In this paper, we show the enhancing of the Demanded Skills Diagnosis (DiCoDe: Diagnóstico de Competencias Demandadas), a system developed by Mexico City’s Ministry of Labor and Employment Promotion (STyFE: Secretaría de Trabajo y Fomento del Empleo de la Ciudad de México) that seeks to reduce information asymmetries between job seekers and employers. The project uses webscraping techniques to retrieve job vacancies posted on private job portals on a daily basis and with the purpose of informing training and individual case management policies as well as labor market monitoring. For this purpose, a collaboration project between STyFE and the Language Engineering Group (GIL: Grupo de Ingeniería Lingüística) was established in order to enhance DiCoDe by applying NLP models and semantic analysis. By this collaboration, DiCoDe’s job vacancies system’s macro-structure and its geographic referencing at the city hall (municipality) level were improved. More specifically, dictionaries were created to identify demanded competencies, skills and abilities (CSA) and algorithms were developed for dynamic classifying of vacancies and identifying terms for searches on free text, in order to improve the results and processing time of queries.