Folding principles uncovered in these simple models can be used to inform the selection and design of novel foldamer monomers. We choose to implement these simple models in Rosetta, an existing protein prediction software such that we can take advantage of many of the physics-based methods used in traditional protein folding algorithms. For example, Rosetta offers fast sampling of configuration spaces of heteropolymer models using Monte Carlo (MC) minimization methods. We introduce cg_pyrosetta as an extension to Rosetta which adds new CG functionality. In this package, we add new CG models to Rosetta's library allowing users to fold a wide range of CG representations of foldamers in the Rosetta workflow. Using Rosetta's Python-wrapped C++ library we are able to build a set of protocols which identify low energy structures available to CG foldamer models. Using cg_pyrosetta, we can explore the parameter space of CG foldamer models and investigate the underlying physical driving forces of secondary structure formation.