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/datasetnpx skills add woodfishhhh/EZ_math_model --skill datasetgit 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/dataset)<a href="https://agentmods.dev/skills/woodfishhhh/ez_math_model/dataset"><img src="https://agentmods.dev/badge/skills/woodfishhhh/ez_math_model/dataset.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 5.1 | $0.00054 | $0.00654 |
| Opus 5 | $0.00027 | $0.00327 |
| Sonnet 5 | $0.00011 | $0.00131 |
| Haiku 4.5 | $0.00005 | $0.00065 |
Grade A, and why
dataset 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 6d 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
dataset — 公开数据集发现
何时使用
- 题目附件没有数据集，但题面要求"查找类似公开数据"。
- 需要历史基准数据集（如 MNIST / Iris / 波士顿房价）做模型对比。
- 不要在已有附件数据时启用本子 skill。
入口
| 数据源 | 入口 | 配置 |
|---|---|---|
| Kaggle | kaggle datasets list -s <kw> / kaggle datasets download -d <user/name> |
~/.kaggle/kaggle.json（注册免费下载） |
| UCI ML | 直接 HTTPS pd.read_csv(url) |
无 |
| HuggingFace | from datasets import load_dataset |
EZMM_HF_TOKEN（私有数据集） |
| 天池 | 浏览器 + 手动下载 | 无 |
命令模板
# Kaggle 搜索 + 下载
kaggle datasets list -s "vegetable retail price"
kaggle datasets download -d <user/dataset-name> -p workdir/.../attachments/external/kaggle --unzip
# HuggingFace
python -c "from datasets import load_dataset; ds = load_dataset('squad', split='train[:1%]')"
# UCI（直接 URL）
python -c "import pandas as pd; df = pd.read_csv('https://archive.ics.uci.edu/...'); df.to_csv('workdir/.../attachments/external/uci/iris.csv', index=False)"
落盘规范
外部下载的数据放在：
workdir/{task_id}/attachments/external/<source>/<dataset>/
并在同目录写 SOURCES.md：
- 数据集名: <name>
- 来源: <kaggle url>
- License: <CC0 / CC-BY / Apache-2.0 / 其他>
- 下载时间: <ISO timestamp>
- 用途说明: <一句话>
失败诊断
| 情况 | 处理 |
|---|---|
| Kaggle token 未配置 | 提示用户 ~/.kaggle/kaggle.json 配置 |
| License 不允许商用 | 数据可用于学术建模报告；论文中标明出处 + license |
| 文件 > 1GB | coder 阶段用 chunksize 处理（参考 prompts/coder.md） |
| 网络受限 | 写诊断；建议用户手动下载放入 attachments/ |
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.
- 6d ago First seen · 68 lines · 54 tokens per session scan A c7b68034c9ee
dataset is a skill published in the GitHub repository woodfishhhh/EZ_math_model (40 stars, last pushed 1mo ago), licensed MIT. It adds 54 tokens to every session and 654 once invoked, about $0.0003 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
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mathmodel-skill
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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。.
agent-platform-rag-engine-management
Manage and query Agent Platform RAG Engine Corpora and retrieve grounded contexts using the Google GenAI SDK. Use when listing RAG corpora or files, inspecting a corpus, retrieving contexts, or generating content grounded in a RAG corpus. Do not use for standard database queries (use SQL/Spanner skills), Google…
agent-platform-model-registry
Agent Platform Model Registry Management. Use when you need to upload, list, describe, update, or delete machine learning models (and their versions) in the Agent Platform Model Registry. Don't use for model training, model deployment to endpoints, or managing non-Agent Platform models.