Craig enhanced user onboarding and model registry workflows across the Lightning-AI/lightning-thunder and Lightning-AI/pytorch-lightning repositories. He addressed a broken installation link in the thunder documentation, ensuring users could reliably access setup instructions and reducing friction for new adopters. In pytorch-lightning, Craig clarified guidance around the LitModelCheckpoint feature, updating documentation to specify the need for the litmodels package and improving messaging for default checkpointing behavior. His work focused on code refactoring and documentation hygiene, leveraging Python and Markdown to deliver clearer product messaging. These targeted improvements deepened alignment with model registry capabilities and streamlined the experience for both new and existing users.