This is likely due to the fact that Skip-gram is trained by using a centre word to maximise the probability of each context word, whereas CBOW uses the average of context word input vectors to maximise the probability of the centre word.
skip-gram performs better in modelling one-word context, while CBOW has better performance in modelling multi-context words.