ez-math-model

Ingest contest problem statements and attachments to produce modeling papers and packaged deliverables.

32|1|Updated May 19, 2026
One-click install
npx skills add https://github.com/woodfishhhh/EZ_math_model --skill ez-math-model
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ez-math-model
Source: https://github.com/woodfishhhh/EZ_math_model/tree/main/skills/ez-math-model
Command: npx skills add https://github.com/woodfishhhh/EZ_math_model --skill ez-math-model

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires python, pandoc, zipfile, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill turns contest math modeling tasks into a complete, submission-ready workflow: it ingests the problem statement and attachments, selects an appropriate modeling approach, runs Python-based solving and plotting, writes the modeling paper, performs quality audits, and packages all deliverables.

Core Features & Use Cases

  • Contest-ready end-to-end pipeline: From intake and model planning to coding, figure generation, paper writing, quality gates, and final artifact packaging (output.zip).
  • Formal vs demo vs blocked safety: Enforces run-mode rules to prevent silent synthetic data in formal results and blocks packaging when required inputs are missing.
  • Evidence-based quality control: Uses deterministic audit scripts to verify paper structure, chart manifest semantics, exported DOCX/PDF object integrity, and artifact consistency.
  • Contest-oriented intelligence: Detects contest signals (CUMCM/MCM/ICM/grad contests, year, problem letter), selects modeling strategies, and generates figures with manifest-based gating.

Quick Start

Use ez-math-model when you have a contest math modeling statement and its files by telling the agent: 用 ez-math-model 做这道数学建模题。把题面和数据附件一起发给它。

Frequently Asked Questions about ez-math-model

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I automate mathematical modeling paper writing and figure generation for contests?▼

Automating mathematical modeling paper writing involves ingesting problem statements, executing Python-based analysis, generating figures, and packaging deliverables. This workflow enforces chart manifest quality gates and paper format discipline to produce submission-ready DOCX/PDF outputs for math modeling contests.

Can I generate Python plots and write the modeling paper for CUMCM or MCM contests in one workflow?▼

Yes, you can generate Python plots and write the modeling paper for CUMCM or MCM contests in a single end-to-end workflow. The pipeline detects contest signals, selects modeling strategies, runs Python solving, produces figures, and writes the paper while enforcing formal and demo run-mode rules.

What is the best way to package contest math modeling deliverables into a submission-ready format?▼

Packaging contest math modeling deliverables requires deterministic pre/post-export audits for DOCX/PDF object integrity and artifact consistency. The workflow bundles the modeling paper, Python analysis scripts, and generated figures into a final output.zip while blocking packaging if required inputs are missing.

How does a quality audit work for mathematical modeling papers and generated figures?▼

A quality audit for mathematical modeling papers uses deterministic scripts to verify paper structure, chart manifest semantics, and exported DOCX/PDF object integrity. This evidence-based quality control prevents silent synthetic data in formal results and ensures artifact consistency before final submission.

Do I need Python and pandoc to run an automated math modeling contest workflow?▼

Yes, you need Python for numerical solving and plotting, and pandoc for document format conversion within an automated math modeling contest workflow. These dependencies support the end-to-end pipeline from model planning and Python-based analysis to DOCX/PDF paper export and artifact packaging.

Why does my math modeling workflow block packaging when required inputs are missing?▼

A math modeling workflow blocks packaging when required inputs are missing to enforce formal run-mode safety. This mechanism prevents silent synthetic data in formal results and ensures artifact consistency by running deterministic pre/post-export audits before generating the final deliverables.