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Math Templates

Community
capwitf
math-templates

Use when 高教社杯/CUMCM mathematical modeling work needs lightweight reusable paper assets, national-prize style paragraph templates, section controls, per-question code/plot/test scaffolds, evidence registries, paper fragments, reproduction scripts, or safe reuse of prior contest project structure without copying old problem-specific content.

Overview

Publishercapwitf
RepositoryMy-MathModeling-skills
Skill namemath-templates
Stars
57
Forks
1
Bundled files
23
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 23 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by capwitf on GitHub. Read the source before you install it.

Installation

Install the Math Templates AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/capwitf/My-MathModeling-skills/tree/main/math-templates
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/capwitf/My-MathModeling-skills.git /tmp/My-MathModeling-skills
mkdir -p .claude/skills
cp -r /tmp/My-MathModeling-skills/math-templates .claude/skills/math-templates
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Math Templates in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Math Templates on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Math Templates is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Math Modeling Templates

Purpose

Provide lightweight reusable assets for 高教社杯/CUMCM-style mathematical modeling papers and support packages. Use the assets to preserve good structure, not to import old conclusions.

Core rule: reuse structure, not old content. Unless the user explicitly says prior contest material is current source evidence, do not copy old problem names, numbers, scenario labels, file paths, figure titles, or conclusions into a new project.

National-first quality gate: 国一候选 is a quality target, not an award promise (不承诺获奖). Template use is acceptable only when it helps the current paper show 题目贴合, 建模洞察, 证据可信, 可复现, and 边界清楚. A template sentence, old project scaffold, or chart script cannot replace current evidence.

Language/output boundary: skill reasoning, template selection, schemas, code scaffolds, algorithms, file names, registry statuses, LaTeX commands, English literature terms, and official rule quotations may stay English; 该英文就用英文. Paper-facing deliverables should default to Chinese contest prose: paper body, abstract snippets, copyable paragraph templates, project manifests, and reproduction user messages. Do not sacrifice model judgment, evidence chains, reproducibility, or terminology precision for localization (不因中文化牺牲).

Exact boundary for audits: the skill's internal 判断 may use English; only final 产出 inside paper-facing 交付文件 defaults to Chinese. Keep 算法名, 字段, code identifiers, and 官方规则原文 unchanged when precision would otherwise be lost.

Default to light asset library mode. Load or copy only the asset the user needs: style profile, section controls, paragraph phrases, code scaffold, chart template, evidence registry, or reproduction helper. Do not impose a project-management workflow unless the user asks to build or reorganize a full project.

Asset Root

Reusable project assets live in:

assets/contest-project-template/

Use them when the user asks to:

  • start a contest project;
  • reuse code or paper assets from a prior project;
  • create per-question computation, plotting, or test scaffolds;
  • standardize results/qX, registries, figure plans, or paper fragments;
  • create national-prize style paper sections or paragraph phrasing;
  • borrow contest writing patterns without copying old content;
  • prepare a reproducible support package.

Reference Routing

Open only the smallest relevant reference:

NeedOpen
Asset inventory or categorizationreferences/asset-library-index.md
Safe copying or rewriting of templatesreferences/template-usage.md
Patterns from prior contest projectsreferences/prior-contest-patterns.md
Official-exemplar style observationsreferences/official-exemplar-notes.md
Paper style only, no project structurereferences/national-prize-style-profile.md
Section labels, bold subquestion labels, colon spacing, 总分 structurereferences/section-format-controls.md
Assumptions, model evaluation, framework advantages, limitations, extension wordingreferences/assumption-evaluation-controls.md
Plain contest prose, empty-phrase removal, numbers/units, judge readabilityreferences/contest-language-guardrails.md
Problem analysis, model-building, result, validation, and evaluation paragraph flowreferences/paragraph-flow.md

Optional Full Scaffold

Use this only when the user asks to build or reorganize a full contest project. If they only need writing style, paragraph phrases, paper skeletons, or chart templates, skip this section.

  1. Check the current problem statement, official rules, data files, and required subquestions.
  2. Create only needed folders such as data/raw, q1/src, q1/tests, results/q1, paper/fragments, logs, and tmp.
  3. Copy only the needed files from assets/contest-project-template/.
  4. Rename problem_template to the active subquestion folder, such as q1 or q2.
  5. Replace placeholder schemas with current data columns, units, constraints, and output tables.
  6. Add or update evidence registries: run_record.csv, result_registry.csv, figure_evidence.csv, and numerical_diagnostics.csv.
  7. Keep official outputs under results/qX/; exploratory files stay out of final registries unless promoted by math-hub.
  8. Build paper/fragments/qX.md only from verified result tables and figure evidence.
  9. Add tests before a new compute or plot script supports a paper claim.

Asset Selection

NeedAsset
New subquestion computationproblem_template/src/compute_template.py
New subquestion plottingproblem_template/src/plot_template.py
Regression test scaffoldproblem_template/tests/test_pipeline_template.py
Reproduce official runsreproduce_all.py
Paper fragment handoffpaper/fragments/fragment_protocol.md
Subquestion writing guidepaper/problem_guide_template.md
National-prize paper section skeletonpaper/section_templates_national.md
National-prize paragraph phrasespaper/paragraph_phrases_national.md
Registry seedsregistries/*.csv

Reuse Safety

  • Prior-project audit: separate reusable structure from problem-specific residue.
  • Path audit: remove absolute local paths before reuse or packaging.
  • Evidence audit: any template output that may enter the paper needs a registry row or verified source.
  • Figure audit: any main-body figure template needs a figure plan/evidence row before paper use.
  • Paper audit: fragments may cite only results/qX, figure evidence, checked formulas, and verified rules.
  • Submission audit: reproduction scripts must run from the project root and must not depend on hidden local files.

Output Contract

Return only what the current request needs:

  • copied or created template paths;
  • reused structure patterns;
  • placeholders still needing current problem facts;
  • tests or validation commands run;
  • old-template residue removed or still open;
  • evidence-chain or submission risks, returned to math-hub when they affect paper-ready status.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Math Templates AI skill do?

Use when 高教社杯/CUMCM mathematical modeling work needs lightweight reusable paper assets, national-prize style paragraph templates, section controls, per-question code/plot/test scaffolds, evidence registries, paper fragments, reproduction scripts, or safe reuse of prior contest project structure without copying old problem-specific content.

Why use Math Templates on TypingMind?

Because you install it once and use it with any model. Math Templates is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Math Templates in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/capwitf/My-MathModeling-skills/tree/main/math-templates. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Math Templates?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Math Templates?

As many as you like. As long as a model supports skills, you can use Math Templates with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Math Templates AI skill free?

Yes. It is published on GitHub by capwitf under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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