Skypatterns are an elegant answer to the pattern explosion issue, when a set of measures can be provided. Sky-patterns for all possible measure combinations can be explored thanks to recent work on the skypattern cube. However, this leads to too many skypatterns, where it is difficult to quickly identify which ones are more important. First, we introduce a new notion of pattern steadiness which measures the conservation of the skypattern property across the skypattern cube, allowing to see which are the " most universal " skypatterns. Then, we extended this notion to partitions of the dataset, and show in our experiments that this both allows to discover especially stable skypatterns, and identify interesting differences between the partitions.