Fact-checking is a crucial yet challenging task that continues to gain importance. In an effort to address this issue, the FEVER large-scale dataset was developed to facilitate evidence-based fact-checking using Wikipedia as a reference. Despite numerous proposed approaches and evaluations on this dataset, a comprehensive understanding of the errors made by these approaches is still lacking. Here, we aim to bridge this gap. We introduce a diagnostic taxonomy and a generative framework to enhance FEVER-style fact-checking. We establish a taxonomy of errors and we construct a diagnostic dataset that enables the analysis of the errors made by state-of-the-art models as well as their distribution within the FEVER dataset. Additionally, we provide a set of prompts to generate examples within this taxonomy. Our experiments demonstrate promising results through the utilization of these generated examples for fine-tuning.