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Mathodology Dev Test Release

Community
sweetcornna
mathodology-dev-test-release

Use when checking skill metadata, references or repository boundaries, or preparing an explicitly requested skills release.

Overview

Publishersweetcornna
Repositorymathodology
Skill namemathodology-dev-test-release
Stars
312
Forks
14
Bundled files
2
LicenseMIT
Links
Skill sourceRepository
  • 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.

  • 2 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 Dev Test Release 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/sweetcornna/mathodology/tree/main/.claude/skills/mathodology-dev-test-release
  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/sweetcornna/mathodology.git /tmp/mathodology
mkdir -p .claude/skills
cp -r /tmp/mathodology/.claude/skills/mathodology-dev-test-release .claude/skills/mathodology-dev-test-release
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Mathodology Dev Test Release 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 Dev Test Release 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 Dev Test Release 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 Repository Checks

Repository maintenance checks are optional utilities; they do not run during modeling by default and cannot establish mathematical or editorial quality.

From a repository checkout, run:

bash
python3 .claude/skills/mathodology-dev-test-release/scripts/validate_repo.py all

The standard-library checker covers skill/role metadata, local Markdown links, known skill references and allowed repository paths (skills, roles, workflows, docs, apps/studio/ and Studio's Windows workflow). Individual commands are metadata, links, boundary and selftest. It supports a source export without Git; in a checkout it also inspects tracked paths. It does not enforce document heading counts, search wording, updater contracts or contest run schemas.

The figure demonstration script and PDF overview utility have self-tests; backup can be verified by extracting the archive and checking its checksum (backup and installation guidance).

Explicitly requested releases

Release work is repository maintenance, separate from the modeling guidance. Inspect the current remote branch, recent releases and existing tags. Choose the next version consistently with that history and describe migration requirements when removing an old entry point or utility. The only package manifest is Mathodology Studio's in apps/studio/; do not add another manifest, application build or CI pipeline for the skills pack itself.

Review the actual changes, run relevant checks and verify retained asset licenses. Use a focused branch and pull request when that matches repository practice. Respect branch protections; do not bypass required reviews or checks. Once the authorized changes are merged, tag that exact commit and publish the requested release with concise changes, migration notes and truthful validation results. Never move an existing release tag to a different commit. Verify the published tag and release point to the intended source. GitHub's source archives are enough unless the user requests additional artifacts. For a Studio release, bump apps/studio/package.json and the download links in both READMEs, build the macOS and Linux packages with pnpm dist and pnpm dist:linux, and attach the Windows installer from the .github/workflows/studio-windows.yml run on the release tag (signed once SignPath is configured; each request waits for an approver). Publish SHA-256 sums of every file.

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 Dev Test Release AI skill do?

Use when checking skill metadata, references or repository boundaries, or preparing an explicitly requested skills release.

Why use Mathodology Dev Test Release on TypingMind?

Because you install it once and use it with any model. Mathodology Dev Test Release 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 Dev Test Release in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/sweetcornna/mathodology/tree/main/.claude/skills/mathodology-dev-test-release. 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 Dev Test Release?

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 Dev Test Release?

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

Is the Mathodology Dev Test Release 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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