SKILL²: Continually improving how agents learn skills from experience

Skills enable language model agents to turn execution experience into procedural knowledge that can support future tasks. However, existing skill generation methods largely focus on producing and refining individual skill artifacts, while the procedure used to generate these skills is typically kept fixed. As a result, improvements in generated skills do not systematically accumulate into a better process for generating future skills. We introduce SKILL² (Skill-Squared), which instead treats the skill generation procedure itself as an optimization target. SKILL² continually improves this procedure, termed the MetaSkill, and uses the evolved MetaSkill to induce new skills from trajectories. This allows improvements to accumulate in the skill generation procedure rather than only in individual skill artifacts. Across four benchmarks and two executor scales, SKILL² outperforms the strongest skill generation baseline by an absolute 5.00% on average. Controlled experiments further show that evolved MetaSkills produce better skills when given the same trajectories and retain their advantage when applied to previously unseen trajectories, while reducing the computational cost of adaptation. These results demonstrate the value of improving the procedure that generates skills, rather than optimizing each skill in isolation.