mm-analysis

Analyze mathematical modeling problems and datasets with iterative Actor-Critic evaluation.

5|Updated Apr 21, 2026

Search Tags:#automation #modeling #data exploration #problem analysis #dataset summary #actor critic

Ready to run

npx skills add https://github.com/911439925/math-modeling-skill --skill mm-analysis

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: mm-analysis
Source: https://github.com/911439925/math-modeling-skill/tree/main/plugins/math-modeling/skills/mm-analysis
Command: npx skills add https://github.com/911439925/math-modeling-skill --skill mm-analysis

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill streamlines the process of analyzing complex mathematical modeling problems, helping users extract structured insights and data-driven understanding efficiently.

Core Features & Use Cases

Deep Problem Decomposition: Analyzes problem backgrounds, objectives, assumptions, and constraints to generate comprehensive reports.
Dataset Examination: Loads and summarizes datasets, identifying data quality issues and summarizing key statistics.
Iterative Analysis with Critic Evaluation: Implements Actor-Critic mechanism to continually improve the analysis quality through structured feedback and refinement.
Use Case: For a data scientist tackling a modeling challenge, this Skill automates the initial problem breakdown, saving hours and reducing oversight.

Quick Start

Invoke mm-analysis with your problem description and dataset files to obtain a detailed structured analysis report.

Frequently Asked Questions about mm-analysis

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

FAQPage Schema

How do I automate problem analysis for mathematical modeling tasks?▼

To automate problem analysis for mathematical modeling, provide your problem description and dataset files to generate a structured report covering backgrounds, objectives, assumptions, and constraints efficiently.

How does Actor-Critic iterative evaluation improve data exploration?▼

Actor-Critic iterative evaluation improves data exploration by continually refining analysis quality through structured feedback loops. The mechanism examines datasets, identifies data quality issues, and summarizes key statistics progressively.

What is the best way to extract structured insights from complex modeling datasets?▼

The best way to extract structured insights from complex modeling datasets is through deep problem decomposition, which examines backgrounds, assumptions, and constraints to produce a comprehensive analytical report.

Can I use this Skill for dataset summary and data quality examination without writing scripts?▼

You cannot use this Skill for dataset summary without scripts because it requires data loading scripts and analysis templates to perform in-depth evaluations and summarize key statistics from your datasets.

Do I need to prepare data loading scripts before starting problem decomposition?▼

Yes, you need to prepare data loading scripts before starting problem decomposition. These scripts are required to load datasets, identify data quality issues, and generate the structured analysis templates for the evaluation.