We present MOTIFY, a novel approach for predicting scene transitions and actions from mobile operating system (OS) task videos. By leveraging pretrained Vision-Language Models (VLMs), MOTIFY extract the task sequences from real-world YouTube videos without manual annotation. Our method addresses the limitations of existing approaches, which rely on manual data annotation or simulation environments. We demonstrate MOTIFY's effectiveness on a diverse set of mobile OS tasks across multiple platforms, outperforming baseline methods in scene transition detection and action prediction. This approach opens new possibilities for scalable, real-world mobile agent development and video understanding research.