In this work, we present Skor-xG, the first model to introduce 3D player skeletons into Expected Goal (xG) estimation as far as we are aware. xG estimation is a fundamental task in soccer analytics that quantifies a shot's likelihood of scoring. Unlike existing xG models which primarily rely on engineered features from event data and 2D positional data, Skor-xG leverages detailed player postures to enhance shot evaluation. To effectively capture the complex interactions between player body parts and the ball, we propose a Graph Neural Network-based framework that models each shot as a spatiotemporal graph. Experimental results demonstrate that incorporating skeleton data improves xG estimation compared to conventional approaches. As 3D player tracking technology becomes increasingly accessible, Skor-xG establishes skeleton data as a valuable new dimension in soccer analytics, enabling deeper tactical insights and more precise performance evaluation.