Chirality information (i.e., information that allows distinguishing left from right) is ubiquitous for various data modes in computer vision, including images, videos, point clouds, and meshes. Contrary to symmetry, for which there has been a lot of research in the image domain, chirality information in shape analysis (point clouds and meshes) has remained underdeveloped. Although many shape vertex descriptors have shown appealing properties (e.g. robust to rigid-body pose transformations), they are not able to disambiguate between left and right symmetric parts. Considering the ubiquity of chirality information in different shape analysis problems and the lack of chirality-aware features within current shape descriptors, developing a chirality feature extractor becomes necessary and urgent. In this paper, based on the recent framework Diff3f, we proposed an unsupervised chirality feature extraction pipeline to decorate shape vertices with chirality-aware information, extracted from 2D foundation models. Quantitative and qualitative results of various experiments and downstream tasks include left-right disentanglement, shape matching, and part segmentation conducted on a variety of datasets proving the effectiveness and usefulness of our extracted chirality features. The code will be available once this work is accepted.