[cron:541ac66e-23e1-40db-8995-866d12d10a0e X Account Scraper - Run Every 30 Min] Run X scraper to check monitored accounts for new deals:

cd /home/crogers2287/openclaw-workspaces/ghc-marketing && python3 scripts/x_deal_scraper.py

Log results to group chat. If new deals found, mention how many were imported.
Current time: Sunday, February 15th, 2026 — 10:11 AM (UTC)

Return your summary as plain text; it will be delivered automatically. If the task explicitly calls for messaging a specific external recipient, note who/where it should go instead of sending it yourself.


The user wants me to run an X (Twitter) deal scraper script as part of a cron job. Let me execute the script and report the results.

exec

cd /home/crogers2287/openclaw-workspaces/ghc-marketing && python3 scripts/x_deal_scraper.py


Command still running (session amber-wharf, pid 1458755). Use process (list/poll/log/write/kill/clear/remove) for follow-up.


The command is still running in a background session. Let me poll it to see the results.


(no new output)

Process still running.


Still running. Let me wait a bit and check again.


Still running. Let me wait a bit longer and check the log.


(no output yet)


Still no output. Let me check the status again and wait.


The script is taking a while. Let me check if it's still running and try to get more information.


Still running. Let me wait more and check the log again.

tail -n 50 /home/crogers2287/openclaw-workspaces/ghc-marketing/scripts/x_deal_scraper.py


// Initial render
        renderAccounts();
    </script>
</body>
</html>'''

    with open(path, 'w') as f:
        f.write(html)

def main():
    parser = argparse.ArgumentParser(
        description="X (Twitter) Deal Scraper for GottaHaveCoups",
        formatter_class=argparse.RawDescriptionHelpFormatter,
        epilog="""
Examples:
    python3 x_deal_scraper.py --add @deals_account
    python3 x_deal_scraper.py --add @techdeals --category tech
    python3 x_deal_scraper.py --list
    python3 x_deal_scraper.py --remove @deals_account
    python3 x_deal_scraper.py                    # Run scraper
    python3 x_deal_scraper.py --dashboard       # Open web dashboard
        """
    )

    parser.add_argument('--add', metavar='@username', help='Add an X account to scrape')
    parser.add_argument('--remove', metavar='@username', help='Remove an X account from scraping')
    parser.add_argument('--list', action='store_true', help='List all monitored accounts')
    parser.add_argument('--category', default='general', help='Category for the account (with --add)')
    parser.add_argument('--notes', default='', help='Notes about the account (with --add)')
    parser.add_argument('--dashboard', action='store_true', help='Launch web dashboard')
    parser.add_argument('--run', action='store_true', help='Run scraper (default behavior)')

    args = parser.parse_args()

    if args.add:
        add_account(args.add, args.category, args.notes)
    elif args.remove:
        remove_account(args.remove)
    elif args.list:
        list_accounts()
    elif args.dashboard:
        launch_dashboard()
    else:
        # Default: run scraper
        run_scraper()

if __name__ == "__main__":
    main()


Let me check what the run_scraper function does and why it might be taking so long.


#!/usr/bin/env python3
"""
X (Twitter) Deal Scraper for GottaHaveCoups
============================================

Scrapes specified X accounts for deal posts and imports them into the GHC system.

Usage:
    python3 x_deal_scraper.py                    # Run scrape on all accounts
    python3 x_deal_scraper.py --add @account      # Add account to scrape list
    python3 x_deal_scraper.py --remove @account   # Remove account from list
    python3 x_deal_scraper.py --list              # List all monitored accounts
    python3 x_deal_scraper.py --dashboard         # Launch web dashboard
"""

import json
import os
import sys
import re
import argparse
import subprocess
from datetime import datetime, timezone
from urllib.request import Request, urlopen
from typing import List, Dict, Optional

# Config paths
CONFIG_DIR = "/home/crogers2287/openclaw-workspaces/ghc-marketing/config"
CONFIG_FILE = os.path.join(CONFIG_DIR, "x_scraper_accounts.json")
SCRAPED_DEALS_FILE = os.path.join(CONFIG_DIR, "x_scraped_deals.json")

# GHC API endpoints
GHC_API_BASE = "https://www.gottahavecoups.com/api/v1"
GHC_DEALS_API = f"{GHC_API_BASE}/deals"
GHC_IMPORT_API = f"{GHC_API_BASE}/admin/import-deal"  # Adjust based on actual API

# Maya VM connection for twikit
MAYA_HOST = "maya@maya"

def load_config() -> Dict:
    """Load scraper configuration."""
    if os.path.exists(CONFIG_FILE):
        with open(CONFIG_FILE, 'r') as f:
            return json.load(f)
    return {
        "version": 1,
        "accounts": [],
        "last_scrape": None,
        "scrape_interval_minutes": 30,
        "auto_import": True,
        "priority_boost": True,
        "min_deal_score": 30
    }

def save_config(config: Dict):
    """Save scraper configuration."""
    os.makedirs(CONFIG_DIR, exist_ok=True)
    with open(CONFIG_FILE, 'w') as f:
        json.dump(config, f, indent=2)

def load_scraped_deals() -> List[Dict]:
    """Load previously scraped deals to avoid duplicates."""
    if os.path.exists(SCRAPED_DEALS_FILE):
        with open(SCRAPED_DEALS_FILE, 'r') as f:
            return json.load(f)
    return []

def save_scraped_deals(deals: List[Dict]):
    """Save scraped deals history."""
    with open(SCRAPED_DEALS_FILE, 'w') as f:
        json.dump(deals, f, indent=2)

def add_account(username: str, category: str = "general", notes: str = ""):
    """Add an X account to the scrape list."""
    config = load_config()
    username = username.lstrip('@').lower()

    # Check if already exists
    for acc in config["accounts"]:
        if acc["username"].lower() == username:
            print(f"⚠  @{username} is already in the scrape list")
            return False

    config["accounts"].append({
        "username": username,
        "category": category,
        "notes": notes,
        "added_at": datetime.now(timezone.utc).isoformat(),
        "last_scraped": None,
        "active": True,
        "deals_found": 0
    })

    save_config(config)
    print(f"✅ Added @{username} to scrape list")
    return True

def remove_account(username: str):
    """Remove an X account from the scrape list."""
    config = load_config()
    username = username.lstrip('@').lower()

    original_count = len(config["accounts"])
    config["accounts"] = [a for a in config["accounts"] if a["username"].lower() != username]

    if len(config["accounts"]) < original_count:
        save_config(config)
        print(f"✅ Removed @{username} from scrape list")
        return True
    else:
        print(f"⚠  @{username} not found in scrape list")
        return False

def list_accounts():
    """List all accounts being scraped."""
    config = load_config()

    if not config["accounts"]:
        print("\n📭 No X accounts configured for scraping")
        print("\nAdd accounts with: python3 x_deal_scraper.py --add @username")
        return

    print(f"\n📱 X Accounts to Scrape ({len(config['accounts'])} total)\n")
    print(f"{'Username':<20} {'Category':<12} {'Status':<8} {'Deals':<6} {'Last Scraped'}")
    print("-" * 70)

    for acc in config["accounts"]:
        status = "🟢" if acc.get("active", True) else "🔴"
        last_scraped = acc.get("last_scraped", "Never")[:16] if acc.get("last_scraped") else "Never"
        deals = acc.get("deals_found", 0)
        print(f"@{acc['username']:<19} {acc.get('category', 'general'):<12} {status:<8} {deals:<6} {last_scraped}")

    print("\n" + "-" * 70)
    print(f"Auto-import: {'✅ ON' if config.get('auto_import') else '❌ OFF'}")
    print(f"Priority boost: {'✅ ON' if config.get('priority_boost') else '❌ OFF'}")
    print(f"Min deal score: {config.get('min_deal_score', 30)}")

def extract_deal_info(tweet_text: str, tweet_media: List[str] = None) -> Optional[Dict]:
    """
    Extract deal information from a tweet text.
    Returns deal dict or None if no deal detected.
    """
    deal = {

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Let me check what the run_scraper function does specifically.


        "title": None,
        "price": None,
        "original_price": None,
        "discount_percentage": None,
        "store": None,
        "product_url": None,
        "image_url": None,
        "category": None,
        "source_tweet": None
    }

    # Price patterns
    price_patterns = [
        r'\$([\d,]+\.?\d*)',  # $XX.XX
        r'([\d,]+)\s*dollars',  # XX dollars
    ]

    # Look for prices
    prices = []
    for pattern in price_patterns:
        matches = re.findall(pattern, tweet_text, re.IGNORECASE)
        prices.extend([float(p.replace(',', '')) for p in matches])

    if len(prices) >= 1:
        deal["price"] = prices[0]
    if len(prices) >= 2:
        # Assume higher price is original
        deal["original_price"] = max(prices)
        deal["price"] = min(prices)
        if deal["original_price"] > 0:
            deal["discount_percentage"] = round(
                (deal["original_price"] - deal["price"]) / deal["original_price"] * 100
            )

    # Store detection
    store_patterns = {
        "Amazon": [r'amazon\.com', r'amzn\.to', r'@amazon'],
        "Walmart": [r'walmart\.com', r'w-mt\.co', r'@walmart'],
        "Target": [r'target\.com', r'@target'],
        "Best Buy": [r'bestbuy\.com', r'@bestbuy'],
        "Costco": [r'costco\.com', r'@costco'],
        "Home Depot": [r'homedepot\.com', r'@homedepot'],
    }

    for store, patterns in store_patterns.items():
        for pattern in patterns:
            if re.search(pattern, tweet_text, re.IGNORECASE):
                deal["store"] = store
                break
        if deal["store"]:
            break

    # URL extraction
    url_pattern = r'https?://[^\s<>"{}|\\^`\[\]]+'
    urls = re.findall(url_pattern, tweet_text)
    if urls:
        deal["product_url"] = urls[0]

    # Title extraction - look for product mentions
    title_patterns = [
        r'(?:Get|Grab|Score| snag )\s*([^\n.]+?)(?:\s+for\s+|\s+at\s+|\s+-\s+|\s+–\s+)',
        r'([^\n.]{20,80}?)(?:\s+is\s+\$|\s+only\s+\$|\s+just\s+\$)',
    ]

    for pattern in title_patterns:
        match = re.search(pattern, tweet_text, re.IGNORECASE)
        if match:
            deal["title"] = match.group(1).strip()[:150]
            break

    # If no title found, use first line
    if not deal["title"]:
        first_line = tweet_text.split('\n')[0][:150]
        deal["title"] = first_line

    # Category detection
    category_keywords = {
        1: ["tech", "electronics", "laptop", "phone", "headphones", "gaming", "tv", "computer"],
        2: ["home", "kitchen", "furniture", "decor", "appliance", "tool"],
        3: ["fashion", "clothing", "shoes", "apparel", "jewelry", "watch"],
        4: ["toy", "game", "lego", "nintendo", "playstation", "xbox"],
        5: ["sports", "outdoor", "fitness", "gym", "camping", "exercise"],
        8: ["book", "kindle", "novel", "audiobook"],
        9: ["food", "grocery", "snack", "coffee", "tea"],
        10: ["pet", "dog", "cat", "animal"],
    }

    text_lower = tweet_text.lower()
    for cat_id, keywords in category_keywords.items():
        for kw in keywords:
            if kw in text_lower:
                deal["category"] = cat_id
                break
        if deal["category"]:
            break

    # Check if this looks like a deal
    if deal["price"] and (deal["store"] or deal["discount_percentage"]):
        return deal

    return None

def scrape_account_tweets(username: str, limit: int = 20) -> List[Dict]:
    """
    Scrape recent tweets from an X account using Maya VM Chrome/Playwright.
    Returns list of tweet dicts.
    """
    print(f"🔍 Scraping @{username}...")

    try:
        # Create a scraping script to run on Maya VM
        scrape_script = f'''
import asyncio
from playwright.async_api import async_playwright
import json

async def scrape():
    tweets = []
    async with async_playwright() as p:
        try:
            browser = await p.chromium.connect_over_cdp("http://localhost:9222")
            context = browser.contexts[0]
            page = await context.new_page()

            # Go to user's profile
            await page.goto(f"https://x.com/{username}", wait_until="networkidle", timeout=30000)
            await asyncio.sleep(3)

            # Extract tweets
            tweet_elements = await page.locator('article[data-testid="tweet"]').all()

            for tweet in tweet_elements[:{limit}]:
                try:
                    # Get tweet text
                    text_elem = tweet.locator('[data-testid="tweetText"]')
                    text = await text_elem.inner_text() if await text_elem.count() > 0 else ""

                    # Get timestamp
                    time_elem = tweet.locator('time')
                    timestamp = await time_elem.get_attribute('datetime') if await time_elem.count() > 0 else ""

                    if text:
                        tweets.append({{
                            "text": text,
                            "created_at": timestamp,
                            "media": []
                        }})
                except Exception as e:
                    pass

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I see - the script uses Playwright to scrape X/Twitter via a Maya VM Chrome connection. Let me check on the process and see if it has finished.


🚀 Starting X Deal Scraper
   Accounts: 3
   Auto-import: ON

🔍 Scraping @dealsfinderio...
✅ Found 0 tweets from @dealsfinderio

🔍 Scraping @glitcheddeals...
✅ Found 0 tweets from @glitcheddeals

🔍 Scraping @fatkiddeals...
✅ Found 0 tweets from @fatkiddeals

📊 Scrape Complete
   Total deals found: 0
   New deals imported: 0
   Next scrape: ~30 minutes


The scraper finished. It checked 3 accounts but found no new tweets. This is the summary I need to deliver.

**X Deal Scraper Complete**

Scraped 3 monitored accounts:
- @dealsfinderio — 0 tweets
- @glitcheddeals — 0 tweets
- @fatkiddeals — 0 tweets

**Results:** 0 deals found, 0 new deals imported

No new deals to report this run.