Developed and integrated the LARX Generator for Cayley growth datasets within the Cayleypy repository, focusing on reproducible synthetic data generation for graph theory research and simulations. Leveraging Python, the work included implementing a dedicated dataset and comprehensive test coverage to ensure reliability and guard against regressions. The approach emphasized maintainability and reproducibility, supporting downstream users of the library. Additionally, the pyproject.toml configuration was updated to ignore a specific linting rule, which improved continuous integration stability by reducing unnecessary noise. The contribution centered on data generation, testing, and graph theory, delivering a focused feature with robust validation in a collaborative environment.