MathModeling Skills plugin for Grok Build

Name: MathModeling Skills
Author: zhnnky329

Community Claude Code plugin, installs in GrokPluginDevelopment

面向数学建模竞赛的 Claude Code / Codex Skills ，支持分阶段建模流程与 Python、MATLAB/北太天元代码分支。

How to install MathModeling Skills in Grok Build

1. Add this repo as a plugin marketplace in Grok Build (once per computer):

grok plugin marketplace add zhnnky329/MathModeling-skills

2. Install the plugin:

grok plugin install mathmodeling-skills@zhnnky329/MathModeling-skills --trust

✓ Install-checked in Grok Buildchecked Oct 18, 2026

Review before you trust it. --trust lets this plugin's hooks, MCP servers and scripts run code on your computer. It's a community repo that neither xAI nor GrokStacks has reviewed, so read its code first.

Then start a new Grok session, or press r in /plugins, to load it.

Inside Grok Build: type /plugins, open the Marketplace tab, press a and add zhnnky329/MathModeling-skills, then select mathmodeling-skills and press i.

Command format: Grok Build's plugin guide

What we checked: on Oct 18, 2026 it installed with grok 1.0.62 and Grok found 29 skills. That's an install check only: we haven't run it in a live Grok session.

What it contains

Skills (25)

code-reviewer — Detect whether approved modeling code is Python or MATLAB/Beita Tianyuan and route it to the matching reviewer using the compact named-check review contract.
completeness-auditor — Audit whether the semantic evidence required by the active lean or submission profile exists and is current, without requiring one verbose artifact per skill or an arbitrary number of pass bullets.
consistency-auditor — Run scoped or final cross-media consistency checks for mathematical-modeling artifacts, comparing canonical numbers, symbols, parameters, decisions, files, and paper claims without performing full-workspace audits for low-risk changes.
data-auditor-cleaner — Map contest attachments to subquestions, audit and clean raw data, and emit one reusable data profile with quality, coverage, imbalance, concentration, and method-readiness evidence for downstream risk screening.
decision-prompt-builder — Build one compact choice card at a genuine mathematical-modeling judgment point. Use before method screening, after a meaningful experiment, or before final claim/freeze approval so the human chooses the trade-off while AI handles mechanica
figure-table-planner — Plan the smallest set of diagnostic, comparison, paper, and appendix figures or tables needed to support verified mathematical-modeling decisions and claims.
final-method-explainer — Build the authoritative final method explanation for a submission-ready subquestion from the method card, human decision ledger, code plan, final results, and robustness evidence.
math-figure-generator — Generate and render-verify publication-quality mathematical-modeling figures from saved evidence, using the approved figure plan, source data, claim, type, and consistent visual system.
matlab-code-reviewer — Review, run, debug, and verify approved MATLAB or Beita Tianyuan modeling code against its plan, data contract, method decision, compatibility constraints, and experiment outputs, saving one compact JSON review.
matlab-model-code-generator — Generate and run minimal reproducible MATLAB or Beita Tianyuan compatible code for the human-approved main method and usable baseline, with compact experiment artifacts and a canonical run summary.
method-selector — Build and risk-screen a compact role-based method shortlist for a mathematical-modeling subquestion. Use after problem framing and data profiling, before model code generation, to propose a main candidate, a usable baseline, and at most one
model-assumptions-builder — Extract and maintain global and method-specific mathematical-model assumptions from the problem frame, active method cards, data profile, and risk probes, while leaving necessity and impact judgments to the human modeler.
model-code-analyzer — Translate a human-approved main method and usable baseline into a minimal language-neutral implementation and experiment contract. Use after G2.5 and data readiness, before Python or MATLAB code generation.
modeler-decision-logger — Faithfully append a human modeler's choice and rationale to one canonical per-subquestion JSONL decision ledger. Use after a choice card is answered or when migrating legacy decision artifacts; never originate or improve the decision.
paper-polisher — Polish mathematical modeling paper drafts for grammar, clarity, formula consistency, hedging calibration, overclaim detection, and contest formatting compliance. Use after paper-section-writer has drafted sections.
paper-section-writer — Draft submission-ready mathematical-modeling paper sections from the approved solution package, frozen numbers, human decision ledger, and verified figures without searching scattered exploratory outputs or inventing interpretation.
problem-classifier — Classify each parsed mathematical-modeling subquestion by required output and structure, surface ambiguous framing trade-offs for human choice, and record primary/secondary task types without selecting algorithms.
problem-parser — Parse a mathematical-modeling problem into goals, objects, data, constraints, outputs, subquestions, dependencies, variables, relationships, and human-confirmed success criteria before any method selection.
python-code-reviewer — Review, run, debug, and verify approved Python modeling code against its code plan, data contract, method decision, risk conditions, and experiment outputs, saving one compact JSON review.
python-model-code-generator — Generate and run minimal reproducible Python modeling code for the human-approved main method and usable baseline, saving compact experiment artifacts and a canonical run summary.
quality-assurance-auditor — Perform the final submission-level audit of mathematical-modeling workflow integrity, evidence quality, anti-fabrication, paper coherence, figures, references, and contest readiness after consistency and completeness audits pass.
reference-manager — Manage and verify references for mathematical modeling contest papers, generating BibTeX entries, checking citation completeness, and ensuring all references are traceable to actual sources.
related-paper-analyzer — Collect and analyze relevant papers, reports, and reference methods to inform method selection without fabricating references or copying models blindly.
result-report-generator — Summarize modeling experiment evidence, compare the approved main method with a usable baseline, surface fallback triggers, and produce a decision-point or final report without creating routine per-round prose.
robustness-checker — Design and run risk-targeted robustness, sensitivity, error, and baseline checks for an approved mathematical model, emitting compact machine evidence in lean mode and a final report in submission mode.

Hooks (1)

hooks — SessionStart

About this listing

MathModeling Skills is a community plugin on GitHub by zhnnky329, written as a Claude Code plugin that Grok Build installs. We install-checked it in Grok; we haven't run it in a live session or reviewed its code. When we installed it, Grok found 25 skills, 1 hook. Its license is MIT. We checked commit 0b46e9c78335. It's one of 409 listings in the Development category on GrokStacks.

MathModeling Skills FAQ

How do I install MathModeling Skills in Grok Build?

Add the repo as a marketplace with grok plugin marketplace add zhnnky329/MathModeling-skills, then run grok plugin install mathmodeling-skills@zhnnky329/MathModeling-skills --trust. Then start a new Grok session, or press r in /plugins, to load it.

Is MathModeling Skills an official Grok Build plugin?

No. MathModeling Skills is a community plugin on GitHub by zhnnky329, not in xAI's official Grok Build marketplace. We checked that it installs and what Grok loads from it, but haven't run it in a live Grok session or reviewed its code.

What does MathModeling Skills include?

According to what Grok loaded in our install check, MathModeling Skills includes 25 skills, 1 hook: code-reviewer, completeness-auditor, consistency-auditor, data-auditor-cleaner, decision-prompt-builder, figure-table-planner….

Details

Type: Plugin
Category: Development
Source: Community Claude Code plugin, installs in Grok
License: MIT
Author: zhnnky329
Source repo: https://github.com/zhnnky329/MathModeling-skills
Checked commit: 0b46e9c78335
Checked by GrokStacks: Install-checked in Grok Build on Oct 18, 2026; not run in a live session

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[![Listed on GrokStacks](https://grokstacks.com/badge.svg)](https://grokstacks.com/p/mathmodeling-skills/)

HTML

<a href="https://grokstacks.com/p/mathmodeling-skills/"><img src="https://grokstacks.com/badge.svg" alt="Listed on GrokStacks" width="164" height="28"></a>

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MathModeling SkillsCommunity plugin for Grok Build · 25 skills, 1 hook面向数学建模竞赛的 Claude Code / Codex Skills ，支持分阶段建模流程与 Python、MATLAB/北太天元代码分支。View on GrokStacks →

<a href="https://grokstacks.com/p/mathmodeling-skills/" style="display:block;max-width:380px;box-sizing:border-box;padding:14px 16px;border:1px solid #2a2e3c;border-radius:12px;background:#0a0b0f;color:#e8eaf0;font:14px/1.45 -apple-system,Segoe UI,Roboto,Helvetica,Arial,sans-serif;text-decoration:none"><span style="display:block;font-size:16px;font-weight:700;color:#e8eaf0">MathModeling Skills</span><span style="display:block;margin:2px 0 8px;font-size:12px;color:#9aa0b1">Community plugin for Grok Build · 25 skills, 1 hook</span><span style="display:block;margin:0 0 10px;color:#c9cdd8">面向数学建模竞赛的 Claude Code / Codex Skills ，支持分阶段建模流程与 Python、MATLAB/北太天元代码分支。</span><span style="font-size:12px;font-weight:600;color:#7c9cff">View on GrokStacks →</span></a>

Markdown summary

**[MathModeling Skills on GrokStacks](https://grokstacks.com/p/mathmodeling-skills/)**: Community plugin for Grok Build with 25 skills, 1 hook. Install: `grok plugin install mathmodeling-skills@zhnnky329/MathModeling-skills --trust` (after `grok plugin marketplace add zhnnky329/MathModeling-skills`)

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