SkillZip Pro: Execution-Aware Dynamic Compression of Progressively Loaded Skills for Self-Evolving Agents

Self-evolving agents accumulate useful procedures, but also repeat the same rules across references and subskills. Compressing these skills as flat prompts misses a crucial distinction: the agent stores a whole directory but loads only the files needed for a task. A smaller directory can therefore increase execution context or leave a required subskill inaccessible. We introduce \method, an evaluation-free compiler that compresses content and its loading structure together. It shares repeated instructions within the branches that use them, removes obligations already supplied by declared context, and defers explicitly guarded sections. Typed removal evidence and an independent audit of the emitted files preserve routing, interfaces, and standalone entries. The output uses ordinary files without changing the agent harness. One-Shot and Continual schedules support both rewritten bundles and source-preserving execution views. Across three task suites, \method saves 19.7% of shipped and 14.3% of mean-run tokens while preserving every declared route and meeting a five-point quality non-inferiority criterion. On a production moderation service, \method shows 38.1% skill token and 10.4% end-to-end runtime token savings, with nearly no performance drop. blueCode available at: https://anonymous.4open.science/r/skillzip-pro-ICLR