One challenge in robot learning is often overlooked: Data collected with one embodiment does not necessarily transfer well to another. A human hand, a parallel gripper, and a dexterous robot such as the X Square Robot all have different contact mechanics, kinematics, and action spaces. Simply retargeting motion can lose the fine-grained details that make a manipulation task work. TwinDEX takes a different approach. It co-designs a wearable manipulation interface with a structurally matched dexterous hand for the X Square Robot, aligning motion structure, contact surfaces, sensing, vision, and timing. The result is robot-free demonstration data that remains closely aligned with the robot being trained. In one reported experiment, a policy trained only on wearable demonstrations completed a chemistry workflow with 20+ sub-actions. No on-robot teleoperation data was used for training. An interesting example of hardware, data, and policy co-design for scaling dexterous manipulation.