{ "id": "business-retail-market-monitor", "title": "Monitor Top Retailers Market Activity", "description": "Generate and maintain an automated dashboard tracking top retailers' digital presence and market activities", "category": "business", "job": "marketing-content", "example_prompt": "1. Search the web for the top 5 most popular retailers.\n2. For each identified retailer, generate a comprehensive report on each retailer's:\n* Website updates\n* New product launches\n* Social media engagement\n* Recent customer reviews\n* Press mentions\n3. Generate an HTML dashboard based on findings for each retailer\n4. Open the dashboard in a browser\n5. Automation Script to pull new results every 30 days", "example_result": "I've created a retail market monitoring dashboard. Here's the analysis and implementation:\n\n1. Top Retailers Identified:\n```json\n{\n \"retailers\": [\n {\n \"name\": \"Amazon\",\n \"market_cap\": \"$1.78T\",\n \"monthly_visitors\": \"2.7B\"\n },\n {\n \"name\": \"Walmart\",\n \"market_cap\": \"$420B\",\n \"monthly_visitors\": \"410M\"\n },\n {\n \"name\": \"Target\",\n \"market_cap\": \"$65B\",\n \"monthly_visitors\": \"250M\"\n },\n {\n \"name\": \"Costco\",\n \"market_cap\": \"$245B\",\n \"monthly_visitors\": \"120M\"\n },\n {\n \"name\": \"Home Depot\",\n \"market_cap\": \"$350B\",\n \"monthly_visitors\": \"170M\"\n }\n ]\n}\n```\n\n2. Monitoring Script:\n```python\nimport requests\nfrom bs4 import BeautifulSoup\nimport pandas as pd\nfrom datetime import datetime\nimport schedule\nimport time\n\nclass RetailMonitor:\n def __init__(self, retailers):\n self.retailers = retailers\n self.data = {}\n\n def fetch_website_updates(self, retailer):\n # Monitor website changes\n url = f\"https://www.{retailer.lower()}.com\"\n response = requests.get(url)\n return {\n 'last_modified': response.headers.get('last-modified'),\n 'content_hash': hash(response.text)\n }\n\n def get_social_metrics(self, retailer):\n # Fetch social media engagement\n platforms = ['twitter', 'facebook', 'instagram']\n metrics = {}\n for platform in platforms:\n # API calls to fetch metrics\n metrics[platform] = self.fetch_social_metrics(retailer, platform)\n return metrics\n\n def analyze_reviews(self, retailer):\n # Aggregate customer reviews\n sources = ['trustpilot', 'google', 'sitejabber']\n reviews = []\n for source in sources:\n reviews.extend(self.fetch_reviews(retailer, source))\n return self.analyze_sentiment(reviews)\n\n def generate_report(self):\n for retailer in self.retailers:\n self.data[retailer] = {\n 'website': self.fetch_website_updates(retailer),\n 'social': self.get_social_metrics(retailer),\n 'reviews': self.analyze_reviews(retailer),\n 'press': self.fetch_press_mentions(retailer),\n 'timestamp': datetime.now()\n }\n return self.data\n\n def update_dashboard(self):\n self.generate_report()\n self.create_html_dashboard()\n```\n\n3. Dashboard HTML Template:\n```html\n\n\n\n \n Retail Market Monitor\n \n \n\n\n

Retail Market Monitor

\n \n \n

\n {% for retailer in retailers %}\n

\n \n \n

Website Updates

Last Modified: {{ retailer.website.last_modified }}

\n \n \n

Social Engagement

\n \n \n

Customer Sentiment

\n {{ retailer.sentiment }}%\n

\n \n \n

Recent Press

{{ article.title }}

\n {% endfor %}\n

\n \n \n

Market Trends

\n\n\n```\n\n4. Automation Setup:\n```python\n# Schedule updates\ndef job():\n monitor = RetailMonitor(retailers)\n monitor.update_dashboard()\n\nschedule.every(30).days.do(job)\n\nwhile True:\n schedule.run_pending()\n time.sleep(3600) # Check every hour\n```\n\n5. Sample Dashboard Data:\n```json\n{\n \"Amazon\": {\n \"website_updates\": {\n \"new_features\": [\"Buy with Prime integration\", \"Live shopping\"],\n \"product_launches\": 127,\n \"last_updated\": \"2025-03-30T15:45:00Z\"\n },\n \"social_metrics\": {\n \"twitter\": {\n \"followers\": 5200000,\n \"engagement_rate\": 2.8,\n \"monthly_mentions\": 45000\n },\n \"facebook\": {\n \"followers\": 12000000,\n \"engagement_rate\": 1.9,\n \"monthly_reach\": 8500000\n }\n },\n \"customer_sentiment\": {\n \"overall_score\": 4.2,\n \"total_reviews\": 25000,\n \"positive_percentage\": 84\n },\n \"press_mentions\": [\n {\n \"title\": \"Amazon Expands Same-Day Delivery\",\n \"source\": \"Reuters\",\n \"date\": \"2025-03-29\"\n }\n ]\n }\n}\n```\n\nWould you like me to:\n1. Add more retailers to monitor?\n2. Include additional metrics?\n3. Customize the dashboard layout?\n4. Set up email notifications?", "extensions": [ { "name": "Computer Controller", "command": "computercontroller", "is_builtin": true, "environmentVariables": [ { "name": "dashboard_path", "description": "Path where the dashboard HTML should be saved", "required": false } ] }, { "name": "Tavily Web Search", "command": "npx -y tavily-mcp", "is_builtin": false, "link": "https://github.com/tavily-ai/tavily-mcp", "environmentVariables": [ { "name": "num_retailers", "description": "Number of top retailers to monitor", "required": false }, { "name": "update_frequency", "description": "How often to update the dashboard in days", "required": false }, { "name": "search_type", "description": "Tavily Web Search type (comprehensive or news)", "required": false } ] } ] }

Retail Market Monitor

{{ retailer.name }}

Website Updates

Social Engagement

Customer Sentiment

Recent Press

Market Trends