Does it make sense to talk about skip-gram and cbow when using The Glove method?
Not really, skip-gram and CBOW are simply the names of the two Word2vec models. They are shallow neural networks that generate word embeddings by predicting a context from a word and vice versa, and then treating the output of the hidden layer as the vector/representation. GloVe uses a different approach, making use of the global statistics of a corpus by training on a co-occurrence matrix, rather than local context windows.
I'm trying different word embeddings methods, in order to pick the approache that works the best for me. I tried word2vec and FastText. Now, I would like to try Glove. In both word2vec and FastText, there is two versions: Skip-gram (predict context from word) and CBOW (predict word from context). But in Glove python package, there is no parameter that enables you to choose whether you want to use skipg-gram or Cbow. 
Given that Glove does not work the same way as w2v, I'm wondering: Does it make sense to talk about skip-gram and cbow when using The Glove method ?
Thanks in Advance