During September 2025, Daniel contributed to the aws/sagemaker-distribution repository by developing a feature that conditionally installs the sm-spark-cli tool based on the status of SageMaker Workflows blueprints. He implemented logic in Python and Bash to update the post-startup script, ensuring it verifies the blueprint status before proceeding, which prevents unnecessary tool execution. Leveraging AWS DataZone and Boto3, Daniel enhanced the workflow client to accurately detect blueprint status and deploy tools only when required. This targeted approach improved deployment reliability and resource efficiency. The work demonstrated a focused application of conditional logic and scripting to streamline workflow automation.