Mathodology Award Gates

Community

sweetcornna

mathodology-award-gates

Use when reviewing mathematical validity, evidence, reproducibility, figures or a submission draft.

Overview

Publisher	sweetcornna
Repository	`mathodology`
Skill name	`mathodology-award-gates`
Stars	312
Forks	14
Bundled files	3
License	MIT
Links	Skill source Repository

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.
3 bundled files
Scripts, templates, and references the model can read while it works. Files are read-only and never executed.
Open source
Published by sweetcornna on GitHub. Read the source before you install it.

Installation

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

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.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/sweetcornna/mathodology/tree/main/.claude/skills/mathodology-award-gates
Enable the skill in any chat where you want it available.

Store

Installed

Skills

Add skills

Import one or more skills from a public GitHub folder or a local .skill or .zip archive, or SKILL.md file.

From GitHub URLFrom .skill, .zip, or SKILL.md

GitHub repo or folder

https://github.com/sweetcornna/mathodology/tree/main/.claude/skills/mathodology-award-gates

Points to a single skill folder, or any folder that contains multiple skills. Public repos only.

CancelContinue

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

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/sweetcornna/mathodology.git /tmp/mathodology
mkdir -p .claude/skills
cp -r /tmp/mathodology/.claude/skills/mathodology-award-gates .claude/skills/mathodology-award-gates

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Mathodology Award Gates 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 Mathodology Award Gates 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 Mathodology Award Gates 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.

Mathodology Review Questions

The legacy skill name is retained for discovery. Review the substance of the work; do not estimate awards from invented numeric thresholds or require a particular handoff format. Scale review to the claims and the deadline.

Mathematical and empirical questions

Does the solution answer every required question and represent its essential mechanisms?
Are equations, units, constraints and limiting cases consistent? Is the solution feasible?
Can the parameters be identified from the available observations?
Does the described method match the code, including preprocessing and exclusions?
Is a reported advantage measured against an appropriate baseline on comparable data?
Are fitting, tuning and evaluation separated where the claim requires it?
Are outcomes forced by normalization or constraints labeled as such?
Could plausible changes to important assumptions reverse the recommendation?
Are confidence intervals, predictive intervals and simulation variability distinguished?
Can reported numbers be traced to data, code, a derivation or a stated assumption?
Are evidence gaps, failed runs and material limitations disclosed?

A longer checklist (paper-vs-code conformance, claim baselines, common random numbers, recommendation consistency, citation closeout, rendered-PDF QA) is in references/review-checklist.md.

Use relevant questions, not every possible test. When scenarios share comparable random inputs, paired simulations may improve precision; explain the coupling. Use Monte Carlo uncertainty for estimated probabilities, and disclose the number of simulations. Do not claim proof from a handful of successful runs.

Figures and paper

Load figure presets. Read figures at the size used in the final document. Check labels, uncertainty definitions, legends, color scales and captions against the underlying results. Inspect dense pages of the compiled PDF as well as individual exports. Image2 imagery must remain consistent with the mechanism; generated pixels are not quantitative evidence.

The optional PDF overview utility requires Poppler's pdftoppm and Matplotlib. It creates a page overview for visual review:

bash
python3 .claude/skills/mathodology-award-gates/scripts/make_contact_sheet.py solution.pdf -o overview.png

An overview helps find layout problems; zoom into dense pages to judge actual legibility. A successful render is not a certificate of quality.

Useful review output

Explain each material issue, its evidence, its effect on the conclusion and a concrete fix. Distinguish errors from optional improvements. Correct consequential errors before relying on the result; disclose unresolved uncertainty. Never require the user to approve routine fixes or invent a scoring ceremony.

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 Mathodology Award Gates AI skill do?

Use when reviewing mathematical validity, evidence, reproducibility, figures or a submission draft.

Why use Mathodology Award Gates on TypingMind?

Because you install it once and use it with any model. Mathodology Award Gates 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 Mathodology Award Gates in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/sweetcornna/mathodology/tree/main/.claude/skills/mathodology-award-gates. 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 Mathodology Award Gates?

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 Mathodology Award Gates?

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

Is the Mathodology Award Gates AI skill free?

Yes. It is published on GitHub by sweetcornna 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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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Mathodology Review Questions

Mathematical and empirical questions

Figures and paper

Useful review output

Bundled files

Frequently asked questions

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