Developed a File Pruning Observability Enhancement for the imputnet/helium repository, focusing on improving diagnostics within the file pruning workflow. Leveraging Python for backend development, the work introduced detailed logging that captures missing file paths alongside error messages when files cannot be pruned. This approach enhanced error tracking and provided richer context for debugging, enabling faster root-cause analysis and reducing troubleshooting time for pruning failures. The implementation aligned with existing instrumentation and operational practices, strengthening the reliability of the pruning pipeline. Emphasis on logging and error handling ensured that future optimization efforts would benefit from more comprehensive diagnostic data.