Repairing flawed domain models remains a critical challenge in AI planning, with few effective techniques available. We propose a novel approach for repairing totally ordered hierarchical task network (TO-HTN) models with missing actions, guided by a plan that must be valid for the repaired model. This problem has only one previously documented approach, which relies on complex re-encoding that's solved via TO-HTN planning. In contrast, our approach translates the repair task into a context-free grammar repair problem and leverages a large language model (LLM) to identify and insert relevant actions directly, simplifying the repair process. We evaluate our approach on established benchmarks and demonstrate substantially improved results over the prior approach, achieving nearly three times the number of instances solved, and nearly solving all instances of domains in which the previous approach solved zero. Importantly, we mask all natural language hints, such as action names, forcing the LLM to simulate reasoning and planning, and mitigating the risk of data leakage from its training corpus.