P2-S1: Automate Data Fetching

# Phase 2, Step 1: Automate Data Fetching

This document details the process of automating the daily collection of data required for the "On This Day" collectible cards. This is the first major step in transitioning from manual creation to an automated workflow.

## Key Objectives:
* Develop scripts to automatically fetch all required data points (headlines, stats, etc.) from the sources identified in P1-S1.
* Implement error handling and logging for the data fetching process.
* Structure and store the fetched data in a consistent format (e.g., JSON) for use by subsequent automation scripts (design population).

## Prerequisites:
* **Completed P1-S1:** \"Data Specification Sheet\" defining data points, primary/backup sources, and extraction logic.
* **Completed Phase 1:** Successful manual creation and feedback gathering, leading to a decision to proceed with automation.
* **Basic Programming Skills:** Familiarity with a scripting language like Python or JavaScript (Node.js) is essential.

## Actionable Steps:

1.  **Choose a Scripting Language &amp; Environment:**
    * **Python:** Excellent for web scraping (libraries like `Requests`, `BeautifulSoup`) and API interaction. Widely used for data processing.
    * **Node.js (JavaScript):** Good for API interactions (`axios`, `node-fetch`) and can also be used for web scraping (though Python is often preferred for complex scraping).
    * Set up your development environment with the necessary interpreters, package managers (pip, npm), and IDE/text editor.

2.  **Develop Scripts for Each Data Source:**
    * For each data point listed in your \"Data Specification Sheet\":
        * **API Interaction:**
            * If the source is an API, use appropriate HTTP client libraries to make requests.
            * Handle authentication (API keys, tokens) securely (e.g., using environment variables, not hardcoding).
            * Parse the API response (usually JSON or XML) to extract the specific pieces of information needed.
        * **Web Scraping (Use with Caution):**
            * If a source has no API and you must scrape a webpage (ensure compliance with `robots.txt` and terms of service):
                * Use libraries like Python's `Requests` to fetch the HTML content and `BeautifulSoup` or `lxml` to parse it.
                * Identify stable HTML elements (tags, classes, IDs) that contain the target data.
                * Be aware that website structures change, which can break scrapers. This method is less reliable than APIs.
        * **RSS Feeds:**
            * Many news sites offer RSS feeds. Libraries exist in most languages (e.g., Python's `feedparser`) to easily parse these.

3.  **Implement Data Extraction &amp; Structuring Logic:*** Based on the \"Data Specification Sheet\" defining data points, primary/backup sources, and extraction logic.
    * Clean and format the data as needed (e.g., remove unwanted HTML, trim whitespace, convert data types).
    * Consolidate all fetched data points for a single day into a structured object (e.g., a Python dictionary or JavaScript object).

4.  **Error Handling &amp; Logging:**
    * **API Errors:** Handle potential issues like network errors, API rate limits, invalid responses (4xx, 5xx status codes). Implement retries with backoff if appropriate.
    * **Scraping Failures:** Handle cases where HTML structure changes or data is missing.
    * **Data Unavailability:** What if a specific piece of data simply isn't there on a given day?
    * **Logging:** Implement logging (e.g., Python's `logging` module) to record:
        * Successful data fetches.
        * Any errors encountered (with timestamps and details).
        * URLs accessed, API endpoints called.
        * This helps in debugging and monitoring.

5.  **Output Data in a Consistent Format:**
    * Serialize the structured data object into a common format, typically JSON.
    * Save this JSON data to a file with a consistent naming convention (e.g., `YYYY-MM-DD-data.json`). This file will be the input for the design automation script.

6.  **Scheduling (Initial Consideration):**
    * How will this script run daily?
    * Initially, you can run it manually.
    * For full automation later:
        * **Cron jobs (Linux/macOS) or Task Scheduler (Windows):** For running on a local machine/server.
        * **Cloud-based schedulers:** (e.g., AWS Lambda scheduled events, Google Cloud Scheduler, GitHub Actions scheduled workflows). This is more robust for a production system.

7.  **Testing:**
    * Test scripts thoroughly with various scenarios (e.g., successful fetches, API errors, missing data).
    * Verify the output JSON is correctly structured and contains all expected data.

## Deliverables for this Step:
* A set of scripts capable of automatically fetching all required daily data.
* Documented error handling and logging mechanisms within the scripts.
* Sample output JSON file(s) demonstrating the structured data.
* Initial plan for how the script will be run daily.

This step is a significant technical undertaking and forms the backbone of the automated content generation.

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