Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add ItamarZand88/awesome-agent-conventions --skill remove-ai-flavorgit clone --depth 1 https://github.com/ItamarZand88/awesome-agent-conventionsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/itamarzand88/awesome-agent-conventions/remove-ai-flavor)<a href="https://agentmods.dev/skills/itamarzand88/awesome-agent-conventions/remove-ai-flavor"><img src="https://agentmods.dev/badge/skills/itamarzand88/awesome-agent-conventions/remove-ai-flavor/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/itamarzand88/awesome-agent-conventions/remove-ai-flavor"><img src="https://agentmods.dev/badge/skills/itamarzand88/awesome-agent-conventions/remove-ai-flavor.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00000 | $0.01853 |
| Opus 5 | $0.00000 | $0.00927 |
| Sonnet 5 | $0.00000 | $0.00371 |
| Haiku 4.5 | $0.00000 | $0.00185 |
Grade A, and why
remove-ai-flavor scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 151 lines — stays where its author put it; the contents beside it link to each section on GitHub.
name: remove-ai-flavor description: 去除 AI 味道的文章风格优化技能。用于识别并改写文章、公众号稿、自媒体稿、口播稿、演讲稿、课程稿、产品文案中的 AI 痕迹、模板腔、资料味、翻译腔、空洞大词、过度金句、破折号滥用、bullet 堆叠、动不动加粗等问题;当用户说“去 AI 味”“去除 AI 痕迹”“不像 AI 写的”“更像人写的”“更自然”“别太机器味”“去掉模板感”“改得像公众号终稿”时使用。不用于事实核查、从零选题策划、论文转公众号、纯标题生成或追求 AI 检测器通过率。
去除 AI 味道
目标
把内容从“正确但像 AI 总结”改成“自然、具体、像人在认真讲一件事”。
优先保留作者的观点、判断、材料和语气,不把文章改成另一种人格。这个 skill 不是为了追求“检测器通过”,而是为了提升真实读者的阅读感。
使用边界
如果用户没有提供原文、文件路径或明确的待改片段,先索要文本,不要凭空示范。
这个 skill 只处理表达质感和读者阅读感,不负责事实核查、资料补充、选题策划、标题生成或平台格式排版。遇到事实可疑、数据缺来源、观点缺证据时,保留原意并提醒用户需要另行核查。
判断模式
先判断用户要哪种处理深度:
诊断模式:用户说“看看哪里有 AI 味”“先审一下”“帮我标出来”。只输出问题清单和改写建议,不直接重写全文。轻改模式:文章基本可用,只是有少量套话、硬句、破折号、冗余连接。做最小改动。深改模式:文章像资料整理稿、翻译稿、AI 草稿或提纲稿。需要重写开头、过渡、小标题、结尾和部分段落。终稿模式:用户明确要“可发布版本”“公众号终稿”“直接改好”。输出完整优化稿,并说明关键修改。
如果用户没有说明,默认使用 轻改模式;如果原稿明显不像成稿,升级到 深改模式 并说明原因。
Gotchas
- 不要为了“像人写”而加入夸张情绪、口水话或油腻表达。
- 不要为了顺滑改掉作者立场、删掉关键限定条件,或发明原文没有的案例和结论。
- 不要把专业文章改成鸡汤、营销腔或段子文。
- 不要承诺可以绕过 AI 检测器;目标是让真实读者读起来更自然。
- 不要把所有列表都强行改成自然段;真正方便扫描的列表要保留。
工作流程
复制此清单并跟踪进度:
去 AI 味进度:
- [ ] 步骤 1:判断处理深度
- [ ] 步骤 2:保留核心观点、事实和作者语气
- [ ] 步骤 3:识别 AI 味、资料味、翻译腔和模板句
- [ ] 步骤 4:按模式输出诊断、局部改写或完整终稿
- [ ] 步骤 5:自查自然度、具体性、节奏和可发布性
1. 先保留骨架
改写前先抓住这些内容:
- 文章最核心的一句话
- 必须保留的事实、数据、案例、术语
- 作者原本的立场和语气
- 读者为什么要读完
不要为了顺滑删掉关键细节。不要擅自加入原文没有支持的新结论。
2. 再清理味道
优先处理这些高频问题:
- 套路开场:在这个时代、随着技术发展、众所周知
- 生硬连接:综上所述、基于此、由此可见、值得一提的是
- 空洞大词:卓越、革命性、颠覆性、重要意义、巨大价值
- 假金句:听起来很猛,但没有信息增量
- 破折号滥用:用
——强行制造解释和转折 - bullet 堆叠:能写成自然段的内容,被硬拆成列表
- 加粗过多:每段都在强调,反而没有重点
- 翻译腔:语序像英文,抽象名词多,动作词少
- 判词腔:用“不是免费的 X 来源”“必须看起来 Y”这类负向定义、制度化判断表达观点,读起来像模型在下结论
- 资料味:元信息堆在开头,像笔记、讲义、PPT 备注
详细识别表和替换策略见 references/anti-ai-flavor-rules.md。当文章问题较多、需要系统处理时读取该文件。
3. 改写原则
- 删除套话,直接切入主题。
- 用具体场景、动作、数据替代抽象形容词。
- 把负向定义改成正面判断或更像人会说的动作句,例如把“不是免费的可靠性来源”改成“可靠性没有那么简单”。
- 把长句拆短,让一句话只说一个主要意思。
- 用自然承接替代机械连接词。
- 少用排比,避免每段都像总结。
- 尽量不用
不是……而是……、不仅……而且……更……这类模板句式。 - 尽量不用破折号;改用冒号、逗号、句号、括号,或重排句子。
- 需要列表时保留列表;不需要时改回自然段。
- 对技术、产品、行业文章,保持准确克制,不改成鸡汤。
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 12d ago First seen · 151 lines · 0 tokens per session scan A 60ae233babb6
remove-ai-flavor is a skill published in the GitHub repository ItamarZand88/awesome-agent-conventions (31 stars, last pushed 1mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,853 tokens. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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