model-creator

Generate and rank 5-8 modeling plans for math competition problems.

1|Updated Apr 2, 2026
One-click install
npx skills add https://github.com/Best6668/AMIS --skill model-creator-best6668
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: model-creator
Source: https://github.com/Best6668/AMIS/tree/main/skills/model-creator
Command: npx skills add https://github.com/Best6668/AMIS --skill model-creator-best6668

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Generate and rank modeling plans for math modeling competition problems, enabling structured solution discovery and efficient planning.

Core Features & Use Cases

  • Automated generation of multiple modeling approaches and evaluation criteria tailored to competition problems.
  • Prioritized sequencing of approaches with feasibility indicators for quick decision-making.
  • Integration with /problem-analysis, /feasibility-check, and /model-review workflows to form an end-to-end pipeline.

Quick Start

Provide a complete competition problem description and any data attachments, then request 5-8 candidate modeling plans ranked by feasibility and impact.

Frequently Asked Questions about model-creator

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

FAQPage Schema
How do I generate and rank modeling plans for math modeling competitions?

To generate and rank modeling plans, provide a complete competition problem description with data attachments to receive 5-8 candidate modeling approaches prioritized by feasibility and impact.

What is the best way to structure solution discovery for math modeling problems?

The best way to structure solution discovery is using automated generation of multiple modeling approaches with evaluation criteria, delivering a prioritized list with feasibility signals and risk assessments.

How many candidate modeling approaches can I expect for a single competition problem?

You can expect 5-8 candidate plans generated from the problem description, sequenced with feasibility indicators for quick decision-making during competitions.

When should I not use automated ranking for competition modeling plans?

Automated ranking is not suitable for problems lacking defined data, constraints, or structured subproblems, as it requires clear inputs to propose and evaluate candidate modeling approaches.