内容来源：https://www.technologyreview.com/2026/04/20/1136149/chinese-tech-workers-ai-colleagues/

内容总结：

近日，一个名为“同事技能”的GitHub项目在中国科技圈引发热议。该项目声称可将同事的工作技能与性格特质“提炼”成可被AI代理复用的手册，虽最初仅为恶搞，却意外戳中了许多科技从业者的焦虑。

当前，中国部分科技公司正鼓励员工使用OpenClaw、Claude Code等AI代理工具，将自身工作流程文档化以实现自动化。而“同事技能”这类工具的出现，进一步加剧了员工对自身价值被“模块化”的担忧。用户只需输入同事基本信息，该工具即可从钉钉、飞书等办公平台导入聊天记录与文件，自动生成涵盖工作职责甚至个人习惯的AI代理指南。

项目创建者、上海人工智能实验室工程师周天翼向媒体表示，其灵感来源于近期的AI裁员潮与企业要求员工“自我自动化”的趋势。尽管以玩笑形式问世，该工具迅速引发了关于AI时代劳动者尊严与独特性的广泛讨论。

在实际体验中，有科技员工发现生成的AI代理能高度还原前同事的代码调试风格甚至标点习惯，令人感到“诡异不适”。尽管当前AI代理在复杂业务场景中作用有限，但企业推动此类实践的背后，是获取员工工作流程数据、探索岗位标准化可能性的深层动机。

对此，有员工以“黑色幽默”表达不安，也有从业者发起反击。4月4日，北京一名AI产品经理在GitHub发布了“反提炼技能”工具，通过将工作流程改写为泛化、无效的表述，干扰AI代理的生成过程。开发者表示，此举旨在唤起对劳动异化与员工权益保护的关注，并指出此类技术可能涉及工作聊天记录与个人特质的知识产权模糊地带。

尽管多数员工认为AI尚无法完全替代人力，但“价值被稀释”的危机感已悄然蔓延。如何在技术浪潮中守护人的独特性与尊严，正成为中国科技职场面临的新命题。

中文翻译：

中国科技从业者开始训练自己的AI分身——并开始反击。一个声称能将同事"克隆"成可复用AI技能的GitHub项目病毒式传播，迫使中国科技从业者直面更深层的焦虑。

中国的科技从业者正被老板要求训练替代自己的AI智能体——这在一向热衷新技术的早期使用者中引发了集体反思。本月初，一个名为"同事技能"的GitHub项目在中国社交媒体爆火。该项目声称可将同事的技能和个性特征"提炼"成AI智能体。虽然该项目本是个恶搞创作，却精准戳中了科技从业者的神经。多位从业者向《麻省理工科技评论》透露，他们的老板正鼓励员工记录工作流程，以便通过OpenClaw或Claude Code等AI智能体工具实现特定任务自动化。

使用"同事技能"时，用户只需输入想要复制的同事姓名和基本资料，该工具便会自动从飞书、钉钉等中国主流办公软件导入聊天记录和文件，生成描述该同事职责——甚至独特习惯——的可复用手册供AI智能体学习。

该项目创建者周天翼在上海人工智能实验室担任工程师。本周他告诉中国媒体《南方都市报》，这个项目源于对AI相关裁员现象以及企业日益要求员工自我自动化趋势的戏谑回应。他未回应进一步采访请求。

网友们在调侃"先自动化同事再自动化自己"的同时，"同事技能"的走红也引发了关于AI时代劳动者尊严与个体价值的广泛讨论。

27岁的上海科技从业者李安珀在社交媒体看到该项目后，尝试用它复制了一位前同事。短短几分钟，工具就生成了详细记录此人工作方式的文件。"效果惊人地好，"李安珀说，"甚至捕捉到了对方的小习惯，比如反应方式和标点使用特点。"现在她可以用AI智能体作为新"同事"来调试代码并即时回复。"既神奇又令人不适，"她坦言。

即便如此，用智能体替代同事可能成为常态。自OpenClaw风靡全国以来，中国企业主一直在推动科技从业者试用各类智能体。

尽管AI智能体已能操控电脑、阅读总结新闻、回复邮件、预订餐厅，但一线科技从业者表示，其在商业场景的实际效用仍然有限。要求员工像"同事技能"那样制作详细工作手册，正是弥合这种差距的途径之一。

研究AI与劳动关系的埃默里大学助理教授曹瀚成认为，企业推动员工创建此类工作蓝图并非单纯跟风。"企业不仅能积累工具使用经验，更能获取关于员工专业知识、工作流程和决策模式的丰富数据。这有助于识别哪些工作可标准化或系统化，哪些仍需人类判断。"

但对员工而言，制作智能体甚至其蓝图可能带来疏离感。一位因担心职业安全而要求匿名的软件工程师表示，用自身工作流程训练AI（非"同事技能"）时，感觉自己的工作被简化为可替换的模块。社交媒体上，劳动者用黑色幽默表达类似情绪。在Rednote平台，有用户写道："冰冷的告别可以转化为温热的代币"，戏称若先用"同事技能"将同事提炼成任务模块，自己或许能多存活一阵。

这股智能体创建风潮也催生了巧妙的反制措施。26岁的北京AI产品经理徐可可对"将人简化为技能"的理念感到不满，于4月4日在GitHub发布了"反提炼"技能。这个耗时约一小时开发的小工具，专门干扰为智能体创建工作流程的过程。用户可根据老板监督的严格程度选择轻度、中度或重度干扰模式，该工具会将材料改写成通用且无法操作的表述，从而生成低效的AI替代品。徐可可发布的项目视频迅速走红，全网获赞超500万。

徐可可告诉《麻省理工科技评论》，她从一开始就在关注"同事技能"现象，这促使她思考异化、赋权缺失及对劳动力的广泛影响。"我本想写评论文章，但觉得制作反击工具更实用。"拥有法学本科和硕士学位的她指出，这种趋势还引发法律问题。虽然企业可能主张工作聊天记录和办公电脑资料属于公司财产，但此类技能还能捕捉个性、语气和判断力等要素，使所有权界定变得模糊。她希望"同事技能"能推动更多关于如何在AI时代保护劳动者尊严与身份的讨论。"紧跟这些趋势很重要，这样我们（员工）才能参与塑造技术的使用方式。"她本人就是积极的AI使用者，在个人和工作设备上设置了七个OpenClaw智能体。

上海的李安珀表示，其公司尚未找到用AI工具替代真人员工的方法，主要因为这些工具仍不可靠且需要持续监督。"我不觉得工作会立即不保，但确实感到自身价值被贬低，却不知如何应对。"

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英文来源：

Chinese tech workers are starting to train their AI doubles–and pushing back
A viral GitHub project that claims to clone coworkers into a reusable AI skill is forcing Chinese tech workers to confront deeper fears.
Tech workers in China are being instructed by their bosses to train AI agents to replace them—and it’s prompting a wave of soul-searching among otherwise enthusiastic early adopters.
Earlier this month a GitHub project called Colleague Skill, which claimed workers could use it to “distill” their colleagues’ skills and personality traits and replicate them with an AI agent, went viral on Chinese social media. Though the project was created as a spoof, it struck a nerve among tech workers, a number of whom told MIT Technology Review that their bosses are encouraging them to document their workflows in order to automate specific tasks and processes using AI agent tools like OpenClaw or Claude Code.
To set up Colleague Skill, a user names the coworker whose tasks they want to replicate and adds basic profile details. The tool then automatically imports chat history and files from Lark and DingTalk, both popular workplace apps in China, and generates reusable manuals describing that coworker’s duties—and even their unique quirks—for an AI agent to replicate.
Colleague Skill was created by Tianyi Zhou, who works as an engineer at the Shanghai Artificial Intelligence Laboratory. Earlier this week he told Chinese outlet Southern Metropolis Daily that the project was started as a stunt, prompted by AI-related layoffs and by the growing tendency of companies to ask employees to automate themselves. He didn’t respond to requests for further comment.
Internet users have found humor in the idea behind the tool, joking about automating their coworkers before themselves. However, Colleague Skill’s virality has sparked a lot of debate about workers’ dignity and individuality in the age of AI.
After seeing Colleague Skill on social media, Amber Li, 27, a tech worker in Shanghai, used it to recreate a former coworker as a personal experiment. Within minutes, the tool created a file detailing how that person did their job. “It is surprisingly good,” Li says. “It even captures the person’s little quirks, like how they react and their punctuation habits.” With this skill, Li can use an AI agent as a new “coworker” that helps debug her code and replies instantly. It felt uncanny and uncomfortable, Li says.
Even so, replacing coworkers with agents could become a norm. Since OpenClaw became a national craze, bosses in China have been pushing tech workers to experiment with agents.
Although AI agents can take control of your computer, read and summarize news, reply to emails, and book restaurant reservations for you, tech workers on the ground say their utility has so far proven to be limited in business contexts. Asking employees to make manuals describing the minutiae of their day-to-day jobs the way Colleague Skill does is one way to help bridge that gap.
Hancheng Cao, an assistant professor at Emory University who studies AI and work, believes that companies have good reasons to push employees to create work blueprints like these, beyond simply following a trend. “Firms gain not only internal experience with the tools, but also richer data on employee know-how, workflows, and decision patterns. That helps companies see which parts of work can be standardized or codified into systems, and which still depend on human judgment,” he says.
To employees, though, making agents or even blueprints for them can feel strange and alienating. One software engineer, who spoke with MIT Technology Review anonymously because of concerns about their job security, trained an AI (not Colleague Skill) on their workflow and found that the process felt reductive—as if their work had been flattened into modules in a way that made them easier to replace. On social media, workers have turned to bleak humor to express similar feelings. In one comment on Rednote, a user wrote that “a cold farewell can be turned into warm tokens,” quipping that if they use Colleague Skill to distill their coworkers into tasks first, they themselves might survive a little longer.
The push for creating agents has also spurred clever countermeasures. Irritated by the idea of reducing a person to a skill, Koki Xu, 26 an AI product manager in Beijing, published an “anti-distillation” skill on GitHub on April 4. The tool, which took Xu about an hour to build, is designed to sabotage the process of creating workflows for agents. Users can choose between light, medium, and heavy sabotage modes depending on how closely their boss is observing the process, and the agent rewrites the material into generic, non-actionable language that would produce a less useful AI stand-in. A video Xu posted about the project went viral, drawing more than 5 million likes across platforms.
Xu told MIT Technology Review that she has been following the Colleague Skill trend from the start and that it has made her think about alienation, disempowerment, and broader implications for labor. “I originally wanted to write an op-ed, but decided it would be more useful to make something that pushes back against it,” she says.
Xu, who has undergraduate and master’s degrees in law, said the trend also raises legal questions. While a company may be able to argue that work chat histories and materials created on a work laptop are corporate property, a skill like this can also capture elements of personality, tone, and judgment, making ownership much less clear. She said she hopes Colleague Skill prompts more discussion about how to protect workers’ dignity and identity in the age of AI. “I believe it’s important to keep up with these trends so we (employees) can participate in shaping how they are used,” she says. Xu herself is an avid AI adopter, with seven OpenClaw agents set up across her personal and work devices.
Li, the tech worker in Shanghai, says her company has not yet found a way to replace actual workers with AI tools, largely because they remain unreliable and require constant supervision. “I don’t feel like my job is immediately at risk,” she says. “But I do feel that my value is being cheapened, and I don’t know what to do about it.”
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