I've been doing quite a bit of reading on the topic of non-normality, and how it pertains to the F-test. My understanding is that non-normality tests aren't very useful, as outlined in the arguments in this Cross Validated Thread.
However, the following question still stands: if the F-test isn't robust against non-normal data, what should a practitioner do? Upon researching this concern, I've found many resources (for example, this GraphPad guide that essentially advise the practitioner to keep using the standard tests (i.e., T-test, ANOVA, etc.) if the data is "only approximately Gaussian." OK, but at what point do we step away from, say, the F-test, and use a non-parametric test? When is the non-normality too much?