lovstudio-anti-wechat-ai-check

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Analyze articles for AI-generated content indicators and rewrite to pass WeChat's 3.27 non-human automated content creation detection. Checks for template phrases, transition word density, sentence uniformity, paragraph pattern repetition, and other signals that WeChat uses to flag AI content. Outputs a risk report and an optional humanized rewrite. Use when the user wants to check if an article looks AI-generated, make an article more human-like, bypass WeChat AI detection, or humanize AI-written content. Also trigger when the user mentions "去AI痕迹", "人性化润色", "微信AI检测", "anti-ai-check", "humanize article", "公众号发文检查".

4installs

Sourcelovstudio/skills

Added on2026-04-30

NPX Install

npx skill4agent add lovstudio/skills lovstudio-anti-wechat-ai-check

SKILL.md Content

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anti-wechat-ai-check — 微信公众号 AI 痕迹检测与人性化润色

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。

When to Use

用户准备将 AI 辅助写作的文章发布到微信公众号
用户想检查一篇文章是否有明显 AI 痕迹
用户想将 AI 生成的草稿改写为更自然的人类风格

Workflow (MANDATORY)

You MUST follow these steps in order:

Step 1: Get the article

Determine the input source:

If user provides a file path → read the file
If user pastes text in the conversation → save to a temp file or use
```
--text
```

Step 2: Run analysis

bash

python skills/lovstudio-anti-wechat-ai-check/scripts/analyze.py \
  --input <path> --format json

Or with inline text:

bash

python skills/lovstudio-anti-wechat-ai-check/scripts/analyze.py \
  --text "文章内容" --format json

Step 3: Present findings

Show the user:

Risk score (0-100) and risk level (LOW / MEDIUM / HIGH)
Template phrases found — list each one with its location
Structure issues — transition word density, paragraph uniformity, etc.
Sentence issues — length uniformity, repeated starters, excessive "的"

Step 4: Ask 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

Step 5: Humanize (if requested)

When rewriting, follow these humanization rules:

5a. 消除模板短语

删除或替换报告中标出的每个模板短语
"随着科技的不断发展" → 直接说具体的事（"去年 ChatGPT 发布后..."）
"综上所述" → 删掉，或换成口语化的收尾

5b. 降低过渡词密度

目标：过渡词密度 < 15%
删除不必要的 "首先/其次/此外/另外"
用具体的逻辑关系替代泛化连接词

5c. 打破句式均匀

刻意制造长短句交替：短句 < 15 字，长句 > 40 字
加入口语化表达、反问句、感叹句
偶尔使用不完整句或省略句

5d. 打破段落均匀

有的段落只有一两句话，有的段落可以很长
避免每段都是 "论点 + 论据 + 小结" 的三段式

5e. 增加人味

加入个人经历、具体案例、数字细节
使用口语化表达（"说白了"、"讲真"、"你想想"）
适当使用不规范但自然的表达
减少 "的" 字使用（目标 < 5%）

5f. 保留原意

核心观点和信息不能丢失
专业术语保留，不要过度口语化
保持原文的立场和态度

Step 6: Output

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).

CLI Reference

Argument	Default	Description
`--input` , `-i`	—	Input file path (.md, .txt)
`--text` , `-t`	—	Inline text to analyze
`--format` , `-f`	`text`	Output format: `text` or `json`

Dependencies

No external dependencies — stdlib only.