Behavioral research relies on evaluating measurable behaviours to extract their underlying social, cognitive, and neurophysiological mechanisms. Although traditional methods commonly involve simple measures of discrete movements (e.g., RT of keypresses), analyses of dynamic human movement patterns have shown to reveal additional insights. Effectively extracting information from movement trajectories patterns can be challenging because of the complex and dynamic nature of the movements. The current presentation outlines a custom Python toolkit for analyzing human manual movements and extracting relevant information. This toolkit can process single discrete rapid aiming movements in two- (e.g., cursor pointing) and three-dimensional (e.g., manual pointing) space, as well as cyclical movements (e.g., Fitts’s Law task). This toolkit uses various Python libraries, including NumPy and SciPy, and provides a set of frequently used functions for analyzing movement trajectory data. To ensure versatility and user-friendliness, the toolkit offers two approaches: an automated method that processes raw data and generates relevant measurements without intervention, and a manual approach that allows users to selectively utilize different functions according to their specific requirements. The results of a behavioral experiment based on the spatial cueing paradigm was conducted and will be reported to demonstrate the practical application of this toolkit. Readers are encouraged to access the publicly available data and analysis scripts to gain insight into kinematic analysis for human movements.