Skeletal parallel programming makes both parallel programs development and parallelization easier. The idea is to abstract generic and recurring patterns within parallel programs as skeletons and provide them as a library whose parallel implementations are transparent to the programmer. SkeTo is a parallel skeleton library that enables programmers to write parallel programs in C++ in a sequential style. However, SkeTo's matrix skeletons assume that a matrix is dense, so they are incapable of efficiently dealing with a sparse matrix, which has many zeros, because of duplicated computations and commutations of identical values. This problem is solved by re-formalizing the matrix data type to cope with sparse matrices and by implementing a new C++ class of SkeTo with efficient sparse matrix skeletons based on this new formalization. Experimental results show that the new skeletons for sparse matrices perform well compared to existing skeletons for dense matrices.