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phuryn/work-humanizer

文本改写AI文本检测

这是一个用于去除工作文本中AI腔调的Agent Skill,保持事实、承诺和条件不变,适用于销售邮件、营销文案、客服回复等场景。

AI 中文解读 · 根据仓库描述与 README 生成 · 2026/10/17 09:31:13 · 查看原文

值得看Python提示词与 Skills兼有 资源合集
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选用指南

AI 根据 README 整理 · 2026/10/17 09:31:13

使用方式

作为Agent Skill使用,需复制到代理的skills文件夹,或通过Skills CLI安装。

查看 README 依据
One brief, checked by hand against it, so read it as an illustration rather than a measurement. Data: [`evidence/brief-to-email/`](evidence/brief-to-email/). ## Install Copy the `skills/work-humanizer` folder into your agent's skills folder: | Agent | Folder | |---|---| | Claude Code | `~/.claude/skills/work-humanizer` (all projects) or `.claude/skills/work-humanizer` (one project) | | Codex | `~/.codex/skills/work-humanizer` | | Other agents | the agent's skills folder; work-humanizer is a standard `SKILL.md` plus two supporting files |
Or with the [Skills CLI](https://skills.sh): `npx skills add phuryn/work-humanizer`. ## Use ```text Use work-humanizer on this: <paste text> Use work-humanizer on this and interview me first: <paste draft> Use work-humanizer on docs/launch-post.md ``` **For the most human result, give it your own material.** Paste your notes, a voice-memo transcript or a rough draft you wrote yourself. work-humanizer builds the piece on your argument, in your order, and keeps your sentences where it can. If you also have an AI draft, it only uses it as a checklist of points.
部署要求

需要支持SKILL.md的代理环境,如Claude Code、Codex或Cursor。

查看 README 依据
questions first. An Agent Skill: works in any agent that loads `SKILL.md`, including Claude Code, Codex and Cursor. ![work-humanizer scoreboard: judges would send work-humanizer's rewrite 48% of the time, blader/humanizer 20%, a one-line prompt 1%, Emulate-1 0%](evidence/charts/results@2x.png) ## What the test found 12 new AI-written texts, rewritten by each tool on the same model, judged blind by four AI models from different companies, with every meaning change checked against the original.
已知限制

测试样本较小,且未包含合同等法律文件;AI检测器无法完全规避。

查看 README 依据
Caveats: small samples (12 texts on the scoreboard, 56 across all sets, 4 for the notes result); the texts were sales, marketing, support, product and internal writing, with no contracts or other legal documents; AI judges rather than people, and the judges disagree on what "human" means; one sample per text per tool; one Pangram version on one day; a simulated writer in interview mode; and the people who built the skill designed the test. Full method, data, judge verdicts, fact checks and scripts: [`evidence/`](evidence/README.md). Read it before quoting any number. ## Repository ```
- **The one-line prompt reads more human, and that's the catch.** It beat the AI draft on 90% of judgments to work-humanizer's 75% because it rewrites freely: it changes more and adds things the writer never said. Judges who could see the original sent it once in 96 picks. - **No rewrite gets past an AI detector.** On Pangram, work-humanizer, blader/humanizer and the one-line prompt all leave the text reading as AI (2 to 4% human). Emulate-1, sold as undetectable, reaches 87% by changing the most.

README

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收录信息

仓库创建时间
2026/10/9 16:02:57
首次达标收录
2026/10/10 09:27:34
统计更新时间
2026/10/22 19:06:05

按创建时长与累计 Stars 判断等级。首次达标收录后,即使未达当前门槛仍保留;累计 Stars 和仓库状态会定期更新。确认只有 README 及辅助文件的仓库会停止公开。

模型评估

值得看加权总分 4.2 / 5

新颖度
3.9
实用价值
4.4
成熟度
4.8
实质含量
4.0

由 Jev 模型根据仓库描述与 README 评估,2026/10/17 09:31:19 生成。评分仅供参考,不是代码、安全或许可证审查;热度等级另按创建时长与累计 Stars 计算。

仓库信息

最近推送
2026/10/10 18:09:15
许可证标识
MIT
Fork 数
14
当前状态
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