Today, short-videos dominate social media, yet short-video creators lack systematic tools to predict engagement and refine content before uploading. Existing approaches focus primarily on post-publication metrics, failing to address engagement prediction via video editing elements. To address this, we curate VidES, a novel dataset linking short-vdieo engagement to specific editing elements, referred to as Edit Signals (e.g.: narration, text overlays). VidES contains overall engagement scores and detailed human evaluations of Edit Signals. Building on VidES, we propose SmartEdit, a multi-stage framework that predicts short-video engagement and generates actionable improvement suggestions based on Edit Signals. SmartEdit decomposes engagement prediction into interpretable components and uses few-shot Multi-modal Large Language Models to provide specific feedback for content refinement. Experimental results validate SmartEdit’s effectiveness, demonstrating its superiority over zero-shot methods. This work bridges the gap between intuitive and data-driven short-video editing, offering the first dataset and tool tailored for Edit Signal-based short-video refinement.