math-modeling

MM-Skill: Universal Mathematical Modeling Plugin for Claude Code

A Claude Code skill plugin for full-pipeline mathematical modeling, designed for all major math modeling competitions.

Supported Competitions

Competition	Abbr	Duration	Notes
Mathematical Contest in Modeling	MCM	4 days	English paper
Interdisciplinary Contest in Modeling	ICM	4 days	English paper
全国大学生数学建模竞赛	CUMCM	3 days	Chinese paper
MathorCup 高校数学建模挑战赛	MathorCup	4 days	Chinese paper
深圳杯数学建模挑战赛	SZ Cup	~2 weeks	Chinese paper
其他建模比赛	—	—	LaTeX templates extensible

Installation

Option 1: Claude Code Marketplace (Recommended)

# Add the marketplace
/plugin marketplace add 911439925/math-modeling-skill

# Install the plugin
/plugin install math-modeling@mm-skill-market

Private repo: set GITHUB_TOKEN env var first:

export GITHUB_TOKEN=ghp_your_token_here

Option 2: Git clone

# Clone to Claude Code skills directory
git clone https://github.com/911439925/math-modeling-skill.git ~/.claude/skills/math-modeling-plugin

# Windows PowerShell
git clone https://github.com/911439925/math-modeling-skill.git "$env:USERPROFILE\.claude\skills\math-modeling-plugin"

Updating

Marketplace users:

/plugin update math-modeling@mm-skill-market

Git clone users:

cd ~/.claude/skills/math-modeling-plugin && git pull

Features

• Problem Analysis: Deep analysis with Actor-Critic self-improvement (independent Critic subagent)
• Modeling & Decomposition: High-level modeling solution, task splitting, DAG scheduling
• Task Solving: HMML method retrieval, formula generation, Python code execution, result verification
• Global Quality Review: Independent 5-dimension review with iterative rework (up to 3 rounds)
• Sensitivity Analysis: Systematic sensitivity and robustness testing
• Paper Generation: LaTeX source generation with competition-specific templates → PDF
• Git Versioning: Automatic workspace versioning with per-stage commits and iteration tags

Usage

Start the Pipeline

/math-model path/to/problem.pdf

Or simply describe your problem:

/math-model 2026年美赛C题

Pipeline Stages

Initialization → Stage 1 → Stage 2 → Stage 3 → Stage 3.5 → [iterate?] → Stage 4 → Final
                  ↓          ↓          ↓(auto)    ↓(subagent)              ↓
               [review]   [review]   per-task   global review           LaTeX→PDF
               [pause]    [pause]    +verify     +rework loop            [pause]

Stage	Skill	Description	Pause?
Init	math-model-command	Workspace init, git init, problem extraction	No
1	mm-analysis	Problem analysis with Actor-Critic (independent Critic)	Yes
2	mm-modeling	Modeling + task decomposition + DAG	Yes
3	mm-solving	Per-task: HMML → formulas → code → verify (auto)	No
3.5	mm-review	Global quality review (independent subagent, max 3 iterations)	On rework
4	mm-writing	LaTeX paper generation → PDF	Yes

Global Constraints

• Max 2 concurrent subagents at any time
• Git commit after every stage + task
• Iteration tags for version tracking

Output Structure

mm-workspace/
├── .git/                     # Git version history
├── 01_analysis.json          # Stage 1 output
├── 02_modeling.json          # Stage 2 output
├── 03_task_1.json ...        # Stage 3 per-task outputs (with verification)
├── 03.5_review.json          # Stage 3.5 global review
├── 04_abstract.json          # Generated abstract
├── 05_paper/                 # Stage 4 LaTeX paper
│   ├── main.tex              # Main LaTeX source
│   ├── main.pdf              # Compiled PDF
│   ├── sections/             # Paper sections
│   └── figures/              # Figures
├── code/                     # Generated Python scripts
├── data/                     # Intermediate data files
└── charts/                   # Generated visualizations

Requirements

• Python 3.10+
• numpy, pandas, scipy, matplotlib, seaborn, scikit-learn, networkx, sympy, openpyxl, statsmodels
• TeX Live or MiKTeX (for paper compilation)
• Claude Code (Claude Opus 4.6+ recommended)

Architecture

References:

• MM-Agent (NeurIPS 2025) — core pipeline design reference