Large-sample hydrology requires consistent, high-quality geospatial attributes, yet generating catchment attributes for custom study areas remains a common bottleneck. This session presents an automated, scalable framework for extracting over 60 CAMELS-like hydrologic, topographic, climatic, and geological attributes for USGS gauge catchments. A key component of the workflow is the pygeoglim package, which leverages redistributed GLiM and GLHYMPS datasets hosted on the I-GUIDE platform to provide high-resolution subsurface properties including lithology, porosity, and permeability at the catchment scale. The framework supports reproducible, extensible, and community-ready workflows for large-sample hydrologic and environmental research.