Many data mining tasks rely on pattern mining. To identify the patterns of interest in a dataset, an analyst may deﬁne several measures that score, in different ways, the relevance of a pattern. Until recently, most algorithms have only handled constraints in an efﬁcient way, i.e., every measure had to be associated with a user-deﬁned threshold, which can be tricky to determine. Skypatterns were introduced to allow analysts to simply deﬁne the measures of interest, and to get as a result a set of globally optimal and semantically relevant patterns. Skypatterns are Pareto-optimal patterns: no other pattern scores better on one of the chosen measures and scores at least as well on every remaining measure. This article tackles the search of the skypatterns in a more general context than the 0/1 (aka Boolean) matrix: the fuzzy tensor. The proposed solution supports a large class of measures. After explaining why and how their common mathematical property enables a safe pruning of the search space, an algorithm is presented. It builds upon multidupehack, a generalist pattern mining framework, which is now able to efﬁciently list skypatterns in addition to enforcing constraints on them. Experiments on two real-world fuzzy tensors illustrate the versatility of the proposal. Other experiments show it is typically more than one order of magnitude faster than the state-of-the-art algorithms, which can only mine 0/1 matrices.