Kol King focused on stabilizing the core neighbor data flow in the metatensor/metatrain repository by addressing a critical bug in the Neighbor List Registration System. Using Python and leveraging skills in algorithm optimization and data processing, Kol corrected a misreferenced system update, ensuring that neighbor information was accurately propagated throughout the pipeline. This fix reduced the risk of downstream data integrity issues, directly improving the reliability of model training workflows. Kol also enhanced regression coverage for the affected workflow, providing safeguards against similar issues in future development. The work established a stable foundation for upcoming feature enhancements and broader pipeline improvements.