What is the advantage (if any) of using Cohen's $\omega$ instead of Cramer's V for effect size? I'm wondering why I should or should not use Cohen's $\omega$ (omega) for effect size over Cramer's V for a chi-square goodness-of-fit test?
What would be the advantage or disadvantage of favoring Cohen's $\omega$?
I am using the rcompanion package with the cohenW() and cramerV() functions.

As you mention that you're using the variant* of Cramér's V in a goodness-of-fit scenario, in the case of equally-distributed expected frequencies, one advantage of the $V$ variant over Cohen's $\omega$ is its upper bound of 1. So you can see how far the effect size is from its maximum possible value. There's the same advantage in the case of a contingency table, where $V$ is a measure the association between two variables. As noted in Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.) (in the case of $\omega$ used as a measure of association in contingency tables):

Although $\omega$ is a useful ES index in the power analysis of contingency
tables, as a measure of association it lacks familiarity and
convenience

I think that the same remark holds for Cohen's $\omega$ in the case of a one-dimensional table, not just contingency tables.
Note that Cohen's omega has also an upper bound of 1 in the case of two categories (which implies $\omega=V$, see the penultimate paragraph below) with expected proportions of 0.5. But in other cases, $\omega$ maximum possible value exceeds 1, so it might be difficult to interpret.
On the other hand, Cohen's $\omega$ allows you to directly use it for power calculation - again, see Cohen (1988).
In cases other than equally-distributed expected frequencies (always in the situation of a one-dimensional table), as the variant of V can exceed 1, it loses its only advantage. You might have some context-dependent advantages (e.g. if you're more familiar with one coefficient than the other, or if you're trying to compare studies using the same coefficient) but I don't think this is generally a massive advantage – though I'd be happy to be corrected here. As Cohen's w is generally the effect size that software ask you as an input in the context of power analysis (see the pwr library for example), for any pratical purposes it's probably more relevant to use it than the $V$ variant in unequally-distributed expected frequencies.
As a side note, you might be interested by the fact that $\omega = V\times\sqrt{k-1}$, where $k$ is the number of categories used in the Cramér's V formula, so converting one to the other is quite straightforward.
You might be also interested by the Pearson contingency coefficient (C)  and by the Fei coefficient (פ), that are bounded between 0 and 1. If you refer to the R effectsize library, they can be used as an effect size measure in the case of a one-dimensional table. You can find formulas for these two coefficients in the documentation of the  effectsize library, page 5. The library authors recommend using Fei over Cohen's w and Pearson's C for reasons explained here (i.e. they claim that Cohen's w and Pearson's C are inflated, but I'm not sure on what they rely to say this).

* calling it "Cramér's V" is probably a misuse of language, as this variant for one-dimensional table doesn't seem to have been proposed by Cramér. After looking around, the origin of this $V$ variant is unclear to me. After re-reading Cohen's book discussion on $\omega$ and $V$ (pp. 216-224), it seems that Cohen implicitly excludes the use of $V$ for one-dimensional tables. See Cramér's $V$ for one variable for some additional discussion on the use of the $V$ variant on one-dimensional tables.
