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This paper presents an automated pipeline for optimizing natural language skill descriptions in enterprise AI agents to resolve skill collisions, achieving performance matching manual tuning with a 32× speedup. Ablation studies show that a single LLM rewrite using error cases captures most improvements, while other design choices have minimal impact.