Internalized Skills Need Query-Adaptive Control

Skill internalization enables LLM agents to reuse procedural knowledge through compact parameter updates, reducing the context overhead of skill use. However, internalizing a skill does not determine how strongly it should be applied to a given query: the same intervention can correct errors on some queries while disrupting answers the base model would otherwise get right. To this end, we propose Query-Adaptive Skill Gating (QASG), a lightweight framework that regulates skill contribution on a per-query basis. QASG couples two complementary signals: predictive uncertainty from self-reflection gauges the model's need for assistance, while adapter sensitivity quantifies the skill's response relative to the base computation. Together, these signals allow the same skill to provide different levels of support across queries, helping uncertain predictions while limiting unnecessary intervention. Extensive evaluations across five benchmarks show that QASG outperforms the best fixed-strength baseline by 8.1–8.4 points and achieves the highest average accuracy among the evaluated methods, including TextSkill, LatentSkill, and other adaptive gating methods. These results highlight the importance of query-adaptive strength control for effective use of internalized skills.