Action recognition based on human skeleton structure represents nowadays a prosper research field. This is mainly due to the recent advances in terms of capture technologies and skeleton extraction algorithms. In this context, we observed that 3D skeleton-based actions share several properties with handwritten symbols since they both result from a human performance. We accordingly hypothesize that the action recognition problem can take advantage of trial and error already carried out on handwritten patterns. Therefore, inspired by one of the most efficient and compact handwriting feature-set, we propose in this paper a skeleton descriptor referred to as Handwriting-Inspired Features (HIF3D). First of all a data preprocessing is applied to joint trajectories in order to handle the variabilities among actor's morphologies. Then we extract the HIF3D features from the processed joint locations according to a time partitioning scheme so as to additionally encode the temporal information over the sequence. Finally, we selected the Support Vector Machine (SVM) to achieve the classification step. Evaluations conducted on two challenging datasets, namely HDM05 and UTKinect, testify the soundness of our approach as the obtained results outperform the state-of-the-art algorithms that rely on skeleton data.