Recently, multi-layer perceptron-based implicit representations have achieved remarkable successes in hand modeling. Compared with previous explicit mesh-based representation methods, implicit methods are more compact shape representations. However, it is expensive to obtain explicit geometry surfaces from implicit func tions with Marching Cubes, which limits the real-time performance in surface reconstruction applications. To explore a more effective and efficient hand representation, we present a skeleton-driven method to represent a human hand with a point cloud. To achieve this goal, we propose a Tri-Axis Modeling method to model the motion pattern of the xyz coordinate of a patch of point cloud, and an Order Encoding strategy to construct a parameter-sharing and geometry-disentangled network. These two effective strate gies make our method run in real-time and has super-high fidelity close to implicit methods. Qualitative and quantitative experiments on public datasets demonstrate the efficiency, effectiveness, and robustness of our method against state-of-the-art approaches.