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Math Modeling Agent (3-Role Pipeline)

A rigorous math-modeling-contest workflow that splits work into modeler, coder, and paper-writer roles with mandatory independent QA gates at every stage.

EducationAdvanced73642AI score 9/10Last updated: Aug 17, 2026

What it does

  • Runs math modeling problems through three separable roles: modeler → programmer → paper writer, either end-to-end or one role at a time.
  • Locks down deliverables per stage: problem-analysis report + glossary → runnable Python/MATLAB code, result tables (CSV/XLSX), at least 9 figures covering raw data, model process and results for every sub-question, plus a reproduction manifest (random seed, input SHA-256, dependency versions) → a Word paper (LaTeX + compiled PDF on request).
  • Enforces independent Subagent QA gates (M1/P1/P2/W1/W2): self-review never counts as passing, and any FAIL routes work back to the responsible role.
  • Uses progressive loading — algorithm index, PDF reader, Excel handler, paper search, figure and DOCX/LaTeX tools are read only when needed.

Who it's for

  • Students and teams preparing for math modeling competitions (MCM/ICM, CUMCM and similar).
  • Grad students and researchers who need reproducible computation plus a publishable-style write-up.
  • Data analysts who want strict consistency between code output and the figures/tables in a report.

Example uses

  1. "Solve contest problem A end to end" → analysis report → M1 gate → minimal runnable result (P1) → full computation and figures → P2 → evidence outline (W1) → final 完整论文.docx (W2).
  2. "Just help me pick models" → modeler role only: candidate model families per sub-question with justification, plus a glossary.
  3. "I already have the model — write the code and make the plots" → programmer role: Python/MATLAB scripts, raw/process/result figure sets per sub-question, and a reproduction manifest.

· · · Install guide · · ·

Try it now, no install

Paste this into Claude to use the skill without installing anything.

Read the instructions in this file and follow them to help me:
https://raw.githubusercontent.com/XiaoMaColtAI/math-modeling-skill/HEAD/dsh-plugin/math-modeling-agent/skills/math-modeling/SKILL.md

What I want: (describe your task here)

If Claude can't open the link, open it yourself and paste the contents instead.

If it works for you, download the ZIP below and install it. Then it runs on its own — no pasting each time.

Install in the Claude app (no terminal)
  1. Download the ZIP with the button below.
  2. In Claude, open Settings → Capabilities and turn on 'Code execution and file creation'. (one time)
  3. Go to Customize → Skills → + → 'Upload a skill' and upload the ZIP.
Download ZIP
Install in Claude Code

Let Claude do it — paste this into Claude Code

Install the skill I found on Claude Skill Mart.
Copy the dsh-plugin/math-modeling-agent/skills/math-modeling folder from the GitHub repo XiaoMaColtAI/math-modeling-skill into my ~/.claude/skills/xiaomacoltai-math-modeling/.
When it's done, tell me in one line what this skill can do.

Install with a command instead

git clone https://github.com/XiaoMaColtAI/math-modeling-skill.git && mkdir -p ~/.claude/skills && cp -r math-modeling-skill/dsh-plugin/math-modeling-agent/skills/* ~/.claude/skills/

This is a third-party skill. Check the source repository before installing.

  1. Open a terminal and clone the repo: git clone https://github.com/XiaoMaColtAI/math-modeling-skill.git
  2. Create the skills folder if needed: mkdir -p ~/.claude/skills
  3. Copy the skill in: cp -r math-modeling-skill/dsh-plugin/math-modeling-agent/skills/* ~/.claude/skills/
  4. Verify with ls ~/.claude/skills/math-modeling/SKILL.md.
  5. Prepare the toolchain: Python (numpy, pandas, matplotlib) or MATLAB, and a TeX distribution (TeX Live/MiKTeX) if you want the LaTeX/PDF paper.
  6. Restart Claude Code, cd into your project folder (with the problem PDF and data files), and ask: "Use the math-modeling skill to solve this problem."
  7. Note: the skill docs are in Chinese; copy 使用指南.md into your project folder first to understand the usage boundaries of the generated paper.