Most review processes for agent skills still treat them as documents. They check structure, style, and security. They do not answer the deployment Liftquestion: does this skill actually help a live agent complete real tasks under the same model, sandbox, and grading policy?

ACES (Agentic Continuous Evaluation of Skills) answers that question with paired live trials. For each task the system runs the agent once with the target skill and once without it. Trajectories are normalized, graded on multiple runtime metrics, and compared. The central measure is Skill Lift: the measured improvement the skill produces under fixed conditions.

A new paradigm treats agent skills not as brittle prompts or expensive fine-tuning targets, but as an optimizable external text state — exactly like model parameters in deep learning.

SkillOpt introduces a systematic optimizer for natural-language skills: bounded edits, validation gates, meta-updates, and a clean separation between skill storage and execution. The result is compact, transferable, interpretable procedural rules that improve agent performance with zero additional inference cost.