Does creating skills inside the agent loop eliminate mismatches?

Most skill-creation approaches treat skills as isolated, static artifacts authored in a separate pass — generated offline, then handed to an agent that uses them in a different context. MUSE-Autoskill instead tightly couples creation to execution through a built-in skill_create tool invoked from within the runtime loop, so a skill is created on demand inside the same reasoning that needs it. The paper names the problem this solves: the creation-usage mismatch.

This matters because skills authored out-of-loop encode the author's assumptions about a task the agent has not yet faced, and the agent that later applies them lacks the situated context that motivated each step. When creation happens inside the loop, the skill is grounded in the exact trajectory, tools, and failure that prompted it — and the framework can immediately validate it through unit tests and runtime feedback rather than trusting a detached author. On SkillsBench, automatically generated in-loop skills reach 87.94% on their tasks and transfer to other agents with minimal accuracy loss.

The counterpoint is that in-loop creation risks proliferation — an agent that mints a skill for every situation accumulates redundant, narrow artifacts. MUSE addresses this with the rest of its lifecycle (memory, management, evaluation, refinement) that organizes and prunes, so creation alone is not the whole story. Therefore the durable insight is architectural: skills should be live infrastructure produced where they are consumed, not disposable outputs of a separate authoring stage — which is what makes them testable and transferable assets rather than one-off generations.