The behaviors of many complex systems, from nanostructured materials to animal colonies, are governed by local events/rearrangements that, while involving a restricted number of interacting units, may generate collective cascade phenomena. Tracking such local events and understanding their emergence and propagation in the system is often challenging. Common strategies consist, for example, in monitoring over time parameters (descriptors) that are designed ad hoc to analyze certain systems. However, such approaches typically require prior knowledge of the system's physics and are poorly transferable. Here, we present a general, transferable, and agnostic analysis approach that can reveal precious information on the physics of a variety of complex dynamical systems starting solely from the trajectories of their constitutive units. Built on a bivariate combination of two abstract descriptors, Local Environments and Neighbors Shuffling and