humanize-it

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Original

English

🇨🇳

Translation

Chinese

Humanize-It: 迭代式去 AI 味改写

Humanize-It: Iterative AI Trace Removal Rewriting

对指定文档进行去 AI 味的改写。根据文本类型和内容，从三个子 skill 中自动选择最合适的一个开始改写。如果改写效果不满意，换用另一个 skill 继续迭代，直到效果达标或达到 42 次迭代上限。

Rewrite specified documents to remove AI-generated traces. Based on the text type and content, automatically select the most suitable one from three sub-skills to start rewriting. If the rewriting result is unsatisfactory, switch to another skill and continue iterating until the result meets the standard or reaches the maximum limit of 42 iterations.

子 Skill 能力矩阵

Sub-Skill Capability Matrix

Skill	适用场景	核心能力	改写风格
humanizer-zh	通用文本、文章、博客、文案	识别 24+ AI 写作模式，注入个性与灵魂，节奏变化	自然、有温度、带观点
humanize-chinese	通用 + 学术 + 长文本	20+ 规则检测 + 统计特征 + LR 融合评分，CLI 工具链	多种风格可选（知乎/小红书/学术/文学等）
technical-writing	技术文档、架构说明、设计稿、评审文档	去除技术黑话，证据先行，消除主持腔	平实、严谨、可论证

Skill	Applicable Scenarios	Core Capabilities	Rewriting Style
humanizer-zh	General texts, articles, blogs, copywriting	Identifies 24+ AI writing patterns, injects personality and soul, varies rhythm	Natural, warm, opinionated
humanize-chinese	General + academic + long texts	20+ rule checks + statistical features + LR fusion scoring, CLI toolchain	Multiple styles available (Zhihu/Xiaohongshu/academic/literary, etc.)
technical-writing	Technical documents, architecture descriptions, design drafts, review documents	Removes technical jargon, prioritizes evidence, eliminates host-style tone	Plain, rigorous, verifiable

工作流程

Workflow

第一步：读取文档并分析

Step 1: Read and Analyze the Document

读取用户指定的文档内容
判断文档类型：
- 技术文档（技术方案、架构说明、设计文档、评审稿）→ 优先使用
```
technical-writing
```
- 学术论文（论文、研究文章）→ 优先使用
```
humanize-chinese
```
  （学术模式）
- 通用文本（博客、文案、文章）→ 优先使用
```
humanizer-zh
```
- 长文本（≥1500 字）→ 优先使用
```
humanize-chinese
```
  （长文本模式）

Read the content of the document specified by the user
Determine the document type:
- Technical documents (technical solutions, architecture descriptions, design documents, review drafts) → Prioritize using
```
technical-writing
```
- Academic papers (papers, research articles) → Prioritize using
```
humanize-chinese
```
  (academic mode)
- General texts (blogs, copywriting, articles) → Prioritize using
```
humanizer-zh
```
- Long texts (≥1500 words) → Prioritize using
```
humanize-chinese
```
  (longform mode)

第二步：改写策略选择

Step 2: Select Rewriting Strategy

根据文档类型选择第一个改写 skill：

文档类型判断：
├── 技术文档 → technical-writing → humanizer-zh → humanize-chinese
├── 学术论文 → humanize-chinese(academic) → humanizer-zh → technical-writing
├── 通用文本 → humanizer-zh → humanize-chinese → technical-writing
└── 长文本(≥1500字) → humanize-chinese(longform) → humanizer-zh → technical-writing

Select the first rewriting skill based on the document type:

Document type judgment:
├── Technical documents → technical-writing → humanizer-zh → humanize-chinese
├── Academic papers → humanize-chinese(academic) → humanizer-zh → technical-writing
├── General texts → humanizer-zh → humanize-chinese → technical-writing
└── Long texts(≥1500 words) → humanize-chinese(longform) → humanizer-zh → technical-writing

第三步：迭代改写循环

Step 3: Iterative Rewriting Loop

iteration = 0
MAX_ITERATIONS = 42

while iteration < MAX_ITERATIONS:
    iteration += 1

    # 使用当前 skill 进行改写
    result = humanize(current_text, current_skill)

    # 评估改写效果
    score = evaluate(result)

    if score >= PASS_THRESHOLD:
        # 改写效果达标，输出最终结果
        output(result)
        break

    # 效果不达标，切换到下一个 skill
    current_skill = next_skill(skill_order)
    current_text = result  # 在上次改写基础上继续优化

    if all_skills_exhausted():
        # 所有 skill 都用过一轮，从头开始新一轮组合
        current_skill = first_skill(skill_order)

iteration = 0
MAX_ITERATIONS = 42

while iteration < MAX_ITERATIONS:
    iteration += 1

    # Rewrite using current skill
    result = humanize(current_text, current_skill)

    # Evaluate rewriting effect
    score = evaluate(result)

    if score >= PASS_THRESHOLD:
        # Rewriting effect meets standard, output final result
        output(result)
        break

    # Effect does not meet standard, switch to next skill
    current_skill = next_skill(skill_order)
    current_text = result  # Continue optimizing based on last rewrite result

    if all_skills_exhausted():
        # All skills have been used in one round, start a new round of combination from the beginning
        current_skill = first_skill(skill_order)

第四步：质量评估

Step 4: Quality Evaluation

每次改写后，按以下维度评估效果（百分制）：

维度	权重	评估标准
AI 痕迹去除	30%	三段式、套话、机械连接词是否消除
自然度	25%	读起来是否像人写的，节奏是否自然
信息完整	20%	核心信息是否保留，没有丢失关键内容
风格一致	15%	语气是否前后统一，符合文档类型
可读性	10%	句子是否通顺，逻辑是否清晰

评分标准：

≥ 80 分：通过，输出结果
60-79 分：尚可，再迭代一轮看能否提升
< 60 分：不达标，必须继续改写

After each rewrite, evaluate the effect based on the following dimensions (100-point scale):

Dimension	Weight	Evaluation Criteria
AI Trace Removal	30%	Whether three-part structures, clichés, and mechanical conjunctions are eliminated
Naturalness	25%	Whether it reads like human writing, with natural rhythm
Information Completeness	20%	Whether core information is retained without losing key content
Style Consistency	15%	Whether tone is consistent throughout and matches the document type
Readability	10%	Whether sentences are smooth and logic is clear

Scoring Standards:

≥ 80 points: Pass, output result
60-79 points: Acceptable, iterate one more round to see if improvement is possible
< 60 points: Not up to standard, must continue rewriting

第五步：输出结果

Step 5: Output Result

输出最终改写结果，附带：

改写后的完整文本（写入原文件或指定输出文件）
改写摘要（使用了哪些 skill，迭代了几次，最终评分）
主要改动点列表

Output the final rewritten result, accompanied by:

Complete rewritten text (written to the original file or specified output file)
Rewrite summary (which skills were used, number of iterations, final score)
List of main changes

具体执行指令

Specific Execution Instructions

调用 humanizer-zh 时

When Calling humanizer-zh

使用 Skill 工具调用

humanizer-zh

，将文档内容传入，让其按 24 种 AI 写作模式进行检测和改写。重点关注：

删除填充短语
打破公式结构
变化节奏
信任读者
删除金句
注入个性与灵魂

改写后按其 50 分制质量评分进行初步评估（≥ 40 分为达标）。

Use the Skill tool to call

humanizer-zh

, pass in the document content, and let it detect and rewrite according to 24 AI writing patterns. Focus on:

Delete filler phrases
Break formulaic structures
Vary rhythm
Trust readers
Delete golden sentences
Inject personality and soul

Preliminary evaluation is conducted using its 50-point quality scoring system (≥ 40 points is considered up to standard).

调用 humanize-chinese 时

When Calling humanize-chinese

使用 Skill 工具调用

humanize-chinese

，根据文档类型选择对应模式：

通用文本：使用 detect + rewrite 流程
学术论文：使用 academic 模式
长文本：使用
```
--scene novel
```
或
```
--scene auto
```

如果 CLI 工具可用，优先使用 CLI 进行量化检测和改写；否则按其 LLM 使用指南手动执行。

改写后按其 0-100 分评分体系评估（≤ 35 分为 LOW 区间，达标）。

Use the Skill tool to call

humanize-chinese

, select the corresponding mode based on the document type:

General texts: Use detect + rewrite process
Academic papers: Use academic mode
Long texts: Use
```
--scene novel
```
or
```
--scene auto
```

If the CLI tool is available, prioritize using CLI for quantitative detection and rewriting; otherwise, manually execute according to its LLM usage guidelines.

Evaluation is conducted using its 0-100 scoring system (≤ 35 points is in the LOW range, considered up to standard).

调用 technical-writing 时

When Calling technical-writing

使用 Skill 工具调用

technical-writing

，主要用于技术文档的改写。重点关注：

去除 buzzword 黑名单中的词汇
消除主持腔和评价腔
按「条件 → 对象 → 判断」重写句子
证据先行，减少无支撑的强判断
去除过场句

Use the Skill tool to call

technical-writing

, mainly for rewriting technical documents. Focus on:

Remove words from the buzzword blacklist
Eliminate host-style and evaluative tones
Rewrite sentences according to "Condition → Object → Judgment"
Prioritize evidence, reduce unsupported strong judgments
Remove transitional sentences

迭代策略细节

Iteration Strategy Details

何时切换 Skill

When to Switch Skills

同一个 skill 连续改写 3 次评分无提升 → 切换到下一个 skill
评分下降 → 回退到上一个版本，切换 skill
所有 skill 都用完一轮但未达标 → 从第一个 skill 重新开始，但使用上一轮的改写结果作为输入
达到 42 次迭代 → 输出当前最佳结果

No score improvement after 3 consecutive rewrites with the same skill → Switch to the next skill
Score decreases → Roll back to the previous version and switch skill
All skills have been used in one round but not up to standard → Restart from the first skill, using the rewritten result from the previous round as input
Reach 42 iterations → Output the current best result

组合策略

Combination Strategies

不同 skill 的改写侧重不同，组合使用可以互补：

humanizer-zh → humanize-chinese：先注入个性灵魂，再做量化去 AI 模式
humanize-chinese → humanizer-zh：先做系统化去 AI，再注入温度
technical-writing → humanizer-zh：先保证技术严谨性，再增加自然度
humanizer-zh → technical-writing：先去除通用 AI 痕迹，再调整技术语气

Different skills focus on different aspects of rewriting, and combining them can complement each other:

humanizer-zh → humanize-chinese: First inject personality and soul, then perform quantitative AI pattern removal
humanize-chinese → humanizer-zh: First perform systematic AI trace removal, then add warmth
technical-writing → humanizer-zh: First ensure technical rigor, then increase naturalness
humanizer-zh → technical-writing: First remove general AI traces, then adjust technical tone

用户交互

User Interaction

如果用户提供了明确的偏好（如"保持学术风格"、"要更口语化"、"技术文档不要太随意"），在改写时遵循用户偏好，并优先选择对应的 skill。

如果用户未指定文档类型，可以通过 AskUserQuestion 询问：

文档类型（通用 / 技术 / 学术）
期望的改写风格（自然随意 / 平实严谨 / 学术规范）
是否需要写入原文件还是输出到新文件

If the user provides clear preferences (such as "maintain academic style", "be more colloquial", "technical documents should not be too casual"), follow the user's preferences during rewriting and prioritize selecting the corresponding skill.

If the user does not specify the document type, you can ask the user via AskUserQuestion:

Document type (general / technical / academic)
Desired rewriting style (natural and casual / plain and rigorous / academic standard)
Whether to write to the original file or output to a new file