Matlab Plan Grader Adoption logo

Matlab Plan Grader Adoption

Organization
matlab
matlab-plan-grader-adoption

Use when an instructor asks for a MATLAB Grader assessment setup guide, adoption guide, pilot plan, course-specific rollout, QTI 3 sharing workflow, or recommended MATLAB Grader assessment-item-generation configuration based on a learning objective, course title, course description, module description, lab description, or assessment goal.

Overview

Publishermatlab
Repositoryagent-skills-playground
Skill namematlab-plan-grader-adoption
Stars
179
Forks
32
Bundled files
2
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.

  • 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 matlab on GitHub. Read the source before you install it.

Installation

Install the Matlab Plan Grader Adoption 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.
  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/matlab/agent-skills-playground.git /tmp/agent-skills-playground
mkdir -p .claude/skills
cp -r /tmp/agent-skills-playground/demos/assessment-generation-for-matlab-grader/skills/matlab-plan-grader-adoption .claude/skills/matlab-plan-grader-adoption
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Matlab Plan Grader Adoption 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 Matlab Plan Grader Adoption 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 Matlab Plan Grader Adoption 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.

MATLAB Grader Instructor Setup Guide

Purpose

Generate a short instructor-facing setup guide for adopting the MATLAB Grader Assessment Item Generator skill in a course, module, lab, assessment sequence, or instructional-design workflow.

Treat the user's provided objective, course description, assessment goal, or pilot request as the guide input. If the input is missing, ask for one sentence describing the course, module, learning objective, or assessment context before generating the guide.

Input Handling

Use the input to infer the best-fit setup context:

  • introductory MATLAB programming;
  • engineering computation or modeling;
  • data analysis or visualization lab;
  • object-oriented programming with MATLAB classes;
  • graded homework, quiz, lab, project, or exam preparation;
  • QTI 3 interchange, LMS review, or instructional-design sharing;
  • mixed or uncertain context.

If multiple contexts fit, choose the primary context and add a short note about secondary considerations. Do not ask follow-up questions unless the input is too vague to identify an assessment goal.

Guide Workflow

  1. Restate the inferred learning objective or assessment goal.
  2. Identify the best-fit assessment context and why it fits.
  3. Recommend an assessment item type: Script, Function, Class Definition, Class Inheritance, Object Usage, or Class Methods.
  4. Recommend assessment purpose: formative, summative, or both. When the context mapping has a purpose emphasis, use it. Otherwise default to summative for graded or unspecified use, and both when the module serves practice first with grading reuse later.
  5. Recommend content_language: auto unless the instructor requests a specific language code.
  6. Provide an instructor-ready generation prompt for the matlab-generate-grader-assessments skill.
  7. Define review gates for description.txt, solution.m, template.m, template line locks documented in assessments.md, function_call.m when the recommended type is Function, and tests.m.
  8. Define QTI 3 export and sharing guidance when portability is requested.
  9. Propose a first pilot with one generated item, one review pass, and one revision loop.
  10. Include a concise instructor checklist.

If the request requires a MATLAB class definition, class inheritance, object-usage, or class-method submission, include the generator's classdef constraints in the starter prompt: learner-authored class definitions must be plain .m files, not Live Script .m or .mlx files; referenced class, data, and helper files must be listed for MATLAB Grader upload; generated helper files must remain readable .m files rather than .p files. For abstract classes, recommend a concrete subclass or object-usage task unless the learner-submitted class can be assessed without instantiation.

Read references/setup-guide-template.md for context mapping, output format, prompt patterns, and adaptation rules.

Read references/research-summary.md when the user asks for research basis, evidence, rationale, literature mapping, or an instructor-facing explanation of the assessment design choices.

Output Rules

  • Default to Markdown unless the user asks for another format.
  • Keep the guide practical enough for an instructor to use without extra setup.
  • Include concrete matlab-generate-grader-assessments prompts, not only advice.
  • Recommend QTI 3 only when the instructor asks for portability, LMS review, interchange, standards-based sharing, or instructional-design handoff.
  • State that QTI 3 preserves MATLAB Grader artifacts for interchange and does not make generic QTI runtimes execute MATLAB code.
  • Keep policies and institutional workflow language adjustable unless the user provides local requirements.
  • Include a brief research-basis section only when requested or when the guide is intended for departmental review, pilot approval, or assessment redesign.

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 Matlab Plan Grader Adoption AI skill do?

Use when an instructor asks for a MATLAB Grader assessment setup guide, adoption guide, pilot plan, course-specific rollout, QTI 3 sharing workflow, or recommended MATLAB Grader assessment-item-generation configuration based on a learning objective, course title, course description, module description, lab description, or assessment goal.

Why use Matlab Plan Grader Adoption on TypingMind?

Because you install it once and use it with any model. Matlab Plan Grader Adoption 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 Matlab Plan Grader Adoption in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/matlab/agent-skills-playground/tree/main/demos/assessment-generation-for-matlab-grader/skills/matlab-plan-grader-adoption. 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 Matlab Plan Grader Adoption?

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 Matlab Plan Grader Adoption?

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

Is the Matlab Plan Grader Adoption AI skill free?

It is published on GitHub by matlab. Check the repository for licensing terms. 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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