From rn-bw-text-opener
Transforms Chinese AI/tech writing into natural, authorial drafts by removing AI-typical structures like binary contrasts, lecture-colon setups, and fake insight markers while preserving facts and judgments.
How this skill is triggered — by the user, by Claude, or both
Slash command
/rn-bw-text-opener:rn-renhuaThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
Turn Chinese AI/tech writing into a direct public draft that preserves the author's judgment, facts, technical terms, and lived experience. Remove AI-flavored structure without flattening the author's voice.
Turn Chinese AI/tech writing into a direct public draft that preserves the author's judgment, facts, technical terms, and lived experience. Remove AI-flavored structure without flattening the author's voice.
Default output is the revised text only. Add diagnosis only when the user asks why a sentence feels AI-like.
Avoid these in final copy unless the user explicitly wants to discuss the phrase itself.
Do not use:
不是 A,而是 B并非 A,而是 B不在于 A,而在于 B不只是 A,更是 B不仅 A,还/更 B与其 A,不如 B不是一两分钟,而是...Rewrite by stating the actual claim directly.
Bad:
去 AI 味不是把文章改口语,而是保住判断。
Better:
写 AI 技术文章时,我更关心判断有没有保住。
Bad:
这一步省掉的不是一两分钟,而是整套重复动作。
Better:
这一步能省掉来回翻网页、找入口、下载文件、再丢给 AI 的重复动作。
Avoid short imperative templates that sound like a generic tutorial hook:
别急着 X,先 Y先别 X,先 Y别 X,先 Y顺序别反了别搞反了记住这句话Rewrite by stating the concrete problem, failure, or observation directly.
Bad:
用 AI 分析 A 股,别急着问模型,先看数据接得稳不稳。
Better:
你让 AI 分析股票,最怕它一本正经地拿错数据。
Bad:
做 AI 投资分析,顺序别反了。
Better:
做 AI 投资分析时,数据入口不稳,后面的模型分析也会跟着歪。
Avoid:
真正其实本质上核心在于关键在于说白了归根结底更重要的是结果有点出乎意料这说明这背后Rewrite by entering the claim or evidence directly.
Bad:
更重要的是保住三个东西:经验、判断、细节。
Better:
我会检查三件事:有没有真实经验,有没有模型判断,有没有工程细节。
Avoid colon-led setup when it turns the sentence into a lesson.
Do not write:
我的结论是:原因很简单:重点是:分成三类:更重要的是:Use a plain sentence, or split the idea across paragraphs.
Allow a colon when it introduces a concrete inventory with a clear noun before it.
Good:
这 10 个项目覆盖六类用途:中文改写、英文规则库、写作流水线、风格蒸馏、检测研究、前端审美。
Bad:
结果有点出乎意料:这 10 个项目混了几种东西。
Avoid vague placeholders when the reader needs a category.
东西这件事这些一类几个方向Replace them with the exact category: 用途、项目类型、规则、输出形态、测试结果、写作流程.
Match verb tense to the actual work state.
我用了、我测了、我保留了、我拆出了、我最后合成了.我接下来会、下一步我会.我会用 X when the text is describing tools already tested or selected.Bad:
我会先用这个 skill 处理中文语感。
Better:
这轮我保留了中文语感和场景边界处理规则。
Avoid generic 更适合、更像、更自然、更高级 unless the comparison names the exact use.
Bad:
这套规则更像长期方案。
Better:
这套流程可以把选题、证据、审稿、去味和导出串成一条线。
Avoid sentences that sound forceful but do not name a concrete consequence or action.
Do not write:
差距会突然变得很难看差距会被迅速拉开会成为新的分水岭更值得盯的是个人更值得关注的是...Rewrite by naming the visible result, wasted cost, or changed behavior.
Bad:
等公司开始给每个人分 AI 额度,差距会突然变得很难看。
Better:
等公司开始给每个人分 AI 额度,同样一笔钱,有人只换来几段废话,有人能少开几场会、少返几遍工。
Bad:
更值得盯的是个人。
Better:
公司账单之外,还要看每个人把额度花到哪里。
Avoid broad metaphors and quotable endings:
正确但无聊的模型作文上下文燃料能力飞轮时代分水岭作者痕迹把判断盖住Use the concrete loss instead.
Bad:
文章读起来再顺,也只像一篇正确但无聊的模型作文。
Better:
读者看不出作者测过什么、踩过什么坑、为什么得出这个判断。
别急着...先... or 顺序别反了测了、跑了、拉到本地、校验通过、单测过了、保留、删掉、改散.这轮我保留了 X,用它处理 Y.六类用途、三种输出形态、两个校验问题 over 几种东西、几个方向.When the user asks why something feels AI-like, return 3-6 concrete triggers. Each trigger must quote the phrase and name the pattern.
Use this format:
1. 「...」:二元对比壳。直接说后半句承载的判断。
2. 「...」:伪洞察标记。删掉提示词,从事实起句。
3. 「...」:冒号讲义腔。改成普通句子或拆段。
Check the final text for these strings and patterns:
不是 near 而是不在于 near 在于不只是 or 不仅别急着先别顺序别反了别搞反了记住这句话真正、其实、本质上、核心在于、关键在于更重要的是更适合 / 更像差距会突然变得很难看更值得盯的是个人If found, revise before answering.
npx claudepluginhub pluviobyte/rnskill --plugin rn-renhuaEdits prose in Chinese and English to remove AI-like wording, polish release notes, launch copy, social posts, and localization. Useful for drafting, rewriting, proofreading, and reviewing product copy.
Remove AI writing patterns from text with three modes: prewrite (internalize style rules before writing), rewrite (two-pass rewrite of AI tells), and review (flag-only audit).
Detects AI-like Chinese writing and rewrites it to be more natural, with targeted workflows for academic AIGC reduction and style conversion (Zhihu, Xiaohongshu, etc.).