做 LLM 智能体系统或工具链的开发者会发现，技能生成才是当前瓶颈——这个基准直接暴露了从仓库和文档生成可执行技能的难点，值得用来检验自己的管线。
SkillGenBench 是一个专门评估 LLM 智能体技能生成能力的基准测试。现有基准主要测试智能体使用已有技能或解决下游任务的能力，但忽略了技能生成本身。该基准覆盖两种生成模式：任务条件生成（根据任务生成特定技能）和任务无关生成（预先提炼可复用的技能库），并包含两种来源：代码仓库和长文档。实验显示不同方法性能差异大，可复用技能蒸馏尤为困难，且从仓库和文档生成技能时失败模式不同。SkillGenBench 为研究智能体系统中的技能生成提供了可复现的测试平台。
SkillGenBench: Benchmarking Skill Generation Pipelines for LLM Agents
As LLM agents are increasingly built around reusable skills, a central challenge is no longer only whether agents can use provided skills, but whether they can generate correct, reusable, and executable skills from repositories and documents. Existing benchmarks primarily evaluate the efficacy of given skills or the ability of agents to solve downstream tasks from raw context, but they do not isolate skill generation itself as the object of study. We introduce SkillGenBench, a benchmark for evaluating skill generation pipelines under a unified and controlled protocol. In SkillGenBench, a generator receives raw corpora and produces standardized skill artifacts, which are then executed under fixed harnesses and assessed with unified evaluation procedures. The benchmark covers two generation regimes: task-conditioned generation, where a task-specific skill is synthesized after the task is revealed, and task-agnostic generation, where a reusable skill library must be distilled before downstream tasks are known. It also spans two complementary procedural sources: repository-grounded instances, where procedures are distributed across code, configuration, and scripts, and document-grounded instances, where procedures and constraints must be distilled from long-form text. We provide standardized task specifications, pinned environments, and evaluation protocols centered on deterministic execution-based checks, supplemented by auxiliary signals for diagnosis. Experiments across a range of skill-generation methods and backbones show substantial performance variation, highlight the difficulty of reusable skill distillation, and reveal distinct failure modes in skill generation from software repositories versus long-form documents. SkillGenBench establishes a reproducible testbed for studying skill generation as an independent research problem in agent systems.