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。

什么是 AI 文本人性化？

AI 文本人性化是一套专门的 Openclaw Skills，旨在识别和消除 AI 生成内容的独特标记。基于维基百科关于 AI 写作迹象指南的广泛研究，该工具提供了一种结构化方法，使来自 ChatGPT、Claude 和 GPT-4 等模型的文本听起来自然且具有人性。它针对 16 个特定的模式类别，包括聊天机器人人工痕迹、意义夸大和可预测的词汇。

通过利用这些 Openclaw Skills，用户可以系统地清除文档中的机器特征。无论您是在处理引用错误、特定的聊天机器人短语，还是过度使用的 AI 词汇（如 'delve' 或 'tapestry'），此技能都能提供必要的自动化功能，确保您的内容通过严格的 AI 检测器，同时保持可读性和专业语气。

AI 文本人性化 应用场景

• 绕过 GPTZero、Turnitin 和 Originality.ai 等 AI 检测器对提交内容的检测。
• 清理技术草案以消除聊天机器人伪影和引用错误。
• 通过减少意义夸大和促销性语言使营销副本更具人性。
• 使用 Openclaw Skills 审核大型文档库的 AI 概率。
• 优化 AI 生成的学术或专业文本，使其具有自然的流畅度和多样性。

1. 用户使用检测脚本发起扫描，以评估 AI 概率得分并识别特定的模式类别。
2. 系统标记关键问题，如引用错误、知识截止日期短语和聊天机器人特有的特征。
3. 执行转换过程以自动修复识别出的模式，替换机器人填充物并简化复杂的语言结构。
4. 用户利用对比工具查看原始文本与人性化文本的并排指标。
5. 根据 Openclaw Skills 分析提供的详细分类进行最终的手动调整。

AI 文本人性化 配置指南

要开始使用这些 Openclaw Skills，请确保您已安装 Python 并遵循以下步骤：

# 克隆仓库并进入目录
git clone <repository-url>
cd humanize-ai-text

# 对文本文件运行检测脚本
python scripts/detect.py your_document.txt

# 将文本转换为类人版本
python scripts/transform.py your_document.txt -o humanized_output.txt

AI 文本人性化 数据架构与分类体系

该技能通过集中式的配置模式组织其检测逻辑和转换规则：

name: humanize-ai-text
description: Humanize AI-generated text to bypass detection. This humanizer rewrites ChatGPT, Claude, and GPT content to sound natural and pass AI detectors like GPTZero, Turnitin, and Originality.ai. Based on Wikipedia's comprehensive "Signs of AI Writing" guide. Makes robotic AI writing undetectable and human-like.
allowed-tools:
  - Read
  - Write
  - StrReplace
  - Glob

Humanize AI Text

Comprehensive CLI for detecting and transforming AI-generated text to bypass detectors. Based on Wikipedia's Signs of AI Writing.

Quick Start

# Detect AI patterns
python scripts/detect.py text.txt

# Transform to human-like
python scripts/transform.py text.txt -o clean.txt

# Compare before/after
python scripts/compare.py text.txt -o clean.txt

Detection Categories

The analyzer checks for 16 pattern categories from Wikipedia's guide:

Critical (Immediate AI Detection)

High Signal

Medium Signal

Style Signal

Scripts

detect.py — Scan for AI Patterns

python scripts/detect.py essay.txt
python scripts/detect.py essay.txt -j  # JSON output
python scripts/detect.py essay.txt -s  # score only
echo "text" | python scripts/detect.py

Output:

• Issue count and word count
• AI probability (low/medium/high/very high)
• Breakdown by category
• Auto-fixable patterns marked

transform.py — Rewrite Text

python scripts/transform.py essay.txt
python scripts/transform.py essay.txt -o output.txt
python scripts/transform.py essay.txt -a  # aggressive
python scripts/transform.py essay.txt -q  # quiet

Auto-fixes:

• Citation bugs (oaicite, turn0search)
• Markdown (**, ##, ```)
• Chatbot sentences
• Copula avoidance → "is/has"
• Filler phrases → simpler forms
• Curly → straight quotes

Aggressive (-a):

• Simplifies -ing clauses
• Reduces em dashes

compare.py — Before/After Analysis

python scripts/compare.py essay.txt
python scripts/compare.py essay.txt -a -o clean.txt

Shows side-by-side detection scores before and after transformation

Workflow

1. Scan for detection risk:

   python scripts/detect.py document.txt
   

2. Transform with comparison:

   python scripts/compare.py document.txt -o document_v2.txt
   

3. Verify improvement:

   python scripts/detect.py document_v2.txt -s
   

4. Manual review for AI vocabulary and promotional language (requires judgment)

AI Probability Scoring

Customizing Patterns

Edit scripts/patterns.json to add/modify:

• ai_vocabulary — words to flag
• significance_inflation — puffery phrases
• promotional_language — marketing speak
• copula_avoidance — phrase → replacement
• filler_replacements — phrase → simpler form
• chatbot_artifacts — phrases triggering sentence removal

Batch Processing

# Scan all files
for f in *.txt; do
  echo "=== $f ==="
  python scripts/detect.py "$f" -s
done

# Transform all markdown
for f in *.md; do
  python scripts/transform.py "$f" -a -o "${f%.md}_clean.md" -q
done

Reference

Based on Wikipedia's Signs of AI Writing, maintained by WikiProject AI Cleanup. Patterns documented from thousands of AI-generated text examples.

Key insight: "LLMs use statistical algorithms to guess what should come next. The result tends toward the most statistically likely result that applies to the widest variety of cases."