Agent Skill is already one of the most widely accepted practices in AI Agent engineering: encapsulating repeatable tasks into capability packages that are discoverable, callable, and injectable into context.
This is certainly valuable. Diagnosing bugs can be a skill. Reviewing code can be a skill. Generating slides can be a skill. Converting files can be a skill. Skills let an Agent enter a proven way of working faster, and they allow teams to distill experience into reusable work specifications.
But as the saying goes: “If all you have is a hammer, everything looks like a nail.” The same is true for skills: as soon as people find a useful practice, their first instinct is often “wrap it into a reusable skill.”
Attractor Guided Engineering (AGE) points out that if every AI engineering practice is turned into a skill, we lose sight of the most critical question in AI-dominated software engineering: how does the system remain controlled and convergent under continuous perturbation?
Skills solve the problem of capability invocation. AGE solves the problem of how domain structure is preserved during the long-term evolution of a repository, and how trajectory drift is suppressed.
