Agent skill

Model Code Analyzer

by zhnnky329 in zhnnky329/MathModeling-skills

Translate a human-approved main method and usable baseline into a minimal language-neutral implementation and experiment contract.

MITAuto-check passed

Install Model Code Analyzer

skills CLI

$ npx skills add zhnnky329/MathModeling-skills --skill model-code-analyzer -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI

$ gh skill install zhnnky329/MathModeling-skills model-code-analyzer --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy

$ git clone --depth 1 https://github.com/zhnnky329/MathModeling-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.codex/skills/model-code-analyzer .claude/skills/model-code-analyzer && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Paste into Claude Code

Install the "model-code-analyzer" agent skill from https://github.com/zhnnky329/MathModeling-skills/tree/main/.codex/skills/model-code-analyzer into .claude/skills/model-code-analyzer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "model-code-analyzer", then confirm the skill loads.

Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

$skill-installer

$skill-installer install https://github.com/zhnnky329/MathModeling-skills/tree/main/.codex/skills/model-code-analyzer

Type this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.

skills CLI

$ npx skills add zhnnky329/MathModeling-skills --skill model-code-analyzer -a codex

Project install goes to .agents/skills/; add -g for ~/.codex/skills/.

GitHub CLI

$ gh skill install zhnnky329/MathModeling-skills model-code-analyzer --agent codex

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/zhnnky329/MathModeling-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/.codex/skills/model-code-analyzer .agents/skills/model-code-analyzer && rm -rf skills-src

Use ~/.agents/skills/ instead of .agents/skills for a personal install.

Codex skills documentation · loads skills from .agents/skills/

Paste into Codex

Install the "model-code-analyzer" agent skill from https://github.com/zhnnky329/MathModeling-skills/tree/main/.codex/skills/model-code-analyzer into .agents/skills/model-code-analyzer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "model-code-analyzer", then confirm the skill loads.

Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add zhnnky329/MathModeling-skills --skill model-code-analyzer -a cursor

Project install goes to .agents/skills/; add -g for ~/.cursor/skills/.

GitHub CLI

$ gh skill install zhnnky329/MathModeling-skills model-code-analyzer --agent cursor

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/zhnnky329/MathModeling-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/.codex/skills/model-code-analyzer .cursor/skills/model-code-analyzer && rm -rf skills-src

Use ~/.cursor/skills/ instead of .cursor/skills for a personal install.

Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/

Paste into Cursor

Install the "model-code-analyzer" agent skill from https://github.com/zhnnky329/MathModeling-skills/tree/main/.codex/skills/model-code-analyzer into .cursor/skills/model-code-analyzer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "model-code-analyzer", then confirm the skill loads.

Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

gemini skills install

$ gemini skills install https://github.com/zhnnky329/MathModeling-skills.git --path .codex/skills/model-code-analyzer

--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.

skills CLI

$ npx skills add zhnnky329/MathModeling-skills --skill model-code-analyzer -a gemini-cli

Project install goes to .agents/skills/; add -g for ~/.gemini/skills/.

GitHub CLI

$ gh skill install zhnnky329/MathModeling-skills model-code-analyzer --agent gemini-cli

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/zhnnky329/MathModeling-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/.codex/skills/model-code-analyzer .gemini/skills/model-code-analyzer && rm -rf skills-src

Use ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.

Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/

Paste into Gemini CLI

Install the "model-code-analyzer" agent skill from https://github.com/zhnnky329/MathModeling-skills/tree/main/.codex/skills/model-code-analyzer into .gemini/skills/model-code-analyzer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "model-code-analyzer", then confirm the skill loads.

Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

GitHub CLI

$ gh skill install zhnnky329/MathModeling-skills model-code-analyzer

Installs for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).

skills CLI

$ npx skills add zhnnky329/MathModeling-skills --skill model-code-analyzer -a github-copilot

Project install goes to .agents/skills/; add -g for ~/.copilot/skills/.

Manual copy

$ git clone --depth 1 https://github.com/zhnnky329/MathModeling-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/.codex/skills/model-code-analyzer .github/skills/model-code-analyzer && rm -rf skills-src

Use ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.

GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/

Paste into GitHub Copilot

Install the "model-code-analyzer" agent skill from https://github.com/zhnnky329/MathModeling-skills/tree/main/.codex/skills/model-code-analyzer into .github/skills/model-code-analyzer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "model-code-analyzer", then confirm the skill loads.

GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add zhnnky329/MathModeling-skills --skill model-code-analyzer -a opencode

OpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.

GitHub CLI

$ gh skill install zhnnky329/MathModeling-skills model-code-analyzer --agent opencode

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/zhnnky329/MathModeling-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/.codex/skills/model-code-analyzer .opencode/skills/model-code-analyzer && rm -rf skills-src

Use ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.

OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/

Paste into OpenCode

Install the "model-code-analyzer" agent skill from https://github.com/zhnnky329/MathModeling-skills/tree/main/.codex/skills/model-code-analyzer into .opencode/skills/model-code-analyzer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "model-code-analyzer", then confirm the skill loads.

OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

Facts

Skill name: model-code-analyzer
GitHub stars: 1.1k
Token cost: ~881 tokens
SKILL.md length: 329 words
Files: 1
Skills in repo: 29
Repo updated: Sep 24, 2026
First seen: Oct 7, 2026
Licence: MIT

At a glance

Translate a human-approved main method and usable baseline into a minimal language-neutral implementation and experiment contract.

Works in 6 steps: Read the approved choice, method card,… → Plan only → Record the fallback ID and trigger, but… → …
Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Model Code Analyzer is an agent skill from zhnnky329/MathModeling-skills. 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.

Its SKILL.md is about 880 tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It works with Python. The repository describes itself as: 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。 The licence is MIT.

Example prompts

“/model-code-analyzer”

Requirements

Python 3

Workflow steps

6 steps, taken from the first numbered list in SKILL.md.

Read the approved choice, method card, probe conditions, and experiment budget.
Plan only
Record the fallback ID and trigger, but do not plan its full implementation unless the trigger is already evidenced and the human chose…
Map mathematical definitions to inputs, processing steps, intermediate evidence, outputs, and validation checks.
Define a directly comparable metric/output contract for main and baseline.
Define the round output

What it can do on your machine

Read from SKILL.md and the folder at commit 0b46e9c. It shows what the files ask for, not the result of running them.

Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
No scripts in the folder and no shell commands in SKILL.md (its code samples are json).
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Model Code Analyzer loads about 881 tokens when it runs. Until then it costs about 57 tokens; SKILL.md has 329 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it: ~57
When it runs · the whole SKILL.md, loaded when a task matches: ~881

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from zhnnky329/MathModeling-skills at commit 0b46e9c, republished under its MIT licence (© zhnnky329). 329 words, ~881 tokens.

Download SKILL.mdSave it as .claude/skills/model-code-analyzer/SKILL.md (or your agent's skills folder).

name: model-code-analyzer
description: 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.

Purpose

Define exactly what code must implement and save. Do not expand the approved experiment scope or fully plan a dormant fallback.

Preconditions

methods/Qx/qx_method_card.md and probe summary exist.
methods/Qx/qx_decisions.jsonl contains a human DECIDED method choice.
A usable baseline is identified.
Cleaned data and data_profile.json are ready when data is required.
Implementation target and round are known.

Read legacy candidate/decision artifacts only when the new artifacts are absent.

Workflow

Read the approved choice, method card, probe conditions, and experiment budget.
Plan only:
- approved main;
- approved usable_baseline;
- shared helpers and comparison logic.
Record the fallback ID and trigger, but do not plan its full implementation unless the trigger is already evidenced and the human chose activation.
Map mathematical definitions to inputs, processing steps, intermediate evidence, outputs, and validation checks.
Define a directly comparable metric/output contract for main and baseline.
Define the round output:

text

results/Qx/experiments/roundN/
├── figures/
├── tables/
├── metrics/
└── run_summary.json

Create logs/ only for failures, warnings, or reproducibility needs. 7. Write code/Qx/qx_code_plan.md for Python or code/matlab/Qx/qx_code_plan.md for MATLAB. 8. Hand off to the matching language generator.

Run Summary Contract

Require:

json

{
  "schema_version": 1,
  "question": "Q1",
  "round": "round1",
  "implementation_target": "python",
  "random_seed": 2026,
  "approved_decision_id": "q1_method_choice",
  "methods": [
    {
      "method_id": "M1",
      "role": "usable_baseline",
      "script": "code/Q1/q1_baseline.py",
      "status": "success",
      "execution_time_seconds": 0,
      "input_files": [],
      "output_files": [],
      "figure_files": [],
      "metrics_summary": {},
      "warnings": [],
      "errors": []
    }
  ],
  "comparison": {},
  "fallback_trigger": {
    "fallback_id": null,
    "condition": null,
    "observed": false,
    "evidence": null
  },
  "environment": {}
}

Code Plan Contents

target language and round purpose;
approved decision ID;
main and baseline IDs and roles;
input fields and units;
per-method computation steps;
comparable outputs and metrics;
risk-probe conditions that implementation must monitor;
fallback trigger evaluation;
paths, seed, dependencies, and expected runtime;
named review checks expected downstream.

Rules

Do not write executable model code.
Do not add candidates or change model meaning.
Do not plan a diagnostic reference as the official baseline.
Do not implement a fallback before activation.
Do not require success logs.
Do not create a README when the code plan already provides the same instructions.
Stop if a human choice, required parameter, input field, or comparable baseline output is missing.

Verification

Plan scope is exactly main plus usable baseline unless fallback activation is recorded.
Outputs are directly comparable.
Probe risks and fallback trigger are represented in run_summary.json.
Paths follow the experiment contract.
Handoff targets the correct language generator.

Files

Just SKILL.md in .codex/skills/model-code-analyzer of zhnnky329/MathModeling-skills.

Open the folder on GitHubat commit 0b46e9c

Compare with similar skills

Model Code Analyzer next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Model Code Analyzer compared with similar skills
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NotebookLM Research AssistantPleasePrompto/notebooklm-skill	7.8k	14 repos	~2.4k	Automated safety check: Notes	MIT	27 days ago
Manim Video Productionbrowser-use/video-use	28k	6 repos	~3k	Automated safety check: Pass	MIT	5 days ago
Code Review ChecklistshareAI-lab/learn-claude-code	78k	5 repos	~1.1k	Automated safety check: Pass	MIT	9 days ago

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Matlab Model Code Generator
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Works with

Python

Questions about Model Code Analyzer

What does Model Code Analyzer do?

Translate a human-approved main method and usable baseline into a minimal language-neutral implementation and experiment contract. Model Code Analyzer is an agent skill from zhnnky329/MathModeling-skills. Translate a human-approved main method and usable baseline into a minimal language-neutral implementation and experiment contract.

How do I install Model Code Analyzer in Claude Code?

Run `npx skills add zhnnky329/MathModeling-skills --skill model-code-analyzer -a claude-code`. Or copy the skill folder (.codex/skills/model-code-analyzer in zhnnky329/MathModeling-skills) into .claude/skills/model-code-analyzer in your project. Claude Code loads it when a task matches its description.

How do I install Model Code Analyzer in Codex?

Run `npx skills add zhnnky329/MathModeling-skills --skill model-code-analyzer -a codex`. Or copy the skill folder (.codex/skills/model-code-analyzer in zhnnky329/MathModeling-skills) into .agents/skills/model-code-analyzer in your project. Codex loads it when a task matches its description.

Can I use Model Code Analyzer in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add zhnnky329/MathModeling-skills --skill model-code-analyzer -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/model-code-analyzer, .gemini/skills/model-code-analyzer, .github/skills/model-code-analyzer and .opencode/skills/model-code-analyzer in your project.

What does Model Code Analyzer need to run?

SKILL.md names no scripts, command-line tools or credentials: Model Code Analyzer is instructions for the agent only. Our summary lists: Python 3.

Does Model Code Analyzer access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Model Code Analyzer safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Model Code Analyzer use?

Model Code Analyzer is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Model Code Analyzer use?

About 881 tokens (SKILL.md is roughly 3.5k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Model Code Analyzer?

Skills that share tags, products or a category with Model Code Analyzer: MCP Server Builder (anthropics/skills, 180k stars), PDF Processing (anthropics/skills, 180k stars), NotebookLM Research Assistant (PleasePrompto/notebooklm-skill, 7.8k stars) and Manim Video Production (browser-use/video-use, 28k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Model Code Analyzer?

zhnnky329 (a GitHub user) maintains it in zhnnky329/MathModeling-skills, which has 1,059 GitHub stars. The repository holds 29 skills in this directory. The repository was last updated on September 24, 2026.

Source: zhnnky329/MathModeling-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.

Install Model Code Analyzer

Facts

At a glance

What it does

Example prompts

Requirements

Workflow steps

What it can do on your machine

Tool permissions

Runs code

Network

Credentials

Context cost

Safety

SKILL.md

Purpose

Preconditions

Workflow

Run Summary Contract

Code Plan Contents

Rules

Verification

Files

Compare with similar skills

Similar skills

More from zhnnky329/MathModeling-skills

Related

Questions about Model Code Analyzer