Russian grammar correction models can improveon aggregate benchmarkswhile getting worse at specific grammar rules.We show this through per-rule evaluationon a diagnostic benchmark of 48 prescriptive rules:finetuning on synthetic data improves overall F_0.5while driving subordinate-clause comma accuracyfrom 14% to 1%.The suppression is invisible under corpus-level metricsand undetectable with existing coarse, corpus-specific tagsets;it is recoverable only when diagnosed at rule granularity.To enable this analysis,we develop a 98-category error taxonomygrounded in Rozental’s reference grammarand SyntErr, an open-source synthetic data generatorwhose per-rule distribution is an explicit parameter,designed to support arbitrary rule sets and languages.Finetuning eight open models (0.8B–12B)on 39K synthetic examplesyields up to 75.3 F_0.5,approaching frontier API modelswith models small enough to run on device.We release the taxonomy, generator,per-rule evaluation data, and all training artifacts.