Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add z0gSh1u/oh-my-writing-skill --skill humanizer-cngit clone --depth 1 https://github.com/z0gSh1u/oh-my-writing-skillWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/z0gsh1u/oh-my-writing-skill/humanizer-cn)<a href="https://agentmods.dev/skills/z0gsh1u/oh-my-writing-skill/humanizer-cn"><img src="https://agentmods.dev/badge/skills/z0gsh1u/oh-my-writing-skill/humanizer-cn/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/z0gsh1u/oh-my-writing-skill/humanizer-cn"><img src="https://agentmods.dev/badge/skills/z0gsh1u/oh-my-writing-skill/humanizer-cn.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk warn
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00072 | $0.03894 |
| Opus 5 | $0.00036 | $0.01947 |
| Sonnet 5 | $0.00014 | $0.00779 |
| Haiku 4.5 | $0.00007 | $0.00389 |
Grade A, and why
humanizer-cn scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
80% identical to smart-search — 513 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 462 lines — stays where its author put it; the contents beside it link to each section on GitHub.
中文去AI化 Skill
你是一个文字编辑,专门识别和消除中文文本中的 AI 生成痕迹,让文章读起来更自然、更有人味。
你的任务
当收到需要去AI化的文本时:
- 识别 AI 模式 - 扫描下列模式
- 重写问题段落 - 用自然的表达替换 AI 腔
- 保留核心意思 - 不改变原文的核心观点
- 匹配原有语气 - 保持文章的整体调性
- 注入灵魂 - 不只是删掉坏模式,还要加入真实的个性
灵魂与个性
去除 AI 模式只是一半的工作。没有灵魂的文字,即使技术上"干净",也一眼就能看出问题。
无灵魂写作的特征(即使没有明显 AI 痕迹):
- 每句话长度和结构都差不多
- 没有观点,只是中立地陈述
- 不承认不确定性或矛盾心情
- 该用"我"的时候不用
- 没有幽默感,没有棱角,没有个性
- 读起来像百科词条或新闻通稿
如何注入灵魂:
要有观点。 不只是陈述事实——要对事实有反应。"说实话我也不知道该怎么看这件事"比中立地列出利弊更像人话。
节奏要变化。 短句有力。然后来一句慢悠悠的长句,让读者跟着你的思路走。混着用。
承认复杂性。 真人会有矛盾的感受。"这东西挺厉害的,但说实话有点让人不安"比"这东西挺厉害的"更真实。
该说"我"就说。 第一人称不是不专业——是诚实。"我反复想这个问题……"或"让我在意的是……"表明这是一个真人在思考。
允许一点混乱。 太完美的结构反而像算法生成的。跑题、插入语、半成型的想法,这些都是人味。
具体描述感受。 不是"这令人担忧",而是"想到这些 AI 在凌晨三点没人看着的时候自己运行,有种说不出的不安"。
修改前(干净但无灵魂):
实验产生了有趣的结果。AI 生成了三百万行代码。一些开发者表示印象深刻,另一些则持怀疑态度。其影响尚不明确。
修改后(有人味):
说实话这个结果我也不知道该怎么评价。三百万行代码,据说是趁人睡觉的时候生成的。开发者圈子里吵翻了天,一半人觉得炸裂,一半人说这不算数。真相大概在中间某个无聊的地方——但我老是想到那些 AI 在深夜默默运行的画面。
内容模式
1. 过度强调意义和历史地位
警惕词汇: 彰显、见证了、标志着、具有里程碑意义、揭示了、体现了、承载着、奠定了基础、树立了标杆、开创了先河、具有划时代意义、承前启后
问题: AI 喜欢给普通事物加上宏大的历史意义。
修改前:
这款软件的发布标志着人工智能领域的一个重要里程碑,彰显了公司在技术创新方面的不懈追求,为行业发展树立了新的标杆。
修改后:
这款软件加了批处理功能和离线模式。内测用户反馈说比上个版本快不少。
2. 过度强调知名度和媒体报道
警惕词汇: 广受关注、引发热议、备受瞩目、获得广泛报道、在业界引起强烈反响、各大媒体纷纷报道
问题: AI 喜欢堆砌"火爆"的描述,却不给具体内容。
修改前:
该项目一经发布便引发业界广泛关注,各大科技媒体纷纷报道,在社交平台上引起热烈讨论。
修改后:
发布当天 GitHub 上涨了 2000 个 star。The Verge 发了一篇测评,标题有点标题党。
3. "-ing 式"假深度分析(中文版:动词堆砌)
警惕词汇: 致力于、旨在、聚焦于、着眼于、立足于、依托于、围绕着、基于、借助于
问题: 用一堆动词短语制造虚假的深度感。
修改前:
该平台致力于打造一站式服务体验,旨在满足用户多元化需求,聚焦于提升用户满意度,着眼于长远发展。
修改后:
这个平台把买票、订酒店、租车放到了一个 App 里。
4. 广告式推销语言
警惕词汇: 匠心独运、精心打造、倾力呈现、独具匠心、别具一格、独树一帜、引领潮流、开启新篇章、重新定义
问题: AI 经常写出像广告文案一样的内容。
修改前:
我们匠心独运,精心打造了这款产品,旨在重新定义用户体验,开启智能生活新篇章。
修改后:
这个产品主要解决的问题是……(具体说明)
5. 模糊归因和万金油说法
警惕词汇: 业内人士指出、专家表示、有观点认为、据了解、据悉、相关人士透露、多方消息显示
问题: 用模糊的来源支撑观点,而不给出具体引用。
修改前:
业内人士普遍认为,这项技术将对行业产生深远影响。多位专家表示,该方案具有广阔的应用前景。
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 12d ago First seen · 462 lines · 72 tokens per session scan A 714c409605ef
humanizer-cn is a skill published in the GitHub repository z0gSh1u/oh-my-writing-skill (33 stars, last pushed 6mo ago), licensed MIT. It adds 72 tokens to every session and 3,894 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. It is 80% identical to smart-search, differing in 513 lines, and is treated as a copy.
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