In this work we offer an open and data-driven skills taxonomy, which is independent of ESCO and O*NET, two popular available taxonomies that are expert-derived. Since the taxonomy is created in an algorithmic way without expert elicitation, it can be quickly updated to reflect changes in labour demand and provide timely insights to support labour market decision-making. Our proposed taxonomy also captures links between skills, aggregated job titles, and the salaries mentioned in the millions of UK job adverts used in this analysis. To generate the taxonomy, we employ machine learning methods, such as word embeddings, network community detection algorithms and consensus clustering. We model skills as a graph with individual skills as vertices and their co- occurrences in job adverts as edges. The strength of the relationships between the skills is measured using both the frequency of actual co-occurrences of skills in the same advert as well as their shared context, based on a trained word embeddings model. Once skills are represented as a network, we hierarchically group them into clusters. To ensure the stability of the resulting clusters, we introduce bootstrapping and consensus clustering stages into the methodology. While we share initial results and describe the skill clusters, the main purpose of this paper is to outline the methodology for building the taxonomy.