Image-based AI assistants are now deployed at production scale on e-commerce platforms, where a single uploaded image can trigger fundamentally different user intents: product search, style recommendation, visual encyclopedia, or utility tool calls, each demanding its own response format, tool invocation, and domain knowledge. Without per-intent behavioral constraints, LLM-based systems conflate these heterogeneous modes and fall short of domain quality standards, while the breadth and dynamism of the intent space render manual engineering infeasible. To address this, we present SkillChain, which closes the production feedback loop on Skill evolution, automating the lifecycle of Skills through three stages: Skill Creator for bootstrapping from task specs and trajectories, Route Optimizer for routing alignment, and Body Refiner for iterative Skill Body refinement via dual-path LLM-Judge evaluation. Deployed on a production-scale e-commerce image assistant, SkillChain substantially improves aggregate response quality, with the strongest gains on structural compliance and content quality; a one-week online A/B experiment further confirms significant gains in user engagement, content consumption, and long-term retention.

翻译：基于图像的AI助手已大规模部署于电商平台。用户上传单张图像可能触发截然不同的意图：商品搜索、风格推荐、视觉百科或实用工具调用，每种意图都对应不同的响应格式、工具调用逻辑及领域知识。若缺乏按意图设定的行为约束，基于大语言模型的系统会混淆这些异构模式，难以达到领域质量标准；而意图空间的广度与动态性又使人工工程化治理不可行。为此，我们提出SkillChain，通过技能演化闭环机制实现生产反馈的自动化。该框架包含三个模块：面向任务规范与轨迹初始化的技能创建器、负责路由对齐的路径优化器、以及通过双路大语言模型裁判评估进行迭代技能体精炼的体态优化器。SkillChain在工业级电商图像助手中部署后，显著提升了整体响应质量，其中结构合规性与内容质量改进最为突出；为期一周的在线A/B实验进一步证实，用户在参与度、内容消费量及长期留存率方面均获得稳步提升。