Shill-O-Tron 3000 is a proof-of-concept Chrome extension designed to detect and highlight potential shilling activity on Reddit. It scans comment threads for repetitive link promotion and flags suspicious behavior—all in a playful and experimental way. 🚀
- Detects repetitive link promotion in Reddit comment threads
- OAuth integration for secure Reddit API access
- Local storage of tokens (no external servers involved)
- User privacy focused – no personal data is stored beyond your session
- Fully open-source and transparent!
- Download the repository:
git clone https://github.com/lucioamor/shill-o-tron-3000.git
- Open Chrome and navigate to
chrome://extensions/
- Enable "Developer Mode" (toggle in the top right corner)
- Click "Load unpacked"
- Select the folder containing the cloned repo
- The extension is now installed!
- Click on the Shill-O-Tron 3000 popup in Chrome
- Authenticate via Reddit OAuth (read-only access to comments)
- Navigate to a Reddit thread and activate the "Zap Shills Now! ⚡" button
- The extension analyzes the comments for repetitive link promotion
- Suspicious activity is flagged for further review!
Shill-O-Tron 3000 requests the following permissions:
activeTab
– To analyze the Reddit thread currently being viewedidentity
– For secure Reddit OAuth authenticationstorage
– To store Reddit access tokens locally (no external storage)https://www.reddit.com/*
andhttps://oauth.reddit.com/*
– Required for API access to retrieve public comment data
For more details, see the Permissions Explained page.
Your data stays on your device. We do not store, sell, or share any personal information. Full details can be found in our Privacy Policy.
Contributions are welcome! If you’d like to improve Shill-O-Tron 3000:
- Fork the repository
- Create a new branch (
git checkout -b feature-branch
) - Make your changes and commit (
git commit -m "Added awesome feature"
) - Push to your branch (
git push origin feature-branch
) - Open a Pull Request 🎉
This project is licensed under the MIT License. See the LICENSE file for details.
For inquiries, visit nxlv.ai or reach out on GitHub!