发表机构
Alibaba Group; Zhejiang University（阿里巴巴集团; 浙江大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
SkillZip Pro 是面向自进化智能体的渐进式加载技能的执行感知压缩器，可高效降低技能 bundle 的 token 成本且不损失性能，还能保留路由和公共入口。
AI 中文摘要
生产环境中智能体的技能是目录 bundle，而非孤立的提示词：激活时仅加载根目录，引用、模式、脚本、资产及嵌套子技能仅在执行路径需要时才加载。仅压缩根目录会忽略大部分部署成本，还可能将分支特定细节移入始终加载的上下文；而扁平化操作则会破坏渐进式加载的边界。我们提出了\textit{SkillZip Pro}（\textit{method}），这是一种针对完整渐进式加载技能 bundle 的无评估压缩器，它不改变智能体框架，输出普通目录。该方法结合了两项保障措施：其一，它跨文件压缩，当根目录或声明的环境契约已提供某引用或子技能的内容时，将其移除；其二，它保留路由，重写后每个必需文件和可直接调用的入口仍可访问。用户可沿两个独立轴配置\textit{SkillZip Pro}：“一次性”模式重建完整 bundle，“持续”模式复用状态并在每次进化补丁后应用“写入时压缩”；“持久”压缩重写已部署的 bundle 以减少存储和运行时上下文，“瞬态”压缩保持该 bundle 字节级一致，仅构建任务特定视图，在构建成本后仅减少每次运行的上下文。入口契约标记私有、公共及条件资源，多入口审计可保留独立的公共子技能。在我们的工业多轮框架评估的生产内容审核技能上，\textit{SkillZip Pro} 移除了技能 bundle 38% 的 token 和端到端每次运行 10.4% 的 token，且无质量损失；而未受保护的 71% 配置因单侧误报最多损失 26 个准确率点。在多入口 bundle 上，\textit{SkillZip Pro} 可高效降低 token 成本，同时近乎完美地保留每条路由和公共入口。
英文摘要
Production agent skills are directory bundles, not isolated prompts. The root is loaded at activation; references, schemas, scripts, assets, and nested subskills are loaded only when an execution path needs them. Compressing only the root misses most deployment cost and may move branch-specific details into the always-loaded context. Flattening instead destroys progressive-loading boundaries. We introduce \method, an evaluation-free compressor for complete, progressively loaded skill bundles. It leaves the agent harness unchanged and emits an ordinary directory. The method combines two safeguards. First, it compresses \emph{across files}, removing content from a reference or subskill when the root or a declared environment contract already provides it. Second, it preserves routing, so every required file and directly callable entry remains reachable after rewriting. Users can configure \method along two independent axes. \emph{One-Shot} mode rebuilds the full bundle; \emph{Continual} mode reuses state and applies Zip-on-Write after each evolution patch. \emph{Persistent} compression rewrites the shipped bundle to reduce storage and runtime context. \emph{Transient} compression keeps that bundle byte-identical and builds a task-specific view, reducing only per-run context after build cost. Entry contracts mark private, public, and conditional resources; a multi-entry audit preserves standalone public subskills. On a production content-moderation skill evaluated by our industrial multi-round harness, \method removes \hl{38\%} of skill bundle tokens and \hl{10.4\%} of end-to-end per-run tokens with no quality loss, while an unprotected 71\% configuration loses up to 26 accuracy points to one-sided false positives. On a multi-entry bundle, \method effeciently reduces token cost while near-perfectly preserving every route and public entry.
