COLLEAGUE.SKILL：通过专家知识蒸馏实现自动化AI技能生成

摘要

LLM代理正被期望不仅完成孤立的任务，还能承载人类专业知识、判断和互动风格的有限表征。构建这种基于个人的代理仍然困难，因为与个人或角色相关的可操作知识通常嵌入在异质痕迹中，而非作为清晰的指令书写。现有的记忆和角色系统捕捉了这些证据的片段，而技能框架提供了便携的封装格式；然而，目前尚无端到端的工作流可将这些痕迹提炼为可检查、可修正且代理可用的技能。我们提出了一种自动化的痕迹到技能的蒸馏系统，通过专家知识蒸馏生成基于个人的AI技能。给定目标个人或角色的材料，COLLEAGUE.SKILL生成一个带版本控制的技能包，包含两个协调轨道：用于实践、思维模型和决策启发法的能力轨道，以及用于沟通风格、互动规则和修正历史的受限行为轨道。该技能包可被检查、调用、通过自然语言反馈更新、回滚、跨代理主机安装，并可选择性地为受控分发做准备。我们描述了该开源系统中实现的工件契约、生成工作流、修正生命周期、部署界面和领域预设。截至撰写本文时，公共仓库拥有约18.5k GitHub星标；图库列出了来自165位贡献者的215个技能，且所列技能卡累计超过10万星标。该系统展示了基于个人的技能如何被表示为便携、可修正的包，而非不透明的提示或隐藏的记忆。

English

LLM agents are increasingly expected not only to complete isolated tasks, but also to carry bounded representations of human expertise, judgment, and interaction style. Building such person-grounded agents remains difficult because actionable knowledge associated with a person or role is usually embedded in heterogeneous traces rather than written as clean instructions. Existing memory and persona systems capture fragments of this evidence, while skill frameworks provide portable packaging formats; however, there is no end-to-end workflow for distilling these traces into inspectable, correctable, and agent-usable skills. We present an automated trace-to-skill distillation system for generating person-grounded AI skills via expert knowledge distillation. Given materials from a target person or role, COLLEAGUE.SKILL produces a versioned skill package with two coordinated tracks: a capability track for practices, mental models, and decision heuristics, and a bounded behavior track for communication style, interaction rules, and correction history. The package can be inspected, invoked, updated through natural-language feedback, rolled back, installed across agent hosts, and optionally prepared for controlled distribution. We describe the artifact contract, generation workflow, correction lifecycle, deployment surface, and domain presets implemented in the open-source system. At the time of writing, the public repository has approximately 18.5k GitHub stars; the gallery lists 215 skills from 165 contributors and more than 100k cumulative stars across listed skill cards. The system illustrates how person-grounded skills can be represented as portable, correctable packages rather than opaque prompts or hidden memories.