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stvlynn/qclaw-skills · Archived

humanize-ai-text

AI 生成文本人性化改写。将 ChatGPT、Claude、GPT 等生成的?

First seen Mar 21, 2026

Installation

$ npx skills add stvlynn/qclaw-skills --skill humanize-ai-text
View on skills.sh ↗ GitHub repository ↗

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More details

Agent compatibility

Declared targets from SKILL.md / docs. Unmarked agents are not listed — the skill may still install via the CLI.

Claude Code Not declared
Cursor Not declared
Codex Not declared
GitHub Copilot Not declared
Windsurf Not declared
Gemini CLI Not declared
Cline Not declared
OpenCode Not declared

Also listed on

Alternate registries and mirrors of this skill.

Repository health

Stars 4
Default branch main
Open issues 0
Status Archived

Skill metadata

Parsed from SKILL.md frontmatter.

Allowed toolsRead, Write, StrReplace, Glob
Declared agents clawdbot
More metadata
openclaw
{"emoji":"✍️","requires":{"bins":["python3"]}}

Package contents

Files included with this skill beyond the listing page.

  • skill md SKILL.md 5,289 B
  • docs SUMMARY.md 302 B

History

  1. First seen on skills.sh
  2. First recorded snapshot · 3 installs

SKILL.md

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:

``bash python scripts/detect.py document.txt ``

  1. Transform with comparison:

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

  1. Verify improvement:

``bash python scripts/detect.py document_v2.txt -s ``

  1. 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."