No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by B1lli · Codex Skill · ★ 197
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
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remove-ai-flavor-writing-skill 一个中文去 AI 味写作 Skill。它不是把文字改得更花，而是清理那些一眼像模型生成的表达外壳：、、、、、讲义腔路标词、冒号模板、段落同构，以及结尾突然抛给读者的假互动问题。 默认原则很窄：保留原意、事实、语气和文体，只去掉让读者意识到“这是模型在组织答案”的结构痕迹。内部 skill 名是 。 快速使用 把本仓库复制到 Codex skills 目录： 也可以直接从 GitHub 安装： 重启 Codex 后，用下面这类说法触发： 它会清理什么 二分对照壳：、、 机械顺序壳：、、 本质拔高壳：、、、 助手路标词：、、、 总结讲义腔：、、、 收束开场和简单答案壳：、 假互动结尾：、、 段落同构：连续几段都是“观点句 + 解释 + 段尾总结”，或者排比太密、段落长度太均匀 已测试场景 小红书/社媒文案：清理机械步骤、抽象拔高、结尾假互动。 公众号/长文评论：清理讲义腔、二分对照壳、段落同构。 邮件/评论回复：清理助手路标词、虚假客气、莫名其妙的结尾提问。 中文小说正文：清理套话反应、抽象评论、无必要的 和 。 学术/技术短文：清理填充短语、泛化结论、整齐编号逻辑、模糊权威。 过度整齐的短文：打散 、、 和密集排比。 待办/未验证 Claude Code / OpenCode 兼容性：规则是纯 Markdown，理论上可迁移，但尚未在这两个环境里完整安装测试。 长篇整稿批量处理：当前只做短样本和片段级 fixture，未承诺整本书或整篇论文自动改完。 AI 检测器
| Stars | 197 |
| Forks | 7 |
| Language | Python |
| Category | Codex Skill |
| License | MIT |
| Quality Score | 64.293352698479/100 |
| Open Issues | 1 |
| Last Updated | 2026-06-10 |
| Created | 2026-06-06 |
| Platforms | claude-code, codex, python |
| Est. Tokens | ~3k |
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remove-ai-flavor-writing-skill is 去AI味 skill for Codex: a compact Chinese AI writing humanizer for AI味清理, template sentence shells, and fake engagement endings.. It is categorized as a Codex Skill with 197 GitHub stars.
remove-ai-flavor-writing-skill is primarily written in Python. It covers topics such as ai-flavor, ai-humanizer, ai-writing.
You can find installation instructions and usage details in the remove-ai-flavor-writing-skill GitHub repository at github.com/B1lli/remove-ai-flavor-writing-skill. The project has 197 stars and 7 forks, indicating an active community.
remove-ai-flavor-writing-skill is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to remove-ai-flavor-writing-skill on Agent Skills Hub include humanize, patina, humanizer-skill. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.