什么是 Humanize AI？

Humanize AI 技能为开发者和内容创作者提供了一个强大的框架，用于净化大型语言模型生成的文本。通过将这些 Openclaw 技能集成到您的工作流中，您可以通过编程方式识别并纠正暗示 AI 创作的语言模式，例如可预测的词汇、过度的夸大辞藻和重复的句子结构。

该工具集不仅限于简单的检测；它提供了一条自动化路径，将机械的散文转化为更真实、更具人性化的内容。对于需要保持一致的品牌形象或确保自动化内容管道产出高质量、无法被检测的输出的用户来说，它具有极高的价值。

下载入口:https://github.com/openclaw/skills/tree/main/skills/artur-zhdan/humanize-ai

安装与下载

1. ClawHub CLI

从源直接安装技能的最快方式。

2. 手动安装

将技能文件夹复制到以下位置之一

优先级：工作区 > 本地 > 内置

3. 提示词安装

将此提示词复制到 OpenClaw 即可自动安装。

Humanize AI 应用场景

• 通过移除可预测的语言签名来检测并绕过 AI 内容检测器。
• 批量处理营销文案，移除诸如“为了”或“充当”之类的冗余短语。
• 自动删除聊天机器人导出的内容中如“希望这有所帮助”之类的痕迹，从而实现净化。
• 识别并减少 AI 生成的产品描述中的宣传性夸大辞藻。
• 通过转换弯引号和修复破折号模式来规范文档排版。

1. 用户使用 analyze 脚本启动分析，扫描文档中特定的 AI 签名。
2. 系统生成详细报告，将问题分类为 AI 词汇、夸大辞藻和可自动替换的短语。
3. 使用 humanize 脚本执行安全替换，该脚本应用基于规则的逻辑，将冗余短语替换为简洁的备选项。
4. 工具移除配置中定义的已知聊天机器人痕迹和机械化的开头语。
5. 执行最终验证扫描，以确认内容现在符合 Openclaw Skills 生态系统内的自然语言标准。

Humanize AI 配置指南

要开始使用这些 Openclaw 技能，请确保您已准备好 Python 环境。进入技能目录并使用以下命令：

# 查看分析功能
python scripts/analyze.py --help

# 在示例文件上测试人性化脚本
python scripts/humanize.py input.txt -o output.txt

Humanize AI 数据架构与分类体系

该技能通过结构化的 JSON 配置来管理其检测逻辑和替换规则。这使得在使用 Openclaw Skills 针对不同领域进行定制时具有高度的灵活性。

name: humanize-ai
description: Humanize AI content by detecting and auto-fixing AI generated content. Humanize AI text using Python scripts. Scans for AI vocabulary, puffery, chatbot artifacts, and auto-replaces filler phrases. Use when you want to analyze text in AI detector and bypass it in future, batch-process files, run automated cleanup, or get a report before manual humanizing.
allowed-tools:
  - Read
  - Write
  - StrReplace
  - Shell
  - Glob

Humanize CLI

Command-line tools for detecting and auto-fixing AI writing patterns.

Scripts

analyze.py — Detect AI Patterns

Scans text and reports AI vocabulary, puffery, chatbot artifacts, and auto-replaceable phrases.

# Analyze a file
python scripts/analyze.py input.txt

# Analyze from stdin
echo "This serves as a testament to our commitment" | python scripts/analyze.py

# JSON output for programmatic use
python scripts/analyze.py input.txt --json

Output example: ```

AI PATTERN ANALYSIS - 5 issues found

AI VOCABULARY: ? testament: 1x ? crucial: 2x

AUTO-REPLACEABLE: ? "serves as" → "is": 1x ? "in order to" → "to": 1x

---

### humanize.py — Auto-Replace Patterns

Performs automatic replacements for common AI-isms.

```bash
# Humanize and print to stdout
python scripts/humanize.py input.txt

# Write to output file
python scripts/humanize.py input.txt -o output.txt

# Include em dash replacement
python scripts/humanize.py input.txt --fix-dashes

# Quiet mode (no change log)
python scripts/humanize.py input.txt -q

What it fixes automatically:

• Filler phrases: "in order to" → "to", "due to the fact that" → "because"
• Copula avoidance: "serves as" → "is", "boasts" → "has"
• Sentence starters: removes "Additionally,", "Furthermore,", "Moreover,"
• Curly quotes → straight quotes
• Chatbot artifacts: removes "I hope this helps", "Let me know if", etc.

Workflow

1. Analyze first to see what needs fixing:

   python scripts/analyze.py document.txt
   

2. Auto-fix safe replacements:

   python scripts/humanize.py document.txt -o document_clean.txt
   

3. Manual review for AI vocabulary and puffery flagged by analyze (these require human judgment)

4. Re-analyze to confirm improvements:

   python scripts/analyze.py document_clean.txt
   

Customizing Patterns

Edit scripts/patterns.json to add/remove:

• ai_words — vocabulary that flags but doesn't auto-replace
• puffery — promotional language to flag
• replacements — phrase → replacement mappings (empty string = delete)
• chatbot_artifacts — phrases to auto-remove
• hedging_phrases — excessive hedging to flag

Batch Processing

Process multiple files:

# Analyze all markdown files
for f in *.md; do
  echo "=== $f ===" 
  python scripts/analyze.py "$f"
done

# Humanize all txt files in place
for f in *.txt; do
  python scripts/humanize.py "$f" -o "$f.tmp" && mv "$f.tmp" "$f"
done