Developed a data readiness reporting enhancement for the APPFL/APPFL repository, focusing on improving the reliability and transparency of client training workflows for the Cora dataset. The work involved refining the generation of data readiness reports and strengthening error handling for client training indices, which reduced the risk of workflow failures and enabled faster troubleshooting. Leveraging Python, PyTorch, and data analysis techniques, the developer incorporated code review feedback to improve maintainability and readability. This feature established a foundation for scalable data readiness reporting, supporting future training workloads and contributing to smoother, more robust machine learning pipelines within the APPFL/APPFL project.