The discovery of patterns using a minimal set of assumptions constitutes a central challenge in the modeling of polyphonic music and complex streams in general. Skipgrams have been found to be a powerful model for capturing semi-local dependencies in sequences of entities when dependencies may not be directly adjacent (see, for instance, the problems of modeling sequences of words or letters in computational linguistics). Since common skipgrams define locality based on indices, they can only be applied to a single stream of non-overlapping entities. This paper proposes a generalized skipgram model that allows arbitrary cost functions (defining locality), efficient filtering, recursive application (skipgrams over skipgrams), and memory efficient streaming. Further, a sampling mechanism is proposed that flexibly controls runtime and output size. These generalizations and optimizations make it possible to employ skipgrams for the discovery of repeated patterns of close, nonsimultaneous events or notes. The extensions to the skipgram model provided here do not only apply to musical notes but to any list of entities that is monotonic with respect to a given cost function.