This paper improves the test of symmetry by Fernandes, Mendes and Scaillet (2015) through combining it with the generalized gamma kernels, a new class of asymmetric kernels proposed by Hirukawa and Sakudo (2015). It is demonstrated that the improved test statistic has a normal limit under the null of symmetry and is consistent under the alternative. A test-oriented smoothing parameter selection method is also proposed to implement the test. Monte Carlo simulations indicate superior finite-sample performance of the test statistic. It is worth emphasizing that the performance is grounded on the first-order normal limit and a small number of observations, despite a nonparametric convergence rate and a sample-splitting procedure of the test.