A unifying framework for post-training time series foundation models (TSFMs) is proposed to adapt, augment, compose, calibrate, or specialize pretrained TSFMs for reliable downstream deployment. The framework categorizes post-training methods into five categories: parameter adaptation, context augmentation, model composition, output processing and uncertainty control, and compression and specialization.