Expert-written natural language skills can improve tool-using agents, yet agent-authored skills perform 8--11 points worse than using no skill. While inference-time loops can repair individual skills per task, they leave the underlying generator's refinement capability unimproved. We study how to organize execution experience from intermediate skills into training states for an optimizer. We introduce WER(Write, Execute, and Refine), a multi-phase framework that trains a Skill Optimizer outside a frozen executor. The optimizer proposes skills, a frozen agent executes each repeatedly, and a programmatic verifier scores the outcomes. These scores serve as group-relative rewards and filter for informative, mixed-outcome instances. Matched successful and failed trajectories from these records form the next phase's refinement states, so the optimizer learns from the consequences of its earlier outputs. On BFCL v4 multi-turn and tau2-bench, WER improves average Pass@1 over the no-skill baseline by 7.80 and 6.06 points, respectively. Under an identical refinement workflow, it outperforms the same backbone without optimizer training by 10.02 and 12.50 points. Notably, our trained 4B optimizer achieves 76.63\% on BFCL v4, outperforming all evaluated off-the-shelf general-purpose LLMs in the same role on average. Our code is available at https://github.com/littlepkk/wer-training.

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