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 agentmods add skills/woodfishhhh/ez_math_model/humanizernpx 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.svg" alt="Measured on agentmods" 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 | $0.00039 | $0.00491 |
| Opus 5 | $0.00019 | $0.00246 |
| Sonnet 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 5d 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.
- 5d 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 (40 stars, last pushed 1mo ago), licensed MIT. It adds 39 tokens to every session and 491 once invoked, about $0.0002 per session on Opus 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
math-modeling-solver
数学建模竞赛解题全流程指导。覆盖国赛(CUMCM)和美赛(MCM/ICM)全部题型(A-F)，提供12种问题本质分析、95+场景模型决策矩阵、5本算法Cookbook、11本完整例题Playbook、22个Python+7个MATLAB可运行代码模板。与math-modeling-paper形成"解题→写作"配对。当用户提及建模思路、选什么模型、怎么建模、赛题求解、粘贴赛题文本、美赛/国赛题目分析、CVaR/NSGA-II/Monte Carlo/时间序列/ANOVA/灰色关联、网络流/图论/生态建模、模型命名/Memo/Letter/Our Work流程图时，使用此skill。.
mathmodel-skill
CUMCM 国赛、MCM/ICM 美赛与电工杯数学建模竞赛的端到端协作工作流。Use when a user explicitly works on one of these modeling contests or asks to run/review a modeling-competition paper from problem selection through modeling, solving, robustness, writing, compliance, and final submission review. Provides 10 stages, persistent decision state…
math-modeling-paper
数学建模竞赛论文写作全流程指导。覆盖国赛(CUMCM)和美赛(MCM/ICM)，从论文结构规划、各章节撰写、模型检验、参考文献规范到最终格式检查。与math-modeling-solver形成"解题→写作"配对——可接收solver输出的论文草稿片段直接展开写作。当用户提及数学建模论文写作、建模比赛、国赛/美赛/电工杯/亚太杯/深圳杯/华为杯论文、CUMCM、MCM/ICM、数模论文结构、摘要写作、模型检验、灵敏度分析、latex建模模板、word建模排版、Memo/Letter写作、模型命名、Our Work流程图，或需要写/修改/优化/检查建模论文的任何部分时，都必须使用此 skill。.
interpret-modeling-problems
根据原始赛题和附件，生成可回查证据的国赛及类似数学建模赛题解读，建立小问输入输出、约束与歧义、附件审计、模型蓝图、验证方案和论文交付接口。适用于选题比较、正式建模前的完整解读和已有解读复核；不用于在缺少原题时臆测题意，也不把候选模型伪装成已经验证的数值求解。.
1start-mathmodel
数学建模国赛（CUMCM）工作流入口。用于启动完整建模流程：询问用户偏好，生成 plan.md 和 todo.md，并按阶段调用赛题分析、建模、代码与图表、流程图、论文撰写、验证验收等 skills。.
math-modeling-finalizer
数学建模项目收口与终审 Skill。用于正式结果已基本冻结后的参赛者尺度代码简化与行为回归、最终完整复审、AI 工具使用详情 DOCX、Word/PDF 排版检查、支撑材料 ZIP 与提交前卫生检查。典型请求包括“结果已经定了，把代码改得更像学生项目”“最后完整审一遍”“检查最终 Word/PDF”“生成 AI 使用详情和支撑材料”。若需要重新建模、修改 evaluator 或正式结果，路由回 math-modeling-solver；正文、摘要、引用语义或公式写作问题路由回 math-modeling-paper；提交后的项目复盘/教学交给 math-modeling-growth。.