Compressing Agent Skills Without Task Evaluation

Summary & Methodology Analysis

SkillZip addresses the accumulation of redundant textual instructions in self-evolving agents, which can lead to high maintenance costs and increased latency during invocation. Rather than relying on task-based evaluation or behavioral verifiers, the method operates by scanning agent instructions to identify front matter, headings, and stable block identifiers. It then extracts a structured typed contract that includes interface definitions, tool protocols, output schemas, and cited evidence, allowing the system to treat the instruction set as a formal data structure rather than opaque text. By identifying equivalent requirements and repeated rules within these contracts, the method identifies reuse candidates that can be safely consolidated.

The core algorithm utilizes a minimum description length objective to select the shortest possible explanation for the instruction set while ensuring all original requirements remain covered. This involves re-rendering the optimized contract and optionally performing structural audits to ensure no critical components were lost during compression. To handle ongoing updates, the system uses a local state-aware mechanism to absorb, refine, and refactor new patches into the existing contract, supplemented by periodic global repacking to maintain efficiency.

Despite its effectiveness, the approach faces limitations regarding semantic reliability. Because the compression relies entirely on the structural parser, the process is subject to semantic uncertainty where the parser may fail to correctly interpret complex instruction patterns. Additionally, the system provides a hard preservation guarantee only for the extracted contract elements. Any source spans that the parser cannot definitively map are kept verbatim, meaning those specific sections cannot be compressed by the system.