A Claude Code skill plugin for full-pipeline mathematical modeling, designed for all major math modeling competitions.
Current version: v0.4.4 — Pipeline state machine, schema enforcement, automated validation scripts.
| 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 |
# Add the marketplace
/plugin marketplace add 911439925/math-modeling-skill
# Install the plugin
/plugin install math-modeling@mm-skill-marketPrivate repo: set GITHUB_TOKEN env var first:
export GITHUB_TOKEN=ghp_your_token_here# 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"Marketplace users:
/plugin marketplace update mm-skill-market
/plugin install math-modeling@mm-skill-marketGit clone users:
cd ~/.claude/skills/math-modeling-plugin && git pull- Problem Analysis: Deep analysis with Actor-Critic self-improvement (independent Critic subagent)
- Modeling & Decomposition: High-level modeling solution, task splitting (3-6 subtasks), DAG scheduling
- Task Solving: HMML method retrieval (98 methods, 5 domains), formula generation, Python code execution, result verification
- Global Quality Review: Independent 6-dimension review with iterative rework (up to 3 rounds, 80-point threshold)
- Sensitivity Analysis: Systematic sensitivity and robustness testing (REQUIRED/RECOMMENDED/OPTIONAL priority)
- Paper Generation: LaTeX source generation with competition-specific templates (MCM/CUMCM/generic) → PDF
- Pipeline State Machine:
pipeline_state.jsonenforces stage transitions — no stage can be skipped - Schema Validation: Automated task output validation (
validate_task_output.py) with backfill - Cross-Task Consistency: Automated metric conflict and value chain checks (
cross_task_consistency.py) - Git Versioning: Automatic workspace versioning with per-stage commits and iteration tags
/math-model path/to/problem.pdf
Or simply describe your problem:
/math-model 2026年美赛C题
Init → Stage 1 → Stage 2 → Stage 3 → Stage 3.5 → [iterate?] → Stage 4 → Final
↓ ↓ ↓(auto) ↓(mandatory) ↓
[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, pipeline state init | No |
| 1 | mm-analysis | Problem analysis with Actor-Critic (independent Critic, 75-point threshold) | Yes |
| 2 | mm-modeling | Modeling + task decomposition + DAG (75-point threshold) | Yes |
| 3 | mm-solving | Per-task: HMML → formulas → code → schema validate → verify | No |
| 3.5 | mm-review | Global quality review (independent subagent, 80-point threshold, max 3 iterations) | On rework |
| 4 | mm-writing | LaTeX paper generation → PDF (requires Stage 3.5 passed) | Yes |
| Gate | Mechanism | Threshold |
|---|---|---|
| Stage 1 Actor-Critic | Independent Critic subagent scores the analysis | 75/100 |
| Stage 2 Actor-Critic | Independent Critic subagent scores the modeling | 75/100 |
| Per-task verification | Independent verification subagent checks each task result | Pass/Fail |
| Schema validation | validate_task_output.py checks required JSON fields |
All required fields present |
| Cross-task consistency | cross_task_consistency.py checks metric conflicts + value chains |
No conflicts |
| Global review | Independent review subagent, 6 dimensions, iterative rework | 80/100 |
| Stage 4 precondition | pipeline_state.json must show stage_3_5_passed: true |
Hard gate |
mm-workspace/
├── .git/ # Git version history
├── pipeline_state.json # Pipeline state machine (v0.4.4)
├── 01_analysis.json # Stage 1 output
├── 02_modeling.json # Stage 2 output (with DAG)
├── 03_task_1.json ... # Stage 3 per-task outputs (with verification)
├── 03.5_review.json # Stage 3.5 global review
├── 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
- 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)
plugins/math-modeling/
├── .claude-plugin/plugin.json # Plugin metadata
├── scripts/ # Automation scripts
│ ├── validate_task_output.py # Task JSON schema validation
│ └── cross_task_consistency.py # Cross-task consistency checks
├── skills/
│ ├── math-modeling/ # Main orchestrator + references
│ │ ├── SKILL.md # Pipeline definition, state machine, stage guards
│ │ └── references/ # HMML index, Actor-Critic guide, templates, etc.
│ ├── math-model-command/ # /math-model entry point
│ ├── mm-analysis/ # Stage 1: Problem analysis
│ ├── mm-modeling/ # Stage 2: Modeling & decomposition
│ ├── mm-solving/ # Stage 3: Task solving (subagent dispatch)
│ ├── mm-review/ # Stage 3.5: Global quality review
│ └── mm-writing/ # Stage 4: Paper generation
| Concept | Implementation |
|---|---|
| Pipeline enforcement | pipeline_state.json state machine with stage transition guards |
| Method retrieval | HMML knowledge base (98 methods, 5 domains) with on-demand loading |
| Self-improvement | Actor-Critic with independent Critic subagent + adaptive 1-3 rounds |
| Quality assurance | Per-task verification subagent + schema validation + global review |
| Cross-task integrity | cross_task_consistency.py + known_issues propagation via dispatch prompts |
| Error recovery | Fix verification loop (max 1 re-attempt) + _schema_incomplete fallback |
| Paper generation | LaTeX templates (MCM/CUMCM/generic) → PDF compilation |
- MM-Agent (NeurIPS 2025) — core pipeline design reference
- Pipeline state machine (
pipeline_state.json) with stage transition guards - Task output schema validation script (
validate_task_output.py) - Cross-task consistency check script (
cross_task_consistency.py) - Stage 3.5 hard precondition in mm-writing (4-point verification)
- Verification results standardized (embedded in task JSON)
- Known issues propagation between subagents via dispatch prompts
- Git commit/tag enforcement policy
- Parallel dispatch fallback (sequential acceptable)
- Sensitivity analysis test priority (REQUIRED/RECOMMENDED/OPTIONAL)
- Fix verification loop (max 1 re-attempt per issue type)
- Per-task verification gate enforcement
- Git operations removed from subagents (main agent only)
- Methodological rigor enhancements from MCM C practice
- Actor-Critic percent-based scoring system
- Subagent dispatch, independent verification, chart review, mandatory Stage 3.5
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