Unsupervised text classification with R/Python
I can think of two approaches.

You could use term-frequency/inverse-document-frequency (tf-idf) to cluster the vectors. Personally, I would start by first clustering the full text of the original job vacancies and then use this to assign clusters to the vectors. I have the feeling that it will outperform clustering directly the vectors.
There are implementations both in R and in Python. I would use Python for clustering in a language other than English. You can use TfidfVectorizer from sklearn.
I've a post on medium with an example of clustering wikipedia articles in Python if you are interested.

You can start by a list of key/basic skills and then try to match your vectors to the items of this list. For defining a distance between words/phrases you can:
a. use Jaccard index (ex. see this)
b. use SpaCy in Python to find descriptions that are similar. I believe there is a language model for Polish. I haven't tried it but I would use something that is described in &quot;The Beginner’s Guide to Similarity Matching Using spaCy&quot;. Since you are trying to find the similarity between phrases (not single words) you might also  want to consider this stackoverflow discussion.