Skill: 0xspeter/humanize-chinese-2-0-0 (version 1.0.0)

--- name: humanize-chinese description: Detect and humanize AI-generated Chinese text. 20+ detection categories, weighted 0-100 scoring with sentence-level analysis, 7 style transforms (casual/zhihu/xiaohongshu/wechat/academic/literary/weibo), sentence restructuring, context-aware replacement. Pure Python, no dependencies. v2.0.0 allowed-tools: - Read - Write - Edit - exec --- # 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 ```bash # 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 ``` --- ## Detection System ### Scoring Weighted 0-100 score with 4 severity levels: | Score | Level | Meaning | |-------|-------|---------| | 0-24 | LOW | Likely human-written | | 25-49 | MEDIUM | Some AI signals | | 50-74 | HIGH | Probably AI-generated | | 75-100 | VERY HIGH | Almost certainly AI | ### Detection Categories #### 🔴 Critical (weight: 8) | Category | Examples | |----------|----------| | Three-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) | Category | Examples | |----------|----------| | AI 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) | Category | Examples | |----------|----------| | Hedging Language | 在一定程度上, 某种程度上, 通常情况下 (>5 occurrences) | | List Addiction | Excessive numbered/bulleted lists | | Punctuation Overuse | Dense em dashes, semicolons | | Excessive Rhetoric | 对偶/排比句过多 | #### ⚪ Style Signal (weight: 1.5) | Category | Description | |----------|-------------| | Uniform Paragraphs | Low CV in paragraph lengths | | Low Burstiness | Monotonous sentence lengths | | Emotional Flatness | Lack of emotional/personal expressions | | Repetitive Starters | Same sentence starters >3 times | | Low Entropy | Low 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 ``` --- ## Humanization Engine ### Transforms (applied in order) 1. **Structure cleanup** — Remove three-part structure (首先/其次/最后) 2. **Phrase replacement** — Context-aware replacement of AI phrases (regex patterns first, then plain text, longest-first matching) 3. **Sentence merge** — Merge overly short consecutive sentences 4. **Sentence split** — Split long sentences at natural breakpoints (但是/不过/同时) 5. **Punctuation normalization** — Reduce excessive semicolons, em dashes 6. **Vocabulary diversification** — Replace repeated words (进行/实现/提供 etc.) with synonyms 7. **Paragraph rhythm** — Vary uniform paragraph lengths (merge short, split long) 8. **Casual injection** — Add human expressions (scene-dependent) 9. **Paragraph shortening** — For social/chat scenes ### Scenes | Scene | Casualness | Best For | |-------|-----------|----------| | `general` | 0.3 | Default, balanced | | `social` | 0.7 | Social media, short posts | | `tech` | 0.3 | Tech blogs, tutorials | | `formal` | 0.1 | Formal articles, reports | | `chat` | 0.8 | Conversations, 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: | Style | Name | Description | |-------|------|-------------| | `casual` | 口语化 | Like chatting with friends — natural, relaxed | | `zhihu` | 知乎 | Rational, in-depth, personal opinions | | `xiaohongshu` | 小红书 | Enthusiastic, emoji-rich, product-focused | | `wechat` | 公众号 | Storytelling, engaging, relatable | | `academic` | 学术 | Rigorous, precise, no colloquialisms | | `literary` | 文艺 | Poetic, imagery-rich, metaphorical | | `weibo` | 微博 | Short, opinionated, shareable | ### Combine humanize + style ```bash 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 --- ## Scripts Reference ### detect_cn.py ```bash python scripts/detect_cn.py [file] [-j] [-s] [-v] [--sentences N] ``` | Flag | Description | |------|-------------| | `-j` | JSON output | | `-s` | Score only (e.g. "72/100 (high)") | | `-v` | Verbose: show worst sentences | | `--sentences N` | Number of worst sentences to show (default: 5) | ### humanize_cn.py ```bash python scripts/humanize_cn.py [file] [-o output] [--scene S] [--style S] [-a] [--seed N] ``` | Flag | Description | |------|-------------| | `-o` | Output file | | `--scene` | general/social/tech/formal/chat | | `--style` | casual/zhihu/xiaohongshu/wechat/academic/literary/weibo | | `-a` | Aggressive mode | | `--seed` | Random seed for reproducibility | ### style_cn.py ```bash python scripts/style_cn.py [file] --style S [-o output] [--seed N] [--list] ``` ### compare_cn.py ```bash 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 ```bash # 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 ```bash # 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 ```


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)