让智能体从开放材料中自动构建可复用技能，无需人工编写。
SkillAlchemy: Open-World Agent Skill Creation
- 通过对比证据挖掘隐含需求，自动发现任务缺失信息
- 在87个任务上比无技能执行提升19.9个百分点，超最强基线8.6个百分点
- 适合需要自动化技能生成的开放世界应用开发者
智能体技能是可复用的过程性组件，在推理时扩展语言智能体的专用工作流、工具规范和领域行为。然而，可靠技能的创建仍严重依赖人工编写、模型先验或执行轨迹，这些资源在陌生任务中往往不可得，因此亟需从开放世界材料中创建技能。本文研究开放世界技能创建：给定一个不完整的技能说明和源访问规范，创建者需发现说明中遗漏的行为相关要求，并判断每个来源程序的适用范围。我们提出SkillAlchemy，一种以准入为中心的源引导型技能创建框架。该框架通过对比证据识别隐含需求，基于证据支持的范围接纳候选程序，并将接纳内容编译为语法引导的技能包。在SkillsBench v1.1的87个任务上，实验表明SkillAlchemy相比无技能执行提升19.9个百分点，较最强自动化基线提升8.6个百分点，性能接近人工标注技能。
原文摘要 · Abstract (English)
Agent skills are reusable procedural artifacts that extend language agents with specialized workflows, tool conventions, and domain behaviors at inference time. However, creating reliable skills still depends largely on human authorship, model priors, or execution traces. These sources are often unavailable for unfamiliar tasks, suggesting the need to create skills from open-world materials. In this paper, we study open-world skill creation: given an underspecified skill brief and a source-access specification, a creator must discover behavior-relevant requirements omitted by the brief and determine how broadly each source-derived procedure is justified. We propose SkillAlchemy, an admission-centered framework for source-grounded skill creation. SkillAlchemy identifies implicit requirements through contrastive evidence, admits candidate procedures based on evidence-supported scope, and compiles the admitted content into a grammar-guided skill package. Extensive experiments across 87 SkillsBench v1.1 tasks demonstrate that SkillAlchemy improves pass rate over no-skill execution by 19.9 percentage points and the strongest automated baseline by 8.6 percentage points, while achieving performance comparable to human-curated skills.
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