Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub · Communication & Collaboration
Humanize Chinese
Detect and humanize AI-generated Chinese text with 6 style transforms (casual/zhihu/xiaohongshu/wechat/academic/literary). Removes "AI flavor" using 16 detec...
skill openclawclawhub Free
Detect and humanize AI-generated Chinese text with 6 style transforms (casual/zhihu/xiaohongshu/wechat/academic/literary). Removes "AI flavor" using 16 detec...
⬇ 0 downloads ★ 0 stars Unverified but indexed
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Package facts
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- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- README.md, SKILL.md, evals/evals.json, package.json, scripts/compare_cn.py, scripts/detect_cn.py
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Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 2.0.0
Provenance
- Publisher
- swaylq
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Documentation
Humanize Chinese AI Text v2.0
Comprehensive CLI for detecting and transforming Chinese AI-generated text. Makes robotic AI writing natural and human-like. v2.0 highlights: weighted 0-100 scoring, sentence-level analysis, sentence restructuring (merge/split), context-aware replacement, rhythm variation, vocabulary diversification, 7 style transforms, external pattern config (patterns_cn.json).
Quick Start
# Detect AI patterns (20+ categories, 0-100 score) python scripts/detect_cn.py text.txt python scripts/detect_cn.py text.txt -v # verbose + worst sentences python scripts/detect_cn.py text.txt -s # score only python scripts/detect_cn.py text.txt -j # JSON output # Humanize text python scripts/humanize_cn.py text.txt -o clean.txt python scripts/humanize_cn.py text.txt --scene social python scripts/humanize_cn.py text.txt --scene tech -a # aggressive mode python scripts/humanize_cn.py text.txt --seed 42 # reproducible # Apply writing styles python scripts/style_cn.py text.txt --style zhihu -o zhihu.txt python scripts/style_cn.py text.txt --style xiaohongshu python scripts/style_cn.py --list # Compare before/after python scripts/compare_cn.py text.txt --scene tech -a python scripts/compare_cn.py text.txt -o clean.txt
Scoring
Weighted 0-100 score with 4 severity levels: ScoreLevelMeaning0-24LOWLikely human-written25-49MEDIUMSome AI signals50-74HIGHProbably AI-generated75-100VERY HIGHAlmost certainly AI
Detection Categories
🔴 Critical (weight: 8) CategoryExamplesThree-Part Structure首先...其次...最后, 一方面...另一方面, 其一...其二...其三Mechanical Connectors值得注意的是, 综上所述, 不难发现, 归根结底, 由此可见Empty Grand Words赋能, 闭环, 数字化转型, 协同增效, 全方位, 多维度 🟠 High Signal (weight: 4) CategoryExamplesAI High-Frequency Words助力, 彰显, 底层逻辑, 抓手, 触达, 沉淀, 复盘Filler Phrases值得一提的是, 众所周知, 毫无疑问Balanced Arguments虽然...但是...同时, 既有...也有...更有Template Sentences随着...的不断发展, 在当今...时代, 作为...的重要组成部分 🟡 Medium Signal (weight: 2) CategoryExamplesHedging Language在一定程度上, 某种程度上, 通常情况下 (>5 occurrences)List AddictionExcessive numbered/bulleted listsPunctuation OveruseDense em dashes, semicolonsExcessive Rhetoric对偶/排比句过多 ⚪ Style Signal (weight: 1.5) CategoryDescriptionUniform ParagraphsLow CV in paragraph lengthsLow BurstinessMonotonous sentence lengthsEmotional FlatnessLack of emotional/personal expressionsRepetitive StartersSame sentence starters >3 timesLow EntropyLow character-level entropy (predictable text)
Sentence-Level Analysis
With -v (verbose) mode, the detector identifies the most AI-like sentences: ── 最可疑句子 ── 1. [16分] 随着人工智能技术的不断发展，在当今数字化转型时代... 原因: 数字化转型, 深度融合, 模板: 随着.*?的(不断)?发展
Transforms (applied in order)
Structure cleanup — Remove three-part structure (首先/其次/最后) Phrase replacement — Context-aware replacement of AI phrases (regex patterns first, then plain text, longest-first matching) Sentence merge — Merge overly short consecutive sentences Sentence split — Split long sentences at natural breakpoints (但是/不过/同时) Punctuation normalization — Reduce excessive semicolons, em dashes Vocabulary diversification — Replace repeated words (进行/实现/提供 etc.) with synonyms Paragraph rhythm — Vary uniform paragraph lengths (merge short, split long) Casual injection — Add human expressions (scene-dependent) Paragraph shortening — For social/chat scenes
Scenes
SceneCasualnessBest Forgeneral0.3Default, balancedsocial0.7Social media, short poststech0.3Tech blogs, tutorialsformal0.1Formal articles, reportschat0.8Conversations, messaging
Aggressive Mode (-a)
Adds +0.3 casualness, more colloquial expressions, stronger sentence restructuring. Typical score reduction: 60-80 points on heavily AI-generated text.
Reproducibility
Use --seed N for reproducible results (same input + seed = same output).
Writing Style Transforms
7 specialized Chinese writing styles: StyleNameDescriptioncasual口语化Like chatting with friends — natural, relaxedzhihu知乎Rational, in-depth, personal opinionsxiaohongshu小红书Enthusiastic, emoji-rich, product-focusedwechat公众号Storytelling, engaging, relatableacademic学术Rigorous, precise, no colloquialismsliterary文艺Poetic, imagery-rich, metaphoricalweibo微博Short, opinionated, shareable
Combine humanize + style
python scripts/humanize_cn.py text.txt --style xiaohongshu -o xhs.txt This first humanizes (removes AI patterns) then applies the style transform.
External Configuration
All patterns, replacements, and scoring weights are in scripts/patterns_cn.json. Edit this file to: Add new AI vocabulary patterns Customize replacement alternatives Adjust scoring weights per severity Add regex patterns for template detection Set thresholds for hedging language detection
detect_cn.py
python scripts/detect_cn.py [file] [-j] [-s] [-v] [--sentences N] FlagDescription-jJSON output-sScore only (e.g. "72/100 (high)")-vVerbose: show worst sentences--sentences NNumber of worst sentences to show (default: 5)
humanize_cn.py
python scripts/humanize_cn.py [file] [-o output] [--scene S] [--style S] [-a] [--seed N] FlagDescription-oOutput file--scenegeneral/social/tech/formal/chat--stylecasual/zhihu/xiaohongshu/wechat/academic/literary/weibo-aAggressive mode--seedRandom seed for reproducibility
style_cn.py
python scripts/style_cn.py [file] --style S [-o output] [--seed N] [--list]
compare_cn.py
python scripts/compare_cn.py [file] [-o output] [--scene S] [--style S] [-a] Shows score diff, category changes, and metric comparison before/after humanization.
Workflow
# 1. Check AI score python scripts/detect_cn.py document.txt -v # 2. Humanize with comparison python scripts/compare_cn.py document.txt --scene tech -a -o clean.txt # 3. Verify improvement python scripts/detect_cn.py clean.txt -s # 4. Optional: apply specific style python scripts/style_cn.py clean.txt --style zhihu -o final.txt
Batch Processing
# Scan all files for f in *.txt; do echo "=== $f ===" python scripts/detect_cn.py "$f" -s done # Transform all markdown for f in *.md; do python scripts/humanize_cn.py "$f" --scene tech -a -o "${f%.md}_clean.md" done
Category context
Messaging, meetings, inboxes, CRM, and teammate communication surfaces.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
2 Docs2 Scripts2 Config
- SKILL.md
- README.md
- scripts/compare_cn.py
- scripts/detect_cn.py
- evals/evals.json
- package.json