Deploying LLM-powered agents in enterprise scenarios such as cloud technical support demands high-quality, domain-specific skills. However, existing skill creators lack domain grounding, producing skills poorly aligned with real-world task requirements. Moreover, once deployed, there is no systematic mechanism to trace execution failures back to skill deficiencies and drive targeted refinements, leaving skill quality stagnant despite accumulating operational evidence. We introduce SkillForge, a self-evolving framework that closes an end-to-end creation-evaluation-refinement loop. To produce well-aligned initial skills, a Domain-Contextualized Skill Creator grounds skill synthesis in knowledge bases and historical support tickets. To enable continuous self-optimization, a three-stage pipeline -- Failure Analyzer, Skill Diagnostician, and Skill Optimizer -- automatically diagnoses execution failures in batch, pinpoints the underlying skill deficiencies, and rewrites the skill to eliminate them. This cycle runs iteratively, allowing skills to self-improve with every round of deployment feedback. Evaluated on five real-world cloud support scenarios spanning 1,883 tickets and 3,737 tasks, experiments show that: (1) the Domain-Contextualized Skill Creator produces substantially better initial skills than the generic skill creator, as measured by consistency with expert-authored reference responses from historical tickets; and (2) the self-evolution loop progressively improves skill quality from diverse starting points (including expert-authored, domain-created, and generic skills) across successive rounds, demonstrating that automated evolution can surpass manually curated expert knowledge.

翻译：在云技术支持等企业场景中部署基于大语言模型的智能体需要高质量、领域特定的技能。然而，现有的技能创建方法缺乏领域根基，生成的技能与真实任务需求之间存在严重偏差。此外，技能部署后缺乏系统性机制将执行失败追溯至技能缺陷并驱动针对性优化，导致尽管积累了大量运行证据，技能质量却停滞不前。本文提出SkillForge——一种构建了端到端"创建-评估-优化"闭环的自我进化框架。为生成符合需求的高质量初始技能，领域情境化技能创建器将技能合成过程扎根于知识库和历史工单。为实现持续自我优化，三阶段流水线（故障分析器、技能诊断器和技能优化器）可自动批量诊断执行故障、精确定位潜在缺陷并重写技能以消除缺陷。该循环迭代运行，使技能能随着每次部署反馈实现自我改进。在涵盖1,883个工单和3,737个任务的五个真实云支持场景中进行的实验表明：(1)相较于通用技能创建器，领域情境化技能创建器生成的初始技能与专家基于历史工单撰写的参考回复一致性更优；(2)自我进化循环能从不同初始起点（包括专家撰写、领域创建和通用技能）出发，通过多轮迭代逐步提升技能质量，证明自动化进化可超越人工精修的专家知识。