I'll agree with @BrianSpiering on general approaches, and with you that this is a no-free-lunch situation. But...
Oversampling seems reasonably to fit in just about anywhere. It may depend on what kind of oversampling you're doing. I could see the new points messing up the distributions and thus affecting everything else, but it could potentially also make the other steps more robust.
(Then again, I'm still looking for examples where resampling techniques help substantially beyond threshold selection. See https://stats.stackexchange.com/questions/247871/what-is-the-root-cause-of-the-class-imbalance-problem and its linked questions.)
Outlier removal should happen early, as outliers will affect skew/scaling corrections. Skew/scaling feel like the same process to me, and if they're separate steps I suspect they can occur in either order.
I would keep feature selection for near the end, lest an important feature be overlooked because it is skew/unscaled/outlier-prone. (Feature selection methods are notoriously error-prone though, so I wouldn't be surprised if moving it around performed better for certain datasets.)