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Humanize AI text

将AI生成的文本改写得更自然，以绕过AI检测器（如GPTZero）。支持ChatGPT、Claude、GPT等内容的改写。

来源平台：SkillHub
来源标识：SkillHub/humanize-ai-text
源文件：原始说明
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快速判断

将AI生成的文本改写得更自然，以绕过AI检测器（如GPTZero）。支持ChatGPT、Claude、GPT等内容的改写。

最后校验2026-05-27
来源平台SkillHub
安全提示中
下载副本ZIP 可用

适合任务

  • 按 SkillHub 收录说明复用成熟任务流程。
  • 通过下载包离线阅读完整 Skill 内容。
  • 结合热度指标优先评估常用 Skill。

输入与输出

输入：任务目标、上下文材料、文件路径、约束条件或需要处理的内容。

输出：按 Skill 说明生成的文档、代码、检查结果、计划、建议或操作步骤。

示例任务

  • 使用 Humanize AI text 帮我处理当前任务，并说明需要准备哪些输入。
  • 根据 Humanize AI text 的说明，先列出使用前的安全检查项。

安装方式

  1. 下载本站提供的 Skill ZIP 并解压。
  2. 把解压后的 Skill 目录放入当前 AI 工具支持的 skills 目录。
  3. 如需在线查看原始内容，可打开 GitHub 的 SKILL.md。

在线原始地址：skillhub-humanize-ai-text/SKILL.md

风险边界

SkillHub 提供了源站安全报告入口，但本站不替代人工审查。使用前仍需检查权限、外部依赖和敏感数据边界。

SKILL.md 文档介绍

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)

| Category | Examples |

|----------|----------|

| Citation Bugs | oaicite, turn0search, contentReference |

| Knowledge Cutoff | "as of my last training", "based on available information" |

| Chatbot Artifacts | "I hope this helps", "Great question!", "As an AI" |

| Markdown | bold, ## headers, `` code blocks `` |

High Signal

| Category | Examples |

|----------|----------|

| AI Vocabulary | delve, tapestry, landscape, pivotal, underscore, foster |

| Significance Inflation | "serves as a testament", "pivotal moment", "indelible mark" |

| Promotional Language | vibrant, groundbreaking, nestled, breathtaking |

| Copula Avoidance | "serves as" instead of "is", "boasts" instead of "has" |

Medium Signal

| Category | Examples |

|----------|----------|

| Superficial -ing | "highlighting the importance", "fostering collaboration" |

| Filler Phrases | "in order to", "due to the fact that", "Additionally," |

| Vague Attributions | "experts believe", "industry reports suggest" |

| Challenges Formula | "Despite these challenges", "Future outlook" |

Style Signal

| Category | Examples |

|----------|----------|

| Curly Quotes | "" instead of "" (ChatGPT signature) |

| Em Dash Overuse | Excessive use of — for emphasis |

| Negative Parallelisms | "Not only... but also", "It's not just... it's" |

| Rule of Three | Forced triplets like "innovation, inspiration, and insight" |

---

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

| Rating | Criteria |

|--------|----------|

| Very High | Citation bugs, knowledge cutoff, or chatbot artifacts present |

| High | >30 issues OR >5% issue density |

| Medium | >15 issues OR >2% issue density |

| Low | <15 issues AND <2% density |

---

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."

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