chinese-text-humanizer

Transform AI-generated Chinese text into natural academic writing style

191 stars

Best use case

chinese-text-humanizer is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

Transform AI-generated Chinese text into natural academic writing style

Teams using chinese-text-humanizer should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

  • You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

  • You only need a quick one-off answer and do not need a reusable workflow.
  • You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/chinese-text-humanizer/SKILL.md --create-dirs "https://raw.githubusercontent.com/wentorai/research-plugins/main/skills/writing/polish/chinese-text-humanizer/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/chinese-text-humanizer/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How chinese-text-humanizer Compares

Feature / Agentchinese-text-humanizerStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

Transform AI-generated Chinese text into natural academic writing style

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

SKILL.md Source

# Chinese Academic Text Humanizer

## Overview

AI-generated Chinese academic text often exhibits detectable patterns: overly balanced sentence structures, formulaic transitions, predictable hedging, and unnaturally consistent register. This guide provides strategies to transform AI-assisted drafts into natural, human-sounding Chinese academic prose while maintaining scholarly rigor. Applicable to journal manuscripts, thesis chapters, and grant applications written in Chinese.

## Common AI-Generated Chinese Text Patterns

### Detectable Markers

| Pattern | AI Tendency | Human Academic Style |
|---------|-----------|---------------------|
| **Sentence openings** | 重复使用 "本文"、"本研究"、"值得注意的是" | 变换主语:具体名词、被动句式、无主句 |
| **Transitions** | 机械使用 "首先…其次…最后" | 自然承接:因果、转折、递进交替 |
| **Hedging** | 每句都加 "可能"、"一定程度上" | 有选择地使用限定语,关键结论要果断 |
| **Paragraph structure** | 总-分-总,每段等长 | 段落长短不一,论证节奏有变化 |
| **Vocabulary** | 偏好"关键""重要""显著" | 用词精确:分辨"关键/核心/至关重要"的语境差异 |
| **列举** | 大量并列三项 ("X、Y和Z") | 有时二项,有时四项,避免刻板的三项并列 |

## Humanization Strategies

### Strategy 1: Vary Sentence Structure

```markdown
AI 风格(机械均匀):
  "深度学习在自然语言处理中取得了显著进展。研究者提出了多种模型来解决文本分类问题。
   这些方法在标准数据集上取得了良好效果。然而,实际应用中仍面临诸多挑战。"

修改后(节奏变化):
  "深度学习正深刻改变自然语言处理的面貌——从文本分类到机器翻译,预训练模型已成为
   事实上的标准范式。不过,标准数据集上的高分并不总能转化为实际场景中的可靠表现。
   部署环境中的分布偏移、标注噪声以及计算资源限制,都使得从实验室到生产环境的过渡
   远非一帆风顺。"

改进点:
  ✓ 长短句交替
  ✓ 破折号引入补充说明
  ✓ 具体化 "诸多挑战"
  ✓ "远非一帆风顺" 比 "仍面临挑战" 更生动
```

### Strategy 2: Replace Formulaic Transitions

| AI 公式化 | 替代方案 |
|----------|---------|
| 首先…其次…最后… | 删除序号词,用逻辑关系自然承接 |
| 值得注意的是 | 直接陈述,或用 "尤其是"、"特别是" |
| 综上所述 | "以上分析表明" 或直接给出结论 |
| 总的来说 | "总体而言" 或省略,直接表述 |
| 研究表明 | 具体引用:"Li et al. (2024) 发现…" |
| 具有重要意义 | 说清楚重要在哪里:"这为X提供了Y" |

### Strategy 3: Inject Disciplinary Voice

```markdown
AI(泛化的学术腔):
  "本研究采用混合方法研究设计,结合定量和定性数据分析,
   以期全面了解该现象。"

经济学风格:
  "我们构建了一个双重差分模型,利用2016年政策冲击作为外生变量,
   识别市场准入放宽对中小企业融资成本的因果效应。"

社会学风格:
  "通过对15位流动务工人员的深度访谈,本文试图理解制度性排斥如何
   在日常生活实践中被体验和回应。"

改进点:
  ✓ 具体的方法名称(不是 "混合方法")
  ✓ 具体的研究对象(不是 "该现象")
  ✓ 学科特有术语(因果效应、外生变量、制度性排斥)
```

### Strategy 4: Authentic Chinese Academic Conventions

```markdown
中文学术写作的特有习惯(AI 常遗漏):

1. 四字格的适当使用(但不过度):
   ✓ "方兴未艾""不容忽视""有待商榷"
   ✗ 不要每句都塞入四字格

2. 引述惯例:
   ✓ "正如张三 (2023) 所指出的,…"
   ✓ "有学者认为…(李四,2024;王五,2023)"
   ✗ AI 常写:"研究表明…" 但不给出具体引用

3. 数据呈现:
   ✓ "回归结果(表3第(2)列)显示,处理组均值提高了12.3个百分点(p<0.01)"
   ✗ AI 常写:"结果显示变量之间存在显著正相关关系"(太模糊)

4. 论文基金致谢:
   ✓ 放在首页脚注,格式:"本研究受国家自然科学基金项目(No. XXXXXXX)资助"
```

### Strategy 5: Paragraph-Level Restructuring

```markdown
AI 通常生成 "总-分-总" 结构的等长段落。修改策略:

1. 合并过短的段落(少于3句的段落考虑合并)
2. 拆分过长的段落(超过8句时寻找分割点)
3. 删除重复表述(AI 喜欢在段首和段尾重复相同观点)
4. 添加段间逻辑标记("与此形成对照的是""这一发现的理论含义在于")
5. 允许段落长短不一(3句段和7句段交替出现更自然)
```

## Revision Checklist

```markdown
## 逐段检查清单

□ 删除了所有 "本文"/"本研究" 的过度使用(每页不超过2次)
□ 替换了公式化过渡词
□ 检查了每个 "重要/显著/关键" 是否有具体说明
□ 确保引用了具体文献(不是泛泛的 "研究表明")
□ 段落长度有变化(不是每段都5-6句)
□ 句式有变化(长短句交替,主动被动穿插)
□ 四字格使用适度(每段不超过1-2个)
□ 数据引用精确(有表格编号、列号、数值)
□ 读起来像你自己写的(而不是任何人都能写的通用文本)
```

## References

- 中国社会科学院语言研究所 (2016). *现代汉语词典* (第7版). 商务印书馆.
- 毕飞宇 (2017). *小说课*. 人民文学出版社. (关于汉语写作节奏的参考)
- [中国知网写作规范](https://www.cnki.net/)

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