VPN Promotional Offer and Price Change Tracking Plan

1. Objective

To automatically track promotional offers and price changes for various VPN services, providing timely updates for the "VPN Reviews Daily" company, enabling competitive analysis and dynamic content generation.

2. Tools & Integrations

• `web_search`: For initial discovery of VPN provider websites and affiliate program details.
• `read_url`: To extract content from VPN provider pages, particularly pricing and promotions sections.
• `http_request`: To interact with VPN provider APIs (if available) or affiliate network APIs for programmatic data retrieval.
• `schedule`: To set up recurring checks for price and offer changes.
• `write_file`: To save scraped data, plan details, and status reports.
• `edit_company_goal`: To update the status of tracking goals within the "VPN Reviews Daily" company.
• `run_terminal`: For potential scripting of more complex scraping or data processing tasks if built-in tools are insufficient.

3. High-Level Workflow

• Identify Target VPN Providers: Use `web_search` to find leading VPN providers and their official websites.
• Affiliate Program Research: Investigate if chosen VPN providers have public APIs for affiliates or offer data feeds. If not, plan for direct website scraping.
• Data Extraction Strategy (Per Provider):

* API Integration: If API available, use `http_request` to fetch pricing, discount codes, and offer validities.

* Website Scraping: If no API, use `read_url` to fetch page content. Develop a parsing strategy (likely using `run_terminal` with Python/Node.js script if advanced parsing is needed) to extract relevant pricing, promotional text, and expiry dates.

• Data Storage: Store extracted data in structured formats (e.g., JSON files) within the company's project directory.
• Change Detection: Implement logic to compare newly extracted data with previously stored data to identify changes in prices or offers.
• Trigger Actions: Upon detecting a change:

* Log the change with a timestamp.

* Trigger alerts (e.g., via `notify` if configured for G-Sama).

* Update relevant company goals using `edit_company_goal`.

* Potentially trigger content generation processes (not part of this plan, but a logical next step).

• Scheduling: Use `schedule` to automate the data extraction and change detection process at regular intervals (e.g., daily, weekly).

4. Detailed Implementation Steps

Step 4.1: Initial VPN Provider and Website Identification

• Action: Use `web_search` with queries like "best VPN services 2024 pricing", "VPN affiliate programs API".
• Output: A list of URLs for main VPN provider websites and any potential API documentation.
• Storage: Save this list to a file, e.g., `/Users/nemuclaw/.sovereign/company/companies/vpn-reviews-daily/projects/vpn_providers.json`.

Step 4.2: Develop Data Extraction Modules (Per Provider)

Option A: API Integration (Preferred)

• Action: For each provider with an API, use `http_request` to develop a function that fetches current pricing and offers.
• Example `http_request`: `default_api.http_request(url="https://api.vpnprovider.com/pricing", headers={"Authorization": "Bearer YOUR_API_KEY"})`
• Output: Raw JSON data containing pricing, discounts, terms.

Option B: Website Scraping

• Action: For providers without APIs, use `read_url` to fetch the HTML content of pricing pages. Then, employ `run_terminal` to execute a small Python/Node.js script that parses the HTML to extract price points, discount percentages, and offer descriptions.
• Example `read_url`: `default_api.read_url(url="https://www.vpnprovider.com/pricing")`
• Example `run_terminal` (Python for parsing):

    import json
from bs4 import BeautifulSoup

html_content = default_api.read_file(path="/tmp/vpn_pricing.html")
soup = BeautifulSoup(html_content, 'html.parser')
# Example: find pricing element
price_element = soup.find('span', class_='price-value')
price = price_element.text if price_element else "N/A"
print(json.dumps({"price": price}))
    

• Output: Structured data (JSON) with extracted pricing and offer details.

Step 4.3: Implement Data Storage and Change Detection

• Action: After extracting data for each provider, load the previous state from disk, compare, and save the new state.
• Logic: Use `read_file` to get previous data, perform a diff, then `write_file` to save the new data.
• File Path Convention: `/Users/nemuclaw/.sovereign/company/companies/vpn-reviews-daily/data/_pricing.json`.

Step 4.4: Define Trigger Actions

• Action: If a change is detected, perform the following:

* Log the change to a history file: `/Users/nemuclaw/.sovereign/company/companies/vpn-reviews-daily/logs/price_changes.log`.

* Optionally, use `notify` to send a macOS notification to G-Sama about significant changes: `default_api.notify(title="VPN Price Change Alert", message="[Provider Name] price changed from X to Y!")`

* Update relevant company goals using `edit_company_goal` (e.g., mark "Monitor [Provider] Pricing" as in-progress or completed if a manual review is needed).

Step 4.5: Scheduling the Automation

• Action: Use `schedule` to run the data extraction and change detection process.
• Example Daily Schedule: `default_api.schedule(interval="1d", description="Run VPN price tracking for all providers.", message="Kicking off daily VPN price tracking.")`
• Example Hourly Schedule (for critical providers): `default_api.schedule(interval="1h", description="Hourly check for critical VPN provider offers.")`

5. Reporting and Monitoring

• Periodically review the `price_changes.log`.
• Monitor scheduled tasks using `list_tasks` to ensure the automation is running correctly.
• G-Sama can check the status of goals related to VPN tracking in the Watchdog company tab.

6. Future Enhancements

• Integrate directly with content generation agents to automatically draft articles or updates based on detected price changes.
• Implement sentiment analysis on promotional text to categorize offer attractiveness.
• Expand to cover more VPN providers and different regions/currencies.