comp-modeling

Generates MODELING.md files from contest problem analyses and user data with validation plans.

1|Updated May 14, 2026
One-click install
npx skills add https://github.com/lix965996-art/MMM --skill comp-modeling
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: comp-modeling
Source: https://github.com/lix965996-art/MMM/tree/main/resources/app/skills/comp-modeling
Command: npx skills add https://github.com/lix965996-art/MMM --skill comp-modeling

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This skill turns contest mathematics modeling tasks into a complete, solver-ready workflow that covers assumptions, sub-problem modeling, formulas, algorithms, and validation plans for accurate implementation.

Core Features & Use Cases

  • Contest modeling blueprint: Converts a given contest analysis report into structured modeling sections with formulas and algorithm designs for each sub-problem.
  • Method alignment and anti-error guardrails: Forces method-choice justification against the recommended approach and requires checking the matching error-prevention handbook per problem type.
  • Implementation-ready model outputs: Produces a MODELING_REPORT.md that includes assumptions, symbol definitions, validation/sensitivity plans, constraints checklists, and a fixed method specification to prevent comp-code ambiguity.

Quick Start

Use the comp-modeling skill to generate MODELING_REPORT.md based on PROBLEM_ANALYSIS.md and the attached user_data for your math modeling competition task.

Frequently Asked Questions about comp-modeling

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

FAQPage Schema
How do I convert a mathematical modeling contest problem analysis into a solver-ready model?▼

A mathematical modeling workflow requires aligning methods with the recommended approach and checking an error-prevention handbook per problem type. This ensures method-choice justification and prevents implementation ambiguity through parameterized assumptions and formal constraints.

What's the best way to prevent coding errors when implementing mathematical models from competitions?▼

Yes, the mathematical modeling workflow handles multiple sub-problems by converting the contest analysis into structured modeling sections. It applies method alignment and formula designs independently across each sub-problem within the final MODELING_REPORT.md.

How do I structure a math modeling report for algorithm design and validation?▼

Structuring a math modeling report requires organizing assumptions, symbol definitions, and algorithm designs into an execution-ready MODELING_REPORT.md. This enforces YAML metadata requirements and includes validation checkpoints plus sensitivity plans.

When do I need formal constraints and parameterized assumptions in mathematical modeling?▼

You need formal constraints and parameterized assumptions in mathematical modeling when preparing contest problems for coding. They ensure the resulting MODELING_REPORT.md is execution-ready and prevents implementation ambiguity across sub-problems.