I have skewness and kurtosis values of 0.447 and -0.861 respectively. There are 540 observations and the standard deviation is 1.662. I have seen posts and suggestions saying between -1,1 and 2,2 should be fine but I haven't been able to find a consensus. If it is not strictly normally distributed, is it close enough that running tests that are robust to slight deviations will still produce good enough results?
Your variable is "days" and ranges from 0 to 7 (so, it seems like it is days per week).
It can't be normal. IF the histogram was close to normal then, for some purposes, you might get away with treating it as normal (although I probably wouldn't do this) but yours is not close to normal. It's not even unimodal.
To get better advice on what to do, please tell us the goals of your work, what your research questions and hypotheses are and so on.
EDIT in response to comment by the OP
Then this is a count variable and you should do a count regression model. The usual starting point is Poisson regression, but I, for one, have never had a model that met the assumptions of Poisson. So, I'd start with a negative binomial regression.
Is this a dependent variable? An independent variable? Something else?