SkillZip: Evaluation-Free Skill Compression for Self-Evolving Agents by Discovering Reusable Structure

TL;DR - SkillZip is an evaluation-free method that compresses the skill libraries accumulated by self-evolving LLM agents by finding the shortest faithful structural explanation of each skill. It matters because bloated, redundant skills are expensive to inject into context and hard to maintain, and existing compression either ignores skill structure or requires costly rollout-based evaluation.

• Frames skills as structured objects (name/description triggers, workflow, tool and output contracts, obligations) rather than flat text, so generic prompt compression is deemed ill-suited.
• Core principle is "explain once, reference many": state a repeated rule once at its applicable scope, factor repeated action sequences into shared procedures, and retain only differences as explicit exceptions.
• Formalized as a typed minimum description-length objective over a skill contract plus residual, under a hard coverage constraint for every extracted trigger, workflow edge, tool requirement, obligation, and output field — which preserves rare rules by construction and yields simple sharing thresholds.
• Offers a one-shot mode (single structured extraction call plus deterministic optimization) and a continual "Zip-on-Write" mode that folds in each self-evolution patch without replaying tasks or reparsing history; the abstract claims gains in compression, generalizability, and cost overhead but reports no specific numbers.