Source Score: Using AI to automate addition of new sources

The process you've described for automating the addition of new sources using GitHub Actions and an AI-driven script is quite innovative. Here's a summary of what you accomplished:

1. Setup: You created a Python script that uses an AI model (likely through OpenRouter) to scrape, validate, and generate YAML files for new sources.

2. GitHub Actions Workflow:

   • A scheduled workflow runs on the first day of each month.
   • It checks out the repository, creates a new branch, sets up Python, and runs your script.
   • The script generates new YAML files if there are any new sources to add.
   • Any new YAML files are committed and a pull request is opened.

3. Validation:

   • Once the PR lands, another workflow validates the new YAML files.
   • If validation passes, the new sources are posted to your API, making them available in the live dashboard.

4. Results: The process now takes less than a minute of human time each month and has successfully added new sources with correct URLs and descriptions.

Next Steps: Claims and Proofs

For claims and proofs, you'll face more complex challenges:

1. Data Complexity:
   • Claims and

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