Chizuki worked on the stanfordnlp/dspy repository, focusing on strengthening the Usage Tracking module by addressing a data-merging issue that caused TypeErrors when handling mixed types and None values. Using Python and backend development skills, Chizuki refactored the merge logic to improve readability and maintainability, ensuring that usage data merges accurately and consistently. Comprehensive unit tests were added to cover edge cases involving mixed types and None scenarios, reducing the risk of regressions. This work enhanced the reliability of analytics data, supporting more accurate product decisions and enabling faster debugging of analytics issues through improved data integrity and robust test coverage.