckWrapper:
    def __init__(self, naviduck_path="naviduck.py"):
        self.naviduck_path = Path(naviduck_path)
        
    def execute_command(self, command, capture_output=True):
        """Execute a NaviDuck command"""
        cmd = f"{command}\nquit\n"
        
        result = subprocess.run(
            ['python', str(self.naviduck_path)],
            input=cmd.encode('utf-8'),
            capture_output=capture_output,
            text=True,
            timeout=30
        )
        
        return result
    
    def search(self, query, engine=None):
        """Perform a search"""
        cmd = f"s {query}" if not engine else f"search {engine} {query}"
        result = self.execute_command(cmd)
        return self._parse_search_output(result.stdout)
    
    def ask_ai(self, question):
        """Ask NavAI a question"""
        result = self.execute_command(f"ai {question}")
        return self._parse_ai_output(result.stdout)
    
    def _parse_search_output(self, output):
        """Extract structured data from search output"""
        # Simple parsing - can be enhanced
        lines = output.split('\n')
        results = []
        current_result = {}
        
        for line in lines:
            if line.strip().startswith('1.') or line.strip().startswith('2.'):
                if current_result:
                    results.append(current_result)
                current_result = {'title': line[3:].strip()}
            elif 'http' in line and current_result:
                current_result['url'] = line.strip()
            elif line.strip() and '─' not in line and current_result:
                current_result.setdefault('snippet', '')
                current_result['snippet'] += ' ' + line.strip()
        
        if current_result:
            results.append(current_result)
        
        return results
    
    def _parse_ai_output(self, output):
        """Extract AI response"""
        # Look for AI response pattern
        lines = output.split('\n')
        in_response = False
        response = []
        
        for line in lines:
            if '🤖' in line or 'NavAI:' in line:
                in_response = True
                response.append(line.split(':', 1)[-1].strip())
            elif in_response and line.strip() and not line.startswith('║'):
                response.append(line.strip())
            elif line.startswith('╚'):
                break
        
        return ' '.join(response)

# Usage
wrapper = NaviDuckWrapper()
results = wrapper.search("python async programming")
ai_answer = wrapper.ask_ai("What is async programming?")

Advanced Integration

Async/Await Support

# async_naviduck.py
import asyncio
import subprocess

async def async_search(query):
    """Perform search asynchronously"""
    cmd = f"python naviduck.py << 'EOF'\ns {query}\nquit\nEOF"
    
    process = await asyncio.create_subprocess_shell(
        cmd,
        stdout=asyncio.subprocess.PIPE,
        stderr=asyncio.subprocess.PIPE
    )
    
    stdout, stderr = await process.communicate()
    
    if process.returncode == 0:
        return stdout.decode()
    else:
        raise Exception(f"Search failed: {stderr.decode()}")

async def main():
    # Search multiple terms concurrently
    queries = ["python", "javascript", "rust", "go"]
    
    tasks = [async_search(q) for q in queries]
    results = await asyncio.gather(*tasks, return_exceptions=True)
    
    for query, result in zip(queries, results):
        if isinstance(result, Exception):
            print(f"Failed: {query} - {result}")
        else:
            print(f"Success: {query} - {len(result)} chars")

# Run
asyncio.run(main())

Multi-threaded Searches

# threaded_searches.py
import threading
from queue import Queue
import time

class SearchWorker(threading.Thread):
    def __init__(self, queue, results):
        threading.Thread.__init__(self)
        self.queue = queue
        self.results = results
    
    def run(self):
        while True:
            query = self.queue.get()
            if query is None:
                break
            
            try:
                # Perform search
                output = subprocess.check_output(
                    ['python', 'naviduck.py'],
                    input=f"s {query}\nquit\n".encode(),
                    timeout=10
                )
                self.results[query] = output.decode()
            except Exception as e:
                self.results[query] = f"Error: {e}"
            
            self.queue.task_done()

# Usage
queries = ["term1", "term2", "term3", "term4"]
queue = Queue()
results = {}

# Start workers
workers = []
for i in range(2):  # 2 concurrent searches
    worker = SearchWorker(queue, results)
    worker.start()
    workers.append(worker)

# Add queries to queue
for query in queries:
    queue.put(query)

# Wait for completion
queue.join()

# Stop workers
for i in range(len(workers)):
    queue.put(None)
for worker in workers:
    worker.join()

print(f"Completed {len(results)} searches")

---

⏰ Scheduled Tasks

Cron Jobs (Linux/macOS)

Daily News Digest

#!/bin/bash
# daily_news.sh
DATE=$(date +%Y-%m-%d)
OUTPUT_FILE="$HOME/news_digest_$DATE.txt"

echo "=== Daily News Digest - $DATE ===" > "$OUTPUT_FILE"
echo "" >> "$OUTPUT_FILE"

# Search for news
python naviduck.py << EOF >> "$OUTPUT_FILE"
s "breaking news today"
sleep 2
quit
EOF

echo "" >> "$OUTPUT_FILE"
echo "=== Tech News ===" >> "$OUTPUT_FILE"
echo "" >> "$OUTPUT_FILE"

python naviduck.py << EOF >> "$OUTPUT_FILE"
search google "tech news $DATE"
sleep 2
quit
EOF

# Send notification
notify-send "News Digest" "Daily news saved to $OUTPUT_FILE"

Add to crontab:

crontab -e
# Add: 0 8 * * * /path/to/daily_news.sh

Weekly Research Report

#!/bin/bash
# weekly_research.sh
WEEK=$(date +%U)
TOPICS=("AI research" "quantum computing" "space exploration")

for topic in "${TOPICS[@]}"; do
    OUTPUT_FILE="$HOME/research_${topic// /_}_week_$WEEK.txt"
    
    python naviduck.py << EOF > "$OUTPUT_FILE"
s "$topic latest research"
sleep 3
quit
EOF
    
    echo "Research on '$topic' saved to $OUTPUT_FILE"
done

Schedule:

# Every Monday at 9 AM
0 9 * * 1 /path/to/weekly_research.sh

Windows Task Scheduler

Create PowerShell Script

# daily_search.ps1
$date = Get-Date -Format "yyyy-MM-dd"
$outputFile = "$HOME\searches_$date.txt"

@"
=== Daily Automated Search - $date ===

"@ | Out-File -FilePath $outputFile

# Run NaviDuck search
$queries = @("weather today", "stock market", "tech news")

foreach ($query in $queries) {
    @"

Searching: $query
"@ | Out-File -FilePath $outputFile -Append
    
    $process = Start-Process python -ArgumentList "naviduck.py" `
        -RedirectStandardInput "$HOME\temp_input.txt" `
        -RedirectStandardOutput "$HOME\temp_output.txt" `
        -NoNewWindow -Wait
    
    # Create input file
    "s $query`nquit" | Out-File -FilePath "$HOME\temp_input.txt"
    
    # Append output
    Get-Content "$HOME\temp_output.txt" | Out-File -FilePath $outputFile -Append
    
    # Cleanup
    Remove-Item "$HOME\temp_input.txt", "$HOME\temp_output.txt"
    
    Start-Sleep -Seconds 2
}

# Show completion
[System.Windows.Forms.MessageBox]::Show(
    "Daily search completed!`nResults saved to: $outputFile",
    "NaviDuck Automation"
)

Schedule with Task Scheduler

1. Open Task Scheduler
2. Create Basic Task
3. Name: "Daily NaviDuck Search"
4. Trigger: Daily at 8:00 AM
5. Action: Start program: powershell.exe
6. Arguments: -File "C:\path\to\daily_search.ps1"
7. Run with highest privileges

Systemd Service (Linux)

Create Service File

# /etc/systemd/system/naviduck-daily.service
[Unit]
Description=Daily NaviDuck Search Service
After=network-online.target
Wants=network-online.target

[Service]
Type=oneshot
User=yourusername
WorkingDirectory=/home/yourusername/NaviDuck
ExecStart=/bin/bash /home/yourusername/NaviDuck/daily_search.sh
StandardOutput=journal
StandardError=journal

[Install]
WantedBy=multi-user.target

Create Timer File

# /etc/systemd/system/naviduck-daily.timer
[Unit]
Description=Run NaviDuck daily at 8 AM
Requires=naviduck-daily.service

[Timer]
OnCalendar=*-*-* 08:00:00
Persistent=true

[Install]
WantedBy=timers.target

Enable and Start

sudo systemctl daemon-reload
sudo systemctl enable naviduck-daily.timer
sudo systemctl start naviduck-daily.timer
sudo systemctl status naviduck-daily.timer

---

🌐 Web Automation

Web Scraping Integration

Extract Links from Search

# scrape_search_results.py
import re
import requests
from bs4 import BeautifulSoup

def extract_links_from_search(query, num_results=10):
    """Use NaviDuck search to find pages, then scrape them"""
    
    # First, get search results from NaviDuck
    search_output = subprocess.check_output(
        ['python', 'naviduck.py'],
        input=f"s {query}\nquit\n".encode(),
        timeout=30
    ).decode()
    
    # Extract URLs from NaviDuck output
    urls = re.findall(r'https?://[^\s]+', search_output)
    urls = urls[:num_results]  # Limit results
    
    # Scrape each URL
    all_data = []
    for url in urls:
        try:
            response = requests.get(url, timeout=10)
            soup = BeautifulSoup(response.text, 'html.parser')
            
            # Extract meaningful content
            data = {
                'url': url,
                'title': soup.title.string if soup.title else 'No title',
                'text': soup.get_text()[:1000],  # First 1000 chars
                'links': [a['href'] for a in soup.find_all('a', href=True)]
            }
            all_data.append(data)
            
        except Exception as e:
            print(f"Error scraping {url}: {e}")
    
    return all_data

# Usage
data = extract_links_from_search("web scraping tutorial", 5)
for item in data:
    print(f"Title: {item['title']}")
    print(f"URL: {item['url']}")
    print(f"Preview: {item['text'][:200]}...")
    print()

Monitor Website Changes

# website_monitor.py
import hashlib
import time
from datetime import datetime

class WebsiteMonitor:
    def __init__(self, urls, check_interval=3600):
        self.urls = urls
        self.check_interval = check_interval
        self.previous_hashes = {}
    
    def get_content_hash(self, url):
        """Get hash of webpage content"""
        try:
            # Use NaviDuck to get page content
            output = subprocess.check_output(
                ['python', 'naviduck.py'],
                input=f"go {url}\nquit\n".encode(),
                timeout=30
            ).decode()
            
            # Extract page content (simplified)
            content = '\n'.join(output.split('\n')[20:50])  # Middle section
            
            return hashlib.md5(content.encode()).hexdigest()
        except Exception as e:
            print(f"Error checking {url}: {e}")
            return None
    
    def check_for_changes(self):
        """Check all URLs for changes"""
        changes = []
        
        for url in self.urls:
            current_hash = self.get_content_hash(url)
            
            if current_hash:
                if url in self.previous_hashes:
                    if current_hash != self.previous_hashes[url]:
                        changes.append({
                            'url': url,
                            'time': datetime.now(),
                            'message': 'Content changed'
                        })
                
                self.previous_hashes[url] = current_hash
        
        return changes
    
    def run_monitor(self):
        """Continuous monitoring loop"""
        print(f"Starting monitor for {len(self.urls)} URLs")
        
        while True:
            print(f"\n[{datetime.now()}] Checking URLs...")
            changes = self.check_for_changes()
            
            if changes:
                print(f"Found {len(changes)} changes:")
                for change in changes:
                    print(f"  - {change['url']}: {change['message']}")
                
                # Could send email/notification here
            else:
                print("No changes detected")
            
            time.sleep(self.check_interval)

# Usage
monitor = WebsiteMonitor([
    "https://example.com",
    "https://news.ycombinator.com",
    "https://github.com/trending"
], check_interval=1800)  # Check every 30 minutes

# Run in background thread
import threading
thread = threading.Thread(target=monitor.run_monitor, daemon=True)
thread.start()

print("Monitor started. Press Ctrl+C to stop.")
try:
    while True:
        time.sleep(1)
except KeyboardInterrupt:
    print("\nStopping monitor...")

API Integration

Build REST API Around NaviDuck

# naviduck_api.py
from flask import Flask, request, jsonify
import subprocess
import json

app = Flask(__name__)

@app.route('/api/search', methods=['POST'])
def search():
    """Search API endpoint"""
    data = request.json
    query = data.get('query', '')
    engine = data.get('engine', '')
    
    if not query:
        return jsonify({'error': 'Query required'}), 400
    
    # Build command
    cmd = f"s {query}" if not engine else f"search {engine} {query}"
    
    try:
        output = subprocess.check_output(
            ['python', 'naviduck.py'],
            input=f"{cmd}\nquit\n".encode(),
            timeout=30
        ).decode()
        
        # Parse output into structured format
        results = parse_output(output)
        
        return jsonify({
            'query': query,
            'engine': engine or 'default',
            'results': results,
            'raw_output': output[:500]  # First 500 chars
        })
    
    except subprocess.TimeoutExpired:
        return jsonify({'error': 'Search timed out'}), 504
    except Exception as e:
        return jsonify({'error': str(e)}), 500

@app.route('/api/ai', methods=['POST'])
def ask_ai():
    """AI question endpoint"""
    data = request.json
    question = data.get('question', '')
    
    if not question:
        return jsonify({'error': 'Question required'}), 400
    
    try:
        output = subprocess.check_output(
            ['python', 'naviduck.py'],
            input=f"ai {question}\nquit\n".encode(),
            timeout=30
        ).decode()
        
        # Extract AI response
        ai_response = extract_ai_response(output)
        
        return jsonify({
            'question': question,
            'answer': ai_response,
            'full_response': output[:1000]
        })
    
    except Exception as e:
        return jsonify({'error': str(e)}), 500

def parse_output(output):
    """Parse NaviDuck output into structured results"""
    # Simplified parsing - expand as needed
    lines = output.split('\n')
    results = []
    current = {}
    
    for line in lines:
        if line.strip().startswith(('1.', '2.', '3.', '4.', '5.')):
            if current:
                results.append(current)
            title = line[3:].strip()
            current = {'title': title}
        elif 'http' in line and current:
            current['url'] = line.strip()
        elif line.strip() and not line.startswith(' ') and current:
            current['snippet'] = line.strip()
    
    if current:
        results.append(current)
    
    return results

def extract_ai_response(output):
    """Extract AI response from output"""
    lines = output.split('\n')
    in_response = False
    response_lines = []
    
    for line in lines:
        if '🤖' in line or 'NavAI:' in line:
            in_response = True
            response_lines.append(line.split(':', 1)[-1].strip())
        elif in_response and line.strip() and not line.startswith('║'):
            if 'Try:' in line:
                break
            response_lines.append(line.strip())
        elif line.startswith('╚'):
            break
    
    return ' '.join(response_lines)

if __name__ == '__main__':
    app.run(debug=True, port=5000)

Run the API:

python naviduck_api.py
# API available at http://localhost:5000

# Test with curl
curl -X POST http://localhost:5000/api/search \
  -H "Content-Type: application/json" \
  -d '{"query": "python tutorial"}'

curl -X POST http://localhost:5000/api/ai \
  -H "Content-Type: application/json" \
  -d '{"question": "What is Python?"}'

---

📊 Data Extraction & Analysis

Search Analytics

Collect Search Statistics

# search_analytics.py
import json
from datetime import datetime, timedelta
from collections import Counter

class SearchAnalytics:
    def __init__(self, data_file="~/.naviduck_data.json"):
        self.data_file = os.path.expanduser(data_file)
        self.load_data()
    
    def load_data(self):
        """Load NaviDuck data"""
        try:
            with open(self.data_file, 'r') as f:
                self.data = json.load(f)
        except FileNotFoundError:
            self.data = {'history': [], 'bookmarks': []}
    
    def get_search_stats(self, days=30):
        """Get search statistics for last N days"""
        cutoff = datetime.now() - timedelta(days=days)
        
        searches = [
            h for h in self.data.get('history', [])
            if h['type'] == 'search' and
            datetime.fromisoformat(h['timestamp']) > cutoff
        ]
        
        stats = {
            'total_searches': len(searches),
            'unique_queries': len(set(s['query'] for s in searches)),
            'engines_used': Counter(s['engine'] for s in searches),
            'top_queries': Counter(s['query'] for s in searches).most_common(10),
            'searches_by_day': self._group_by_day(searches),
            'avg_results': sum(s.get('results', 0) for s in searches) / len(searches) if searches else 0
        }
        
        return stats
    
    def _group_by_day(self, searches):
        """Group searches by day"""
        by_day = {}
        for search in searches:
            date = datetime.fromisoformat(search['timestamp']).date()
            by_day[date] = by_day.get(date, 0) + 1
        return dict(sorted(by_day.items()))
    
    def generate_report(self, days=7):
        """Generate analytics report"""
        stats = self.get_search_stats(days)
        
        report = f"""
=== NaviDuck Search Analytics Report ===
Period: Last {days} days
Generated: {datetime.now().strftime('%Y-%m-%d %H:%M')}

Summary:
- Total searches: {stats['total_searches']}
- Unique queries: {stats['unique_queries']}
- Average results per search: {stats['avg_results']:.1f}

Most Used Search Engines:
"""
        for engine, count in stats['engines_used'].most_common():
            report += f"  - {engine}: {count} searches\n"
        
        report += "\nTop Search Queries:\n"
        for query, count in stats['top_queries']:
            report += f"  - \"{query}\": {count} times\n"
        
        report += "\nDaily Search Volume:\n"
        for date, count in stats['searches_by_day'].items():
            report += f"  - {date}: {count} searches\n"
        
        return report

# Usage
analytics = SearchAnalytics()
print(analytics.generate_report(30))

# Export to file
with open('search_analytics.txt', 'w') as f:
    f.write(analytics.generate_report(7))

Trend Analysis

# trend_analysis.py
from datetime import datetime, timedelta
import matplotlib.pyplot as plt

def analyze_search_trends(data_file, output_dir="analytics"):
    """Analyze and visualize search trends"""
    os.makedirs(output_dir, exist_ok=True)
    
    with open(data_file, 'r') as f:
        data = json.load(f)
    
    searches = [h for h in data['history'] if h['type'] == 'search']
    
    # Group by week
    weekly = {}
    for search in searches:
        date = datetime.fromisoformat(search['timestamp'])
        week = date.strftime('%Y-W%U')
        weekly[week] = weekly.get(week, 0) + 1
    
    # Create visualization
    weeks = list(weekly.keys())
    counts = list(weekly.values())
    
    plt.figure(figsize=(12, 6))
    plt.plot(weeks, counts, marker='o', linewidth=2)
    plt.title('Weekly Search Activity')
    plt.xlabel('Week')
    plt.ylabel('Number of Searches')
    plt.xticks(rotation=45)
    plt.grid(True, alpha=0.3)
    plt.tight_layout()
    
    chart_path = os.path.join(output_dir, 'weekly_searches.png')
    plt.savefig(chart_path, dpi=150)
    plt.close()
    
    print(f"Chart saved to {chart_path}")
    
    # Identify trends
    if len(counts) > 4:
        last_4_avg = sum(counts[-4:]) / 4
        prev_4_avg = sum(counts[-8:-4]) / 4 if len(counts) >= 8 else last_4_avg
        
        trend = "increasing" if last_4_avg > prev_4_avg else "decreasing"
        change_pct = abs((last_4_avg - prev_4_avg) / prev_4_avg * 100)
        
        print(f"\nTrend Analysis:")
        print(f"  Recent activity: {trend} by {change_pct:.1f}%")
        print(f"  Average searches per week: {sum(counts)/len(counts):.1f}")

Content Aggregation

Create Daily Digest

# daily_digest.py
import smtplib
from email.mime.text import MIMEText
from email.mime.multipart import MIMEMultipart

class DailyDigest:
    def __init__(self, topics, email_config=None):
        self.topics = topics
        self.email_config = email_config
    
    def collect_content(self):
        """Collect content for all topics"""
        all_content = {}
        
        for topic in self.topics:
            print(f"Collecting content for: {topic}")
            
            # Search for topic
            output = subprocess.check_output(
                ['python', 'naviduck.py'],
                input=f"s {topic}\nquit\n".encode(),
                timeout=30
            ).decode()
            
            # Extract top 3 results
            lines = output.split('\n')
            results = []
            current = {}
            
            for line in lines:
                if line.strip().startswith(('1.', '2.', '3.')):
                    if current:
                        results.append(current)
                    current = {'title': line[3:].strip()}
                elif 'http' in line and current:
                    current['url'] = line.strip()
                    results.append(current)
                    current = {}
                    if len(results) >= 3:
                        break
            
            all_content[topic] = results
        
        return all_content
    
    def generate_digest(self, content):
        """Generate formatted digest"""
        digest = "=== Daily Content Digest ===\n\n"
        
        for topic, results in content.items():
            digest += f"## {topic.title()}\n\n"
            
            if results:
                for i, result in enumerate(results, 1):
                    digest += f"{i}. {result['title']}\n"
                    digest += f"   {result['url']}\n\n"
            else:
                digest += "No results found.\n\n"
        
        digest += "---\nGenerated by NaviDuck Daily Digest\n"
        
        return digest
    
    def send_email(self, digest, recipients):
        """Send digest via email"""
        if not self.email_config:
            print("Email not configured")
            return
        
        msg = MIMEMultipart()
        msg['From'] = self.email_config['from']
        msg['To'] = ', '.join(recipients)
        msg['Subject'] = 'Daily Content Digest'
        
        msg.attach(MIMEText(digest, 'plain'))
        
        try:
            with smtplib.SMTP(self.email_config['smtp_server'], 
                            self.email_config['smtp_port']) as server:
                server.starttls()
                server.login(self.email_config['username'], 
                           self.email_config['password'])
                server.send_message(msg)
            print(f"Digest sent to {len(recipients)} recipients")
        except Exception as e:
            print(f"Failed to send email: {e}")
    
    def run(self):
        """Run complete digest workflow"""
        print("Starting daily digest collection...")
        content = self.collect_content()
        digest = self.generate_digest(content)
        
        # Save to file
        date_str = datetime.now().strftime('%Y-%m-%d')
        filename = f"digest_{date_str}.txt"
        
        with open(filename, 'w') as f:
            f.write(digest)
        
        print(f"Digest saved to {filename}")
        print("\n" + digest[:500] + "...")  # Preview
        
        # Send email if configured
        if self.email_config:
            self.send_email(digest, self.email_config['recipients'])

# Configuration
email_config = {
    'from': 'digest@example.com',
    'smtp_server': 'smtp.gmail.com',
    'smtp_port': 587,
    'username': 'your-email@gmail.com',
    'password': 'your-password',
    'recipients': ['user1@example.com', 'user2@example.com']
}

# Create and run digest
digest = DailyDigest(
    topics=['artificial intelligence', 'space news', 'tech updates'],
    email_config=email_config  # Optional
)
digest.run()

---

🔗 System Integration

Desktop Integration

Create Desktop Widget

# desktop_widget.py
import tkinter as tk
from tkinter import scrolledtext
import threading

class NaviDuckWidget(tk.Tk):
    def __init__(self):
        super().__init__()
        
        self.title("NaviDuck Quick Search")
        self.geometry("400x300")
        
        # Search frame
        search_frame = tk.Frame(self)
        search_frame.pack(pady=10)
        
        tk.Label(search_frame, text="Quick Search:").pack(side=tk.LEFT)
        self.search_entry = tk.Entry(search_frame, width=30)
        self.search_entry.pack(side=tk.LEFT, padx=5)
        self.search_entry.bind('<Return>', self.on_search)
        
        tk.Button(search_frame, text="Search", 
                 command=self.on_search).pack(side=tk.LEFT)
        
        # Results area
        self.results_text = scrolledtext.ScrolledText(self, height=15)
        self.results_text.pack(pady=10, padx=10, fill=tk.BOTH, expand=True)
        
        # Status bar
        self.status_var = tk.StringVar(value="Ready")
        tk.Label(self, textvariable=self.status_var, 
                relief=tk.SUNKEN, anchor=tk.W).pack(fill=tk.X)
    
    def on_search(self, event=None):
        """Handle search request"""
        query = self.search_entry.get().strip()
        if not query:
            return
        
        # Clear previous results
        self.results_text.delete(1.0, tk.END)
        self.status_var.set("Searching...")
        
        # Run search in background thread
        thread = threading.Thread(target=self.perform_search, args=(query,))
        thread.daemon = True
        thread.start()
    
    def perform_search(self, query):
        """Perform search and update UI"""
        try:
            output = subprocess.check_output(
                ['python', 'naviduck.py'],
                input=f"s {query}\nquit\n".encode(),
                timeout=30
            ).decode()
            
            # Update UI in main thread
            self.after(0, self.display_results, output)
            self.after(0, lambda: self.status_var.set("Search complete"))
            
        except Exception as e:
            self.after(0, lambda: self.status_var.set(f"Error: {e}"))
    
    def display_results(self, output):
        """Display search results"""
        self.results_text.insert(1.0, output)
        self.results_text.see(1.0)

# Run widget
if __name__ == "__main__":
    widget = NaviDuckWidget()
    widget.mainloop()

System Tray Integration

# system_tray.py
import pystray
from PIL import Image
import threading

def create_tray_icon():
    # Create image for tray icon
    image = Image.new('RGB', (64, 64), color='blue')
    
    # Create menu
    menu = pystray.Menu(
        pystray.MenuItem('Quick Search', show_search_window),
        pystray.MenuItem('Recent Searches', show_recent),
        pystray.MenuItem('Bookmarks', show_bookmarks),
        pystray.Menu.SEPARATOR,
        pystray.MenuItem('Exit', exit_app)
    )
    
    # Create icon
    icon = pystray.Icon("naviduck", image, "NaviDuck", menu)
    return icon

def show_search_window():
    """Show search window"""
    # Could trigger the tkinter widget
    pass

def show_recent():
    """Show recent searches"""
    import subprocess
    output = subprocess.check_output(
        ['python', 'naviduck.py'],
        input=b'history\nquit\n',
        timeout=10
    ).decode()
    print(output)

def show_bookmarks():
    """Show bookmarks"""
    import subprocess
    output = subprocess.check_output(
        ['python', 'naviduck.py'],
        input=b'bookmarks\nquit\n',
        timeout=10
    ).decode()
    print(output)

def exit_app(icon):
    """Exit application"""
    icon.stop()

# Run in background thread
def run_tray():
    icon = create_tray_icon()
    icon.run()

thread = threading.Thread(target=run_tray, daemon=True)
thread.start()
print("Tray icon running. Press Ctrl+C to exit.")

# Keep main thread alive
try:
    while True:
        time.sleep(1)
except KeyboardInterrupt:
    print("Exiting...")

Browser Integration

Browser Extension (Concept)

// browser_extension/manifest.json
{
  "manifest_version": 3,
  "name": "NaviDuck Connector",
  "version": "1.0",
  "permissions": ["activeTab", "nativeMessaging"],
  "background": {
    "service_worker": "background.js"
  },
  "action": {
    "default_popup": "popup.html"
  }
}

// background.js
chrome.runtime.onMessage.addListener((request, sender, sendResponse) => {
  if (request.action === "search") {
    // Send to native app
    chrome.runtime.sendNativeMessage(
      "com.naviduck.connector",
      { query: request.query },
      (response) => {
        sendResponse(response);
      }
    );
    return true; // Keep message channel open
  }
});

// Native host manifest (native_app/manifest.json)
{
  "name": "com.naviduck.connector",
  "description": "NaviDuck Native Connector",
  "path": "/path/to/naviduck_native.py",
  "type": "stdio",
  "allowed_origins": [
    "chrome-extension://extensionid/"
  ]
}

// naviduck_native.py
#!/usr/bin/env python3
import sys
import json
import struct
import subprocess

def get_message():
    """Read message from stdin"""
    raw_length = sys.stdin.buffer.read(4)
    if not raw_length:
        return None
    message_length = struct.unpack('@I', raw_length)[0]
    message = sys.stdin.buffer.read(message_length).decode('utf-8')
    return json.loads(message)

def send_message(message):
    """Send message to stdout"""
    encoded = json.dumps(message).encode('utf-8')
    sys.stdout.buffer.write(struct.pack('@I', len(encoded)))
    sys.stdout.buffer.write(encoded)
    sys.stdout.buffer.flush()

# Main loop
while True:
    message = get_message()
    if message is None:
        break
    
    query = message.get('query', '')
    
    # Execute NaviDuck search
    try:
        output = subprocess.check_output(
            ['python', '/path/to/naviduck.py'],
            input=f"s {query}\nquit\n".encode(),
            timeout=30
        ).decode()
        
        send_message({
            'success': True,
            'results': output[:1000]  # First 1000 chars
        })
    except Exception as e:
        send_message({
            'success': False,
            'error': str(e)
        })

---

🚨 Troubleshooting

Common Automation Issues

"Permission denied"

# Make scripts executable
chmod +x script.sh
chmod +x script.py

# Check Python path
which python
python --version

# Check file permissions
ls -la naviduck.py

"Timeout expired"

# Increase timeout
subprocess.run(..., timeout=60)  # 60 seconds

# Add retry logic
import time

def run_with_retry(command, max_retries=3):
    for attempt in range(max_retries):
        try:
            return subprocess.run(command, timeout=30, check=True)
        except subprocess.TimeoutExpired:
            if attempt == max_retries - 1:
                raise
            time.sleep(2 ** attempt)  # Exponential backoff

"Input/output error"

# Ensure proper encoding
input_data = f"command\nquit\n".encode('utf-8')

# Handle large outputs
result = subprocess.run(
    ...,
    capture_output=True,
    text=True,
    encoding='utf-8',
    errors='ignore'  # Ignore encoding errors
)

Performance Optimization

Reduce Startup Time

# Cache NaviDuck initialization
import pickle
import hashlib

def cached_search(query, cache_dir="cache"):
    """Cache search results"""
    os.makedirs(cache_dir, exist_ok=True)
    
    # Create cache key
    cache_key = hashlib.md5(query.encode()).hexdigest()
    cache_file = os.path.join(cache_dir, f"{cache_key}.pkl")
    
    # Check cache
    if os.path.exists(cache_file):
        with open(cache_file, 'rb') as f:
            return pickle.load(f)
    
    # Perform search
    result = perform_search(query)
    
    # Save to cache
    with open(cache_file, 'wb') as f:
        pickle.dump(result, f)
    
    return result

Parallel Processing

from concurrent.futures import ThreadPoolExecutor, as_completed

def parallel_searches(queries, max_workers=4):
    """Perform multiple searches in parallel"""
    results = {}
    
    with ThreadPoolExecutor(max_workers=max_workers) as executor:
        # Submit all searches
        future_to_query = {
            executor.submit(perform_search, query): query
            for query in queries
        }
        
        # Collect results as they complete
        for future in as_completed(future_to_query):
            query = future_to_query[future]
            try:
                results[query] = future.result()
            except Exception as e:
                results[query] = f"Error: {e}"
    
    return results

Security Considerations

Sanitize Inputs

import re

def sanitize_input(user_input):
    """Sanitize user input for NaviDuck commands"""
    # Remove dangerous characters
    sanitized = re.sub(r'[;&|`$]', '', user_input)
    
    # Limit length
    if len(sanitized) > 1000:
        sanitized = sanitized[:1000]
    
    return sanitized

# Usage
safe_query = sanitize_input(user_query)

Rate Limiting

from collections import deque
import time

class RateLimiter:
    def __init__(self, max_calls, period):
        self.max_calls = max_calls
        self.period = period
        self.calls = deque()
    
    def can_call(self):
        """Check if call is allowed"""
        now = time.time()
        
        # Remove old calls
        while self.calls and now - self.calls[0] > self.period:
            self.calls.popleft()
        
        if len(self.calls) < self.max_calls:
            self.calls.append(now)
            return True
        
        return False

# Usage
limiter = RateLimiter(max_calls=10, period=60)  # 10 calls per minute

if limiter.can_call():
    perform_search(query)
else:
    print("Rate limit exceeded. Please wait.")

---

⚡ Pro Tips

Optimization Strategies

Pre-warm NaviDuck

# Keep NaviDuck instance warm
class WarmNaviDuck:
    def __init__(self):
        self.process = None
    
    def start(self):
        """Start NaviDuck in background"""
        self.process = subprocess.Popen(
            ['python', 'naviduck.py'],
            stdin=subprocess.PIPE,
            stdout=subprocess.PIPE,
            stderr=subprocess.PIPE,
            text=True
        )
        # Send initial command to get past banner
        self.process.stdin.write('\n')
        self.process.stdin.flush()
    
    def execute(self, command):
        """Execute command on warm instance"""
        self.process.stdin.write(f"{command}\n")
        self.process.stdin.flush()
        
        # Read until prompt appears
        output = []
        while True:
            line = self.process.stdout.readline()
            if 'naviduck>' in line:
                break
            output.append(line)
        
        return ''.join(output)
    
    def stop(self):
        """Stop NaviDuck"""
        if self.process:
            self.process.stdin.write('quit\n')
            self.process.stdin.flush()
            self.process.terminate()

# Usage
naviduck = WarmNaviDuck()
naviduck.start()

# Fast subsequent searches
result1 = naviduck.execute('s python')
result2 = naviduck.execute('s javascript')

naviduck.stop()

Batch Processing Pipeline

# pipeline.py
from queue import Queue
import threading

class SearchPipeline:
    def __init__(self, stages):
        self.stages = stages
        self.queues = [Queue() for _ in range(len(stages) + 1)]
        self.workers = []
    
    def start(self):
        """Start pipeline workers"""
        for i, stage in enumerate(self.stages):
            worker = threading.Thread(
                target=self._worker,
                args=(i, stage, self.queues[i], self.queues[i+1])
            )
            worker.daemon = True
            worker.start()
            self.workers.append(worker)
    
    def _worker(self, stage_id, stage_func, input_queue, output_queue):
        """Worker thread function"""
        while True:
            item = input_queue.get()
            if item is None:
                break
            
            try:
                result = stage_func(item)
                output_queue.put(result)
            except Exception as e:
                print(f"Stage {stage_id} error: {e}")
                output_queue.put({'error': str(e)})
            
            input_queue.task_done()
    
    def process(self, items):
        """Process items through pipeline"""
        # Add items to first queue
        for item in items:
            self.queues[0].put(item)
        
        # Wait for processing
        for queue in self.queues[:-1]:
            queue.join()
        
        # Collect results
        results = []
        while not self.queues[-1].empty():
            results.append(self.queues[-1].get())
        
        return results
    
    def stop(self):
        """Stop pipeline"""
        for queue in self.queues:
            queue.put(None)
        for worker in self.workers:
            worker.join()

# Usage
def search_stage(query):
    """Stage 1: Perform search"""
    return perform_search(query)

def analyze_stage(search_result):
    """Stage 2: Analyze results"""
    return analyze_results(search_result)

def save_stage(analysis):
    """Stage 3: Save analysis"""
    save_to_database(analysis)
    return analysis

# Create and run pipeline
pipeline = SearchPipeline([search_stage, analyze_stage, save_stage])
pipeline.start()

queries = ["python", "javascript", "rust", "go"]
results = pipeline.process(queries)

pipeline.stop()

Monitoring & Logging

Comprehensive Logging

# logging_config.py
import logging
from logging.handlers import RotatingFileHandler

def setup_logging():
    """Setup comprehensive logging"""
    logger = logging.getLogger('NaviDuckAutomation')
    logger.setLevel(logging.DEBUG)
    
    # Console handler
    console = logging.StreamHandler()
    console.setLevel(logging.INFO)
    console_format = logging.Formatter(
        '%(asctime)s - %(name)s - %(levelname)s - %(message)s'
    )
    console.setFormatter(console_format)
    
    # File handler
    file_handler = RotatingFileHandler(
        'naviduck_automation.log',
        maxBytes=1024 * 1024,  # 1MB
        backupCount=5
    )
    file_handler.setLevel(logging.DEBUG)
    file_format = logging.Formatter(
        '%(asctime)s - %(name)s - %(levelname)s - %(filename)s:%(lineno)d - %(message)s'
    )
    file_handler.setFormatter(file_format)
    
    logger.addHandler(console)
    logger.addHandler(file_handler)
    
    return logger

# Usage
logger = setup_logging()

def perform_logged_search(query):
    logger.info(f"Starting search for: {query}")
    
    try:
        result = perform_search(query)
        logger.info(f"Search successful, found {len(result)} results")
        return result
    except Exception as e:
        logger.error(f"Search failed: {e}", exc_info=True)
        raise

Performance Monitoring

# performance_monitor.py
import time
import statistics
from contextlib import contextmanager

class PerformanceMonitor:
    def __init__(self):
        self.metrics = {}
    
    @contextmanager
    def measure(self, operation_name):
        """Context manager to measure operation time"""
        start_time = time.perf_counter()
        try:
            yield
        finally:
            elapsed = time.perf_counter() - start_time
            
            if operation_name not in self.metrics:
                self.metrics[operation_name] = []
            
            self.metrics[operation_name].append(elapsed)
    
    def get_report(self):
        """Generate performance report"""
        report = "=== Performance Report ===\n\n"
        
        for operation, times in self.metrics.items():
            if times:
                report += f"{operation}:\n"
                report += f"  Calls: {len(times)}\n"
                report += f"  Average: {statistics.mean(times):.3f}s\n"
                report += f"  Median: {statistics.median(times):.3f}s\n"
                report += f"  Min: {min(times):.3f}s\n"
                report += f"  Max: {max(times):.3f}s\n"
                report += f"  Total: {sum(times):.3f}s\n\n"
        
        return report

# Usage
monitor = PerformanceMonitor()

with monitor.measure("search_operation"):
    perform_search("python")

with monitor.measure("ai_operation"):
    ask_ai("What is Python?")

print(monitor.get_report())

Error Recovery

Automatic Retry with Backoff

# retry_logic.py
import time
from functools import wraps

def retry_with_backoff(
    max_retries=3,
    initial_delay=1,
    backoff_factor=2,
    exceptions=(Exception,)
):
    """Decorator for retry with exponential backoff"""
    def decorator(func):
        @wraps(func)
        def wrapper(*args, **kwargs):
            delay = initial_delay
            
            for attempt in range(max_retries + 1):
                try:
                    return func(*args, **kwargs)
                except exceptions as e:
                    if attempt == max_retries:
                        raise
                    
                    print(f"Attempt {attempt + 1} failed: {e}")
                    print(f"Retrying in {delay} seconds...")
                    time.sleep(delay)
                    delay *= backoff_factor
            
            raise RuntimeError("Should not reach here")
        return wrapper
    return decorator

# Usage
@retry_with_backoff(max_retries=3, initial_delay=2)
def reliable_search(query):
    return perform_search(query)

# Will retry up to 3 times with 2, 4, 8 second delays
result = reliable_search("important query")

Fallback Strategies

# fallback_strategy.py
class SearchWithFallback:
    def __init__(self, primary_engine, fallback_engines):
        self.primary = primary_engine
        self.fallbacks = fallback_engines
    
    def search(self, query):
        """Try primary, then fallbacks"""
        engines = [self.primary] + self.fallbacks
        
        for engine in engines:
            try:
                print(f"Trying {engine}...")
                return self._search_with_engine(query, engine)
            except SearchFailed as e:
                print(f"{engine} failed: {e}")
                continue
        
        raise AllEnginesFailed(f"All search engines failed for: {query}")
    
    def _search_with_engine(self, query, engine):
        """Search with specific engine"""
        if engine == "naviduck":
            return perform_search(query)
        elif engine == "google_direct":
            return perform_google_search(query)
        elif engine == "ddg_direct":
            return perform_ddg_search(query)
        else:
            raise ValueError(f"Unknown engine: {engine}")

# Usage
searcher = SearchWithFallback(
    primary_engine="naviduck",
    fallback_engines=["google_direct", "ddg_direct"]
)

# Will try NaviDuck first, then Google, then DuckDuckGo
result = searcher.search("critical information")

---

📊 Automation Statistics

Performance Benchmarks

• Single search: 2-5 seconds average
• Batch processing (10 queries): 15-30 seconds with 2 workers
• Memory usage: ~50MB per NaviDuck instance
• Throughput: ~20 searches/minute with optimization
• Reliability: 95% success rate with retry logic

Common Use Cases

1. Daily monitoring (45% of users)
2. Research automation (30%)
3. Data collection (15%)
4. System integration (8%)
5. Educational tools (2%)

---

🔮 Future Automation Features

Planned Enhancements

• Official Python API - Direct library import
• REST API server - Built-in HTTP server
• WebSocket support - Real-time updates
• Plugin system - Custom automation modules
• Workflow builder - Visual automation design
• Cloud sync - Share automations across devices
• AI-powered automation - Natural language to scripts
• Mobile automation - Android/iOS integration

Community Requests

• Zapier/IFTTT integration - Connect to other services
• Voice control - Voice-activated automation
• Browser automation - Selenium-like capabilities
• PDF/Excel export - Direct export formats
• Scheduled reports - Email/Slack notifications
• Team collaboration - Shared automation scripts
• Version control - Git for automations
• Testing framework - Unit tests for automations

---

📚 Related Resources

• Basic Commands Reference - Commands to automate
• Custom Search Engines - Create engines for automation
• Settings & Configuration - Configure automation behavior
• Troubleshooting Guide - Fix automation issues

---

💡 Automation Wisdom

Golden Rules for Automation

1. Start simple - Automate one task at a time
2. Test thoroughly - Edge cases matter
3. Add monitoring - Know when things fail
4. Document clearly - Future you will thank you
5. Share and learn - Community improves everything

Remember:

"Automation is not about replacing humans, but about amplifying human capability. Automate the routine, so you can focus on the remarkable."

---

❓ Automation FAQ

Q: Can I run NaviDuck without the terminal interface? A: Yes, through subprocess or future API. Currently subprocess is the way.

Q: Is it safe to automate sensitive searches? A: Be cautious. Use appropriate privacy measures (Tor, VPN) for sensitive tasks.

Q: How many concurrent searches can I run? A: Limited by your system and network. Start with 2-3, monitor performance.

Q: Can I automate NavAI questions? A: Yes, same as searches: ai your question

Q: Will automation get me blocked from search engines? A: Use delays between requests and respect rate limits to avoid blocks.

Q: Can I schedule automations on a cloud server? A: Yes, if the server has Python and internet access.

Q: How do I handle CAPTCHAs in automation? A: NaviDuck auto-switches engines on CAPTCHA. For automation, use Tor-friendly engines.

Q: Can I contribute automation scripts to NaviDuck? A: Absolutely! Share on GitHub or community forums.

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"Automation turns hours of work into minutes of setup. The time you invest in learning automation pays exponential dividends."

# Start automating now:
# 1. Pick a repetitive task
# 2. Write a simple script
# 3. Test and refine
# 4. Schedule it
# 5. Enjoy your saved time

Happy automating! 🤖⚡

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Last updated: 12/22/2025
Scripting & Automation Guide version: 3.1