SkillBrain: A Brain-Inspired Memory Substrate for Continual Skill Evolution

Large language model agents can continually improve through external Skills while keeping model parameters frozen. However, existing Skill Evolution methods typically treat the current Skill itself as the persistent state across rounds: new experience is repeatedly compressed back into the same executable artifact, entangling reusable task knowledge with model- and Harness-specific realization and making local performance gains difficult to equate with genuine continual knowledge accumulation. Our central view is that **the object of continual evolution should not be the Skill itself, but a persistent Memory that can preserve, reorganize, and reuse experience; Skills should instead be generated from Memory as execution-conditioned interfaces.** Based on this view, we propose **SkillBrain**, a Memory-Centric framework inspired by complementary learning systems. SkillBrain rapidly stores concrete experience in episodic memory, gradually consolidates reusable knowledge into semantic memory, and dynamically generates Skills for the current Executor and Harness. Experiments show strong performance on standard Skill Evolution benchmarks, positive forward and backward transfer on sequential tasks, and low forgetting on fixed per-topic probes. A separate cross-executor analysis shows reuse of experience generated by another model, while repeated Harness changes expose limitations in interface adaptation. These results support a shift from **evolving skills** to **evolving memory** for continual Skill Evolution.