为什么值得关注

该技能不仅替换词汇，还解决更持久的结构性问题（如过度解释、模板段落），基于 2026 年 UMD/Google DeepMind 的研究，并包含声音校准和防止替代词癖的规则。

适合谁使用

使用 Claude 撰写或编辑英文文本的用户
希望避免 AI 检测的内容创作者
精炼 AI 生成草稿的编辑和写作者
希望 AI 辅助写作听起来真正像人类的任何人

典型使用场景

将 Claude 生成的博客文章或帖子人性化
重写 AI 生成的电子邮件或报告，使其符合个人风格
在发布前对推广帖或社交媒体更新进行去 AI 处理
编辑学术或专业文本，去除过度解释和模板结构

项目优势

基于 61,608 篇文本研究的三层方法（排版、词汇、结构）
声音校准功能：从用户提供的文本中学习，保存为可编辑的 Markdown 文件
包含 19 类短语的明确黑名单，每类都有前后对比例子
经过实际测试，根据反馈迭代至 v1.1，修复了余留的人工感

使用前须知

不旨在击败 AI 检测器；目标是让人类阅读者不觉得尴尬
需要用户提供文本进行声音校准才能达到最佳效果
初始发布版本（v1.0）仍被识别为 AI 文本；v1.1 是修复版本，但可能仍有其他层面需改进

README 快速开始

Unslop

A Claude skill that removes signs of AI writing from English text. Not just the word-level tells ("delve", "it's important to note") but the structural ones: over-explained conclusions, template paragraphs, invented specifics. Calibrates to your personal voice.

GitHub About line: "English text humanizer for Claude: typography, vocabulary, structure. Calibrates to your voice. Built on the UMD / Google DeepMind study and Wikipedia's Signs of AI writing."

Why word-swapping doesn't work

Every humanizer promises to make AI text "sound human", and most of them swap vocabulary: "delve" out, "explore" in. There are two problems with that.

First, the wordlist decays. "Delve" peaked in 2023 and collapsed in 2025. GPT-5.1 suppresses em dashes. Wikipedia's Signs of AI writing now dates its vocabulary lists by model era, because each generation retires the previous tells.

Second, and worse: the words were never the real signal. In 2026, the University of Maryland and Google DeepMind compared 61,608 texts written by humans and five models. When they ran AI texts through surface editing (clichés, purple prose, redundant exposition removed), a classifier looking only at structure barely noticed: detection dropped from 95.5% to 93.9%. The text gives itself away by how it's built. AI states the moral of every paragraph, runs single-track claim-support-takeaway structure, renders emotion through body metaphors, avoids naming real things, and rounds every ending to a tidy point.

A humanizer that only touches vocabulary fixes the layer that was already fixing itself, and leaves the durable signal intact. This skill works on three levels at once.

How it's built

Level 1: typography and mechanics. Em dashes, mixed quotation marks, Title Case Headings, bold on every "key term", inline-header bullet lists, "Conclusion" sections, markdown debris like utm_source=chatgpt.com. Cheap fixes; partially grep-checkable, and the skill greps itself.

Level 2: vocabulary and rhetoric. 19 categories of constructions with stop lists and fixes: inflated significance ("stands as a testament"), negative parallelisms ("it's not X, it's Y"), the rule of three, superficial -ing analysis ("...highlighting the importance of"), copula avoidance ("serves as" for "is"), synonym cycling, vague attributions, an

项目描述

English text humanizer for Claude: typography, vocabulary, structure. Calibrates to your voice. Built on the UMD / Google DeepMind study and Wikipedia's Signs of AI writing

unslop