XiaoMaColtAI/math-modeling-skill
数学建模技能 - 面向 CUMCM、MCM/ICM 等数学建模竞赛的三阶段工作流:建模分析、Python/MATLAB 编程与 DOCX 论文生成。包含丰富的算法资源库(优化/预测/评价/图论/机器学习等)、角色指导文档、论文模板和实用工具脚本
What it solves
This project provides a structured, three-stage workflow for mathematical modeling competitions and projects. It solves the problem of fragmented processes by coordinating the transition from initial problem analysis and model design to code implementation and final academic paper generation, ensuring that results are reproducible and visually professional.
How it works
The system operates through three specialized roles that can be executed sequentially or independently:
- Modeling Analyst: Analyzes the problem, breaks down sub-questions, selects appropriate models, and designs verification plans.
- Programmer: Implements the model using Python or MATLAB, generates data tables, and creates scientific visualizations.
- Paper Writer: Constructs the final argument based on the results and generates the paper in Word or LaTeX format.
To ensure quality, the workflow incorporates "quality gates" (independent check-points) and optional sub-agents for specific tasks like literature research or rule verification. It includes a suite of integrated tools for academic paper searching (via OpenAlex and AnySearch), scientific plotting, and document conversion.
Who it’s for
It is designed for participants in mathematical modeling competitions (such as CUMCM, MCM/ICM, APMCM) and researchers or students working on general mathematical modeling projects.
Highlights
- Three-Stage Pipeline: A rigorous flow from analysis $\rightarrow$ implementation $\rightarrow$ writing.
- Dual-Language Support: Native support for both Python and MATLAB with dynamic dependency checking.
- Publication-Quality Visuals: Tools for high-DPI, color-blind friendly, scientific plotting (SVG/PNG).
- Academic Tooling: Integrated dual-engine paper search and support for official competition templates in Word and LaTeX.
- Reproducibility: Automatic recording of random seeds, file hashes (SHA-256), and environment versions.
- Agent Integration: Ready-to-use presets for DeepSeek Harness and compatible with AI coding assistants like Cursor and Claude Code.
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