humanize-ai

humanize-ai is a skill for Claude Code, Codex from taxueseek/say-it-human. It costs 229 tokens per session (5,261 once invoked), scanned A, a copy of smart-search, MIT.

A tool for finding wording that feels machine-written and making it sound more like a real person's voice. It separates an artificial tone from deeper problems such as weak content and normally diagnoses without changing the text.

In plain words
What is it for?
Use it to review or revise posts, articles, and other Chinese-language writing when readers say it sounds like ChatGPT or lacks personality.
Why use it?
It helps identify formulaic phrasing, overly uniform sentences, and other signs of an artificial voice without pretending that style changes can fix missing ideas.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to review or revise posts, articles, and other Chinese-language writing when readers say it sounds like ChatGPT or lacks personality.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/taxueseek/say-it-human/humanize-ai
Install

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.

Any agent
npx skills add taxueseek/say-it-human --skill humanize-ai
Clone the repo
git clone --depth 1 https://github.com/taxueseek/say-it-human

Made for: Claude Code, Codex.

Wrote this? Show the measurements

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README.md
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Your own site
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Your own site · 80×15
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Per session 229 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 5,261 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin 89% copy Near-identical to another mod in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00229 $0.05261
Opus 5 $0.00114 $0.02631
Sonnet 5 $0.00046 $0.01052
Haiku 4.5 $0.00023 $0.00526

Measured 11d ago against content hash 0347a7d04734, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

humanize-ai 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 11d 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.

Origin

This is a copy

89% identical to smart-search — 393 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

skills/humanize-ai/SKILL.md · 344 lines

How it starts

The opening of the file, as written. The whole thing — 344 lines — stays where its author put it; the contents beside it link to each section on GitHub.

AI 味检测与消除

AI 味不是「写得不好」,是「读者感觉到背后没有真人」。但去 AI 味 ≠ 好内容。 花时间去 AI 味不如花时间把事情搞清楚。

文字洁癖(taxue):AI 味不是 AI 的问题,是用套话掩盖不确定的思考。人话三要素:带立场、带偏见、带情绪。每删一个套话,都要问:我到底想说什么?如果删完发现原文也没什么真东西,那问题不在 AI 味,在内容本身。

执行原则

  1. 默认只诊断不改。识别AI特征是帮人看清自己的文字,不是帮人伪装成人类。改写必须用户确认。
  2. 去 AI 味 ≠ 好内容。如果检测结果很干净,直接告诉用户。不要为了输出报告而硬找问题。
  3. 改写必须基于用户的偏好。每个 AI 特征背后都有一个本来想达成的目的。追问那个目的,而不是直接替换。
  4. 见感思行是正面的改写目标(taxue)。去 AI 味不只是删坏东西,更是让文字回到「见(观察)→ 感(感受)→ 思(思考)→ 行(行动)」的自然结构。

三种改写方式(用户选):

  • 自动替换:检测 → 直接改 → 质量验证 → 迭代
  • 追问驱动:检测 → 逐条追问写作意图 → 用户回答后给修改方向
  • 混合:自动替换 + 对关键特征追问意图

怎么运作:先判断AI味有多重,再选策略——轻微改几处、中等改一段、严重整篇重写、致命直接拦下。改完检查质量,不过关就回去重来,最多3轮。


怎么一步步处理

先判断内容类型(推文/公众号/学术),确定AI味的判断标准。然后快速扫一遍,数数AI味关键词命中几个。

  • 命中少于5处 → 直接改标记的位置
  • 命中5处以上 → 进入全量诊断,先找根因(3个根因能同时引发多种表现),再验证独立信号

改之前先识别作者的声音——口头禅、动情处、不完美痕迹,这些标为保护区一字不动。

遇到疑似AI味的句子,走决策树判断该不该删:在保护区?密度够?换话题还成立?是喘息句?删了会失温度?

改写时先清除明显废话,再逐段处理,最后检查人话三要素(偏见、情绪、立场)。

改完过检查:有没有编造?核心观点还在吗?有没有引入新的AI味?A/B级交付,C/D级回去重写。致命浓度直接拦住,不让改。


先判断内容类型

不同体裁对「规范表达」的容忍度不同。推文允许不完美,公众号保留结构感,学术文体别误伤。

体裁 AI 味敏感度 处理原则
个人随笔/推文/短视频文案 允许不完美,允许跑题,允许情绪化
公众号/专栏文章 保留结构感,但打破模板化
商业报告/学术文体 规范表达不是 AI 味,别误伤

体裁判断只在有明显特征时做——大部分内容默认为公众号/专栏类型。详细判断标准 → references/extended-patterns.md


快速扫描 + 全量诊断

快速扫描

扫一遍全文,数数这些关键词命中几个:

  • 结构胶水:「首先…其次…最后…」「值得注意的是」「综上所述」
  • 空洞词:「至关重要」「不可或缺」「深度赋能」「显著提升」
  • 模板句:「在当今…背景下」「随着…的发展」
  • 口水话:「如流星划过」「时不我待」
  • 交流腔:「感谢您的」「期待与您」「让我们一起」

命中少于5处 → 直接改标记的位置。命中5处以上 → 进入全量诊断。

全量诊断

19种AI味模式,有3个根因能同时引发多种表现:

  • 语气过于中性/权威 → 导致结构对称、句式整齐、RLHF模板化
  • 套话与空洞修饰 → 导致概括语言多、情绪词空洞、口水话
  • 翻译腔 → 导致RLHF模板化、虚假范围

先找根因,解决一个就能让多种表现同时消失。剩下的独立问题(缺乏细节、拔高、否定式煽情等)单独处理。不用每次过全部19种。

19 维速查表

每个维度的详细识别信号、修法、边界说明 → references/extended-patterns.md

# 来源 识别信号 修法
1 结构对称 三段式排比、段落长度一致、每段总结句 打破对称,长短不一
2 概括语言多 「总的来说」「综上所述」、抽象词替代场景 用具体数字/人物替代
3 情绪词空洞 「令人深思」「至关重要」无细节 删情绪词,换具体细节
4 句式整齐 「不仅…而且…」大量重复、句子长度一致 长句后接极短句,换口语
5 缺乏细节 没时间地点人物、「研究表明」不说哪项 加时间、数字、具体描述
6 语气权威 整篇没有「我不确定」、像教科书 加入不确定、偏见、犹豫
7 过度拔高 「历史性时刻」「分水岭」「里程碑」 保留事物本来大小
8 否定式煽情 「不仅仅是 X,而是 Y」强行升华 直接说事实
9 虚假范围 「从 X 到 Y」硬凑、「涵盖多个领域」 删范围词,说做了什么
10 翻译腔 被动语态多、中英混杂、名词化堆叠 被动改主动,短句化
11 平台套路 小红书体/抖音体/知乎体全套照搬 像那个人,但不像模板
12 RLHF 模板化 虚词密度异常、句子长度方差过低 故意打断流畅,加入停顿
13 格式型痕迹 破折号/粗体每段都有、表情符号、弯引号 克制格式,用直角引号
14 交流型痕迹 谄媚语气、免责声明、填充短语、过度限定 平等语气,直接说内容
15 口水话修辞 比喻连篇、空洞号召、套话、形容词堆砌 用数字替代比喻,删套话
16 套话修饰 「至关重要」密集、「在当今…」开头、万能句式 删掉或用数据替代
17 视角混乱 人称在「我」「我们」「你」之间漂移 确定稳定视角,全篇保持
18 伪逻辑连接 「因此」「所以」连接无因果关系的分句 检查因果,删假因果词
19 数字伪造 精确到小数但无来源、无上下文 有来源保留,无来源改模糊

Read the full file on GitHub · 344 lines

Changes

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.

  1. 11d ago First seen · 344 lines · 229 tokens per session scan A 0347a7d04734

Subscribe to this mod's changes

humanize-ai is a skill published in the GitHub repository taxueseek/say-it-human (65 stars, last pushed 23d ago), licensed MIT. It adds 229 tokens to every session and 5,261 once invoked, about $0.0011 per session on Opus 5. A static security scan graded it A with 0 findings. It is 89% identical to smart-search, differing in 393 lines, and is treated as a copy.