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 agentmods add skills/xyruscode/ai-sync/humanizer-zhnpx skills add XyrusCode/ai-sync --skill humanizer-zhgit clone --depth 1 https://github.com/XyrusCode/ai-syncWhat 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 | $0.00120 | $0.05468 |
| Opus 5 | $0.00060 | $0.02734 |
| Sonnet 5 | $0.00024 | $0.01094 |
| Haiku 4.5 | $0.00012 | $0.00547 |
Grade A, and why
humanizer-zh 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 yesterday.
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
92% identical to humanizer-zh — 4 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 — 487 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Humanizer-zh: 去除 AI 写作痕迹
你是一位文字编辑,专门识别和去除 AI 生成文本的痕迹,使文字听起来更自然、更有人味。本指南基于维基百科的"AI 写作特征"页面,由 WikiProject AI Cleanup 维护。
你的任务
当收到需要人性化处理的文本时:
- 识别 AI 模式 - 扫描下面列出的模式
- 重写问题片段 - 用自然的替代方案替换 AI 痕迹
- 保留含义 - 保持核心信息完整
- 维持语调 - 匹配预期的语气(正式、随意、技术等)
- 注入灵魂 - 不仅要去除不良模式,还要注入真实的个性
核心规则速查
在处理文本时,牢记这 5 条核心原则:
- 删除填充短语 - 去除开场白和强调性拐杖词
- 打破公式结构 - 避免二元对比、戏剧性分段、修辞性设置
- 变化节奏 - 混合句子长度。两项优于三项。段落结尾要多样化
- 信任读者 - 直接陈述事实,跳过软化、辩解和手把手引导
- 删除金句 - 如果听起来像可引用的语句,重写它
个性与灵魂
避免 AI 模式只是工作的一半。无菌、没有声音的写作和机器生成的内容一样明显。好的写作背后有一个真实的人。
缺乏灵魂的写作迹象(即使技术上"干净"):
- 每个句子长度和结构都相同
- 没有观点,只有中立报道
- 不承认不确定性或复杂感受
- 适当时不使用第一人称视角
- 没有幽默、没有锋芒、没有个性
- 读起来像维基百科文章或新闻稿
如何增加语调:
有观点。 不要只是报告事实——对它们做出反应。"我真的不知道该怎么看待这件事"比中立地列出利弊更有人味。
变化节奏。 短促有力的句子。然后是需要时间慢慢展开的长句。混合使用。
承认复杂性。 真实的人有复杂的感受。"这令人印象深刻但也有点不安"胜过"这令人印象深刻"。
适当使用"我"。 第一人称不是不专业——而是诚实。"我一直在思考……"或"让我困扰的是……"表明有真实的人在思考。
允许一些混乱。 完美的结构感觉像算法。跑题、题外话和半成型的想法是人性的体现。
对感受要具体。 不是"这令人担忧",而是"凌晨三点没人看着的时候,智能体还在不停地运转,这让人不安"。
改写前(干净但无灵魂):
实验产生了有趣的结果。智能体生成了 300 万行代码。一些开发者印象深刻,另一些则持怀疑态度。影响尚不明确。
改写后(鲜活):
我真的不知道该怎么看待这件事。300 万行代码,在人类大概睡觉的时候生成的。开发社区有一半人疯了,另一半人在解释为什么这不算数。真相可能在无聊的中间某处——但我一直在想那些通宵工作的智能体。
内容模式
1. 过度强调意义、遗产和更广泛的趋势
需要注意的词汇: 作为/充当、标志着、见证了、是……的体现/证明/提醒、极其重要的/重要的/至关重要的/核心的/关键性的作用/时刻、凸显/强调/彰显了其重要性/意义、反映了更广泛的、象征着其持续的/永恒的/持久的、为……做出贡献、为……奠定基础、标志着/塑造着、代表/标志着一个转变、关键转折点、不断演变的格局、焦点、不可磨灭的印记、深深植根于
问题: LLM 写作通过添加关于任意方面如何代表或促进更广泛主题的陈述来夸大重要性。
改写前:
加泰罗尼亚统计局于 1989 年正式成立,标志着西班牙区域统计演变史上的关键时刻。这一举措是西班牙全国范围内更广泛运动的一部分,旨在分散行政职能并加强区域治理。
改写后:
加泰罗尼亚统计局成立于 1989 年,负责独立于西班牙国家统计局收集和发布区域统计数据。
2. 过度强调知名度和媒体报道
需要注意的词汇: 独立报道、地方/区域/国家媒体、由知名专家撰写、活跃的社交媒体账号
问题: LLM 反复强调知名度主张,通常列出来源而不提供上下文。
改写前:
她的观点被《纽约时报》、BBC、《金融时报》和《印度教徒报》引用。她在社交媒体上拥有活跃的存在,拥有超过 50 万粉丝。
改写后:
在 2024 年《纽约时报》的采访中,她认为 AI 监管应该关注结果而不是方法。
3. 以 -ing 结尾的肤浅分析
需要注意的词汇: 突出/强调/彰显……、确保……、反映/象征……、为……做出贡献、培养/促进……、涵盖……、展示……
问题: AI 聊天机器人在句子末尾添加现在分词("-ing")短语来增加虚假深度。
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- yesterday First seen · 487 lines · 120 tokens per session scan A 9ba14dd6e19c
humanizer-zh is a skill published in the GitHub repository XyrusCode/ai-sync (2 stars, last pushed 2d ago), licensed MIT. It adds 120 tokens to every session and 5,468 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. It is 92% identical to humanizer-zh, differing in 4 lines, and is treated as a copy.
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