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 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/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/dataset"><img src="https://agentmods.dev/badge/skills/woodfishhhh/ez_math_model/dataset.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 | $0.00054 | $0.00654 |
| Opus 5.5 | $0.00022 | $0.00262 |
| Sonnet 5.5 | $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 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
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.
- 1mo 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 (43 stars, last pushed 2mo ago), licensed MIT. It adds 54 tokens to every session and 654 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.
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.
foundry-config-setup
Resolve missing setup caused by a hardcoded Foundry project endpoint or model in a sample. Use when a sample fails because it uses a placeholder/hardcoded projectendpoint (for example "https://your-project.services.ai.azure.com") or a hardcoded model instead of reading them from the environment.