Graph-based data representations excel at capturing relations among entities, e.g., for semantic web, artificial intelligence, or database applications, and their utility depends critically on their quality, usually enforced via graph schemas. These schemas are often violated, e.g., during data ingestion, calling for repair actions to achieve compliance. This paper presents a formal diagnostic framework for graph repair methods. Current methods often lack rigor and generality, since they rely on ad hoc test datasets. Our method starts, instead, from a compliant graph and uses an abstract rewriting system to systematically introduce schema violations while ensuring provable coverage of the constraints in the schema. We apply the framework to several repair methods, including those based on large language models, and assess their performance across multiple metrics. Results show that our framework effectively differentiates the performance of different methods across various constraints, highlighting the importance of fine-grained, systematic tools for graph repair.

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