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From content-creation
Analyzes Chinese articles for AI-generated content indicators (template phrases, transition density, sentence uniformity) and optionally rewrites to pass WeChat's 3.27 non-human content detection. Use when checking AI traces, humanizing text for WeChat publication, or bypassing WeChat AI detection.
npx claudepluginhub lovstudio/skills --plugin generalHow this skill is triggered — by the user, by Claude, or both
Slash command
/content-creation:anti-wechat-ai-checkThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
检测文章中的 AI 生成痕迹（模板短语、过渡词堆砌、句式雷同等），给出风险
Rewrites Chinese articles to remove AI-generated patterns like template openings, cliché transitions, buzzwords, fake quotes, overused dashes, bullet stacking, and translationese. Supports diagnostic, light, deep, and final editing modes.
Detects AI-like patterns in Chinese text and rewrites for naturalness, academic AIGC reduction (CNKI/VIP checks), and style conversion (Zhihu, WeChat, etc.).
Removes AI writing patterns from prose to make text sound natural and human-written. Detects filler phrases, formulaic structures, and other LLM tells in English and Chinese.
Share bugs, ideas, or general feedback.
检测文章中的 AI 生成痕迹（模板短语、过渡词堆砌、句式雷同等），给出风险 评分和修改建议，并可输出人性化润色后的版本。基于微信公众平台运营规范 3.27 条款（非真人自动化创作行为）的检测逻辑。
You MUST follow these steps in order:
Determine the input source:
--textpython skills/lovstudio-anti-wechat-ai-check/scripts/analyze.py \
--input <path> --format json
Or with inline text:
python skills/lovstudio-anti-wechat-ai-check/scripts/analyze.py \
--text "文章内容" --format json
Show the user:
IMPORTANT: Use AskUserQuestion to ask what to do next:
| Option | Description |
|---|---|
| 仅查看报告 | 用户自己修改，skill 结束 |
| 给出修改建议 | 列出每个问题的具体修改建议，不改原文 |
| 直接输出修改版 | 输出人性化润色后的完整文章 |
When rewriting, follow these humanization rules:
Output the humanized article as markdown. If the input was a file, also offer
to write the result back to a file (with -humanized suffix).
| Argument | Default | Description |
|---|---|---|
--input, -i | — | Input file path (.md, .txt) |
--text, -t | — | Inline text to analyze |
--format, -f | text | Output format: text or json |
No external dependencies — stdlib only.