From Weights to Words: Meta-Learning Skill Writers with Parameter–Text Adaptation

LLM agents increasingly store procedural knowledge in natural-language skills, written by a Writer model and executed by a Reader model. Most methods optimize a skill or its construction prompt for a fixed Reader, without learning a Writer initialization whose construction process can rapidly adapt to new task–Reader pairs. Yet a skill is only as useful as the Reader that executes it. We introduce MetaSkill, which meta-learns a Writer initialization for coupled parameter–text adaptation. Reader feedback temporarily specializes the Writer, and text refinement externalizes that specialization into a persistent skill. Across language, reasoning, and interactive benchmarks, this adaptation is far more effective from the meta-learned initialization than from the original or a multitask-trained Writer, and both stages contribute. MetaSkill also matches the strongest text-space optimizer on non-interactive tasks at a lower token cost and adapts to unseen Reader families. Skills shaped for one Reader serve others less well, tying skill utility to the task–Reader pair. These results show that meta-learning an adaptable Writer enables effective and token-efficient skill acquisition for new tasks and Readers.