The article introduces a new method to customize goodness-of-fit tests by transforming the empirical distribution function. By using a specific transform, non-parametric tests can be created for different types of distributions. The researchers discuss three new tests that correct the Kolmogorov-Smirnov test for goodness-of-fit. One test is effective at determining if the data comes from a distribution with heavier tails, another identifies distributions heavier in the middle, and the last test detects if the data deviates significantly from the hypothesized distribution at specific quartiles. The tests' performance is evaluated through a power study.