gemma_noise_detector_prototype

You can automatically classify codebase files as noise or signal using rule-based checks and the Gemma model to identify temporary files and backups while protecting essential source code and documentation.

The best way to identify temporary files and backups is using automated noise detection that analyzes file extensions, age, naming conventions, and directory context to label clutter for deletion.

You can use the Gemma model for file classification alongside built-in rule-based checks for extensions and naming conventions, ensuring critical assets like configuration files are protected without manual rule configuration.

Automated noise detection protects critical source code by enforcing critical path protection, ensuring essential files like source code, configuration, and documentation are always marked as signal and excluded from cleanup.

To scan a large repository and flag cache directories and log files, point the classifier at the repository path to output noise labels and facilitate autonomous cleanup of the identified clutter.

Limitations of rule-based checks for code cleanup include relying on file extensions and directory context, which may require manual review of ambiguous files to avoid deleting unclassified but necessary assets.