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公开数据集发现入口。覆盖 Kaggle / UCI / HuggingFace / 天池。题目要求"自行 查找数据"或"补充外部数据"时启用；附件已含数据时**不调用**。
Installer avec Codex ou Claude Copiez ce prompt, collez-le dans Codex, Claude ou un autre assistant, puis laissez-le vérifier la page du skill et l'installer pour vous.
Basé sur la classification professionnelle SOC
Step 5 of the PaperOrchestra pipeline (arXiv:2604.05018). Iteratively refine drafts/paper.tex by simulating peer review and applying targeted revisions, with strict accept/revert halt rules. Maintains a worklog and snapshots each iteration so revert is real, not symbolic. TRIGGER when the orchestrator delegates Step 5 or when the user asks to "refine the draft", "iterate on the paper", or "run peer review on this paper".
Step 3 of the PaperOrchestra pipeline (arXiv:2604.05018). Execute the literature search strategy from outline.json — discover candidate papers via web search, verify them through Semantic Scholar (Levenshtein > 70 fuzzy title match, temporal cutoff, dedup by paperId), build a BibTeX file, and draft Introduction + Related Work using ≥90% of the verified pool. Runs in parallel with the plotting-agent. TRIGGER when the orchestrator delegates Step 3 or when the user asks to "find citations for my paper", "draft the related work", or "build the bibliography".
Step 1 of the PaperOrchestra pipeline (arXiv:2604.05018). Convert (idea.md, experimental_log.md, template.tex, conference_guidelines.md) into a strict JSON outline containing a plotting plan, literature search plan (Intro + Related Work), and section-level writing plan with citation hints. TRIGGER when the orchestrator delegates Step 1 or when the user asks to "outline a paper from raw materials" or "generate the paper structure".
Orchestrate the full PaperOrchestra (Song et al., 2026, arXiv:2604.05018) five-agent pipeline to turn unstructured research materials (idea, experimental log, LaTeX template, conference guidelines, optional figures) into a submission-ready LaTeX manuscript and compiled PDF. TRIGGER when the user asks to "write a paper from my experiments", "turn this idea and these results into a paper", "generate a conference submission", "run paper-orchestra on X", or otherwise wants the end-to-end paper-writing pipeline. Coordinates the outline-agent, plotting-agent, literature-review-agent, section-writing-agent, and content-refinement-agent skills.
Use when solving CUMCM, MCM, ICM, or other mathematical modeling tasks that need contest problem intake, model selection, Python solving, figures, paper writing, quality audit, and packaged deliverables.
Use when EZ_math_model needs to convert paper.md to paper.docx, read a DOCX problem statement, preserve equations and tables, or package a modeling report as a Word document.
| name | dataset |
| description | 公开数据集发现入口。覆盖 Kaggle / UCI / HuggingFace / 天池。题目要求"自行 查找数据"或"补充外部数据"时启用；附件已含数据时**不调用**。 |
| read_when | ["题目明确要求查找类似公开数据","需要历史基准数据集做对比"] |
| 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 |
# 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)"
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>
| 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/ |