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 woodfishhhh/EZ_math_model --skill humanizergit clone --depth 1 https://github.com/woodfishhhh/EZ_math_modelWrote 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/woodfishhhh/ez_math_model/humanizer)<a href="https://agentmods.dev/skills/woodfishhhh/ez_math_model/humanizer"><img src="https://agentmods.dev/badge/skills/woodfishhhh/ez_math_model/humanizer/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/woodfishhhh/ez_math_model/humanizer"><img src="https://agentmods.dev/badge/skills/woodfishhhh/ez_math_model/humanizer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00039 | $0.00491 |
| Opus 5.5 | $0.00016 | $0.00196 |
| Sonnet 5.5 | $0.00008 | $0.00098 |
| Haiku 4.5 | $0.00004 | $0.00049 |
Grade A, and why
humanizer 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 1mo 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.
What it actually says
humanizer — 论文正文去 AI 味
何时使用
- writer 已落盘
paper.md。 quality_audit尚未运行。- 正文长度通常不少于 5000 字。
- 用户或配置要求降低 AI 写作痕迹。
调用方式
优先加载宿主 humanizer skill。输入 paper.md 正文，要求：
- 保留所有数值。
- 保留引用编号。
- 保留公式块和行内公式。
- 保留 Markdown 表格。
- 保留图片标签
。 - 不修改章节标题。
默认写到：
workdir/{task_id}/paper.humanized.md
workdir/{task_id}/humanizer_diff.md
只有用户显式开启 --humanize-overwrite 时才覆盖 paper.md。
结构化 diff 硬门
humanizer 后必须生成 humanizer_diff.md，并至少比较以下集合：
- 数值集合；
- 引用编号集合；
- 公式块 hash 集合；
- 图片引用集合；
- Markdown 表格数量；
- 一级/二级标题集合。
任一集合变化时，不得覆盖 paper.md；只能保留 paper.humanized.md，并在
diagnostics.md 中记录。quality audit 必须检查实际打包版本，不能审原文却打包
humanized 版本。
检查重点
- 夸张象征语和营销式措辞。
- 空泛转折和堆叠连接词。
- 过度三段式。
- 模糊归因。
- 频繁使用 em dash 或英文 AI 高频词。
失败诊断
| 情况 | 处理 |
|---|---|
| 宿主 skill 未安装 | 跳过，不阻塞 packaging |
| 数值、公式、引用被改坏 | 回滚到原 paper.md |
| 字数明显缩水 | quality_audit 标记字数风险 |
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.
- 1mo ago First seen · 65 lines · 39 tokens per session scan A 3e4e14be6655
humanizer is a skill published in the GitHub repository woodfishhhh/EZ_math_model (43 stars, last pushed 2mo ago), licensed MIT. It adds 39 tokens to every session and 491 once invoked, about $0.0002 per session on Opus 5.5. 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.
Other skills, from other repositories
mathmodel-skill
An end-to-end workflow for mathematical-modeling competitions, including CUMCM, MCM/ICM, and the Electrical Cup. It guides teams from choosing a problem through modeling, solving, checking, writing, rule compliance, and final review.
math-modeling-solver
A Chinese-language guide for solving mathematical modeling competition problems. It covers China’s CUMCM and America’s MCM/ICM, where teams use mathematics and code to answer real-world problem statements.
math-modeling-paper
A Chinese-language guide for writing papers for mathematical modelling competitions, where teams use mathematics and data to study a real-world problem.
interpret-modeling-problems
A method for turning a mathematical modelling contest problem and its attachments into a checked plan for solving and documenting it.
mcm-model-typeset
A set of writing and formatting rules for the model-building and solution section of a Chinese mathematical modelling paper. It defines section structure, equation explanations, constraints, and model summaries without changing the supplied numbers or formulas.
math-modeling-finalizer
A finalization guide for mathematical-modeling projects after the main results are largely fixed.