Matlab Create Ai Policy

Organization

matlab

matlab-create-ai-policy

Use when an instructor wants to create, interview for, configure, install, update, or review a course AI-use policy for MATLAB AI tutoring. Produces an AI-POLICY.md file for LMS sharing and local tutoring-session enforcement by assignment guardrails.

Overview

Publisher	matlab
Repository	`agent-skills-playground`
Skill name	`matlab-create-ai-policy`
Stars	179
Forks	32
Bundled files	3
Links	Skill source Repository Homepage

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

Installation

Install the Matlab Create Ai Policy 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.
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/matlab/agent-skills-playground/tree/main/demos/ai-tutoring/skills/matlab-create-ai-policy

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/matlab/agent-skills-playground.git /tmp/agent-skills-playground
mkdir -p .claude/skills
cp -r /tmp/agent-skills-playground/demos/ai-tutoring/skills/matlab-create-ai-policy .claude/skills/matlab-create-ai-policy

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

Use it in TypingMind

Enable Matlab Create Ai Policy 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 Create Ai Policy 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 Create Ai Policy 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 AI Tutor Course Policy

Purpose

Interview an instructor to create a course-specific AI-POLICY.md file. The file should be suitable to upload to a learning management system, share with learners, and install locally for MATLAB AI tutoring sessions so assignment guardrails can enforce the instructor's rules.

Use this skill before a course pilot, when adopting the tutor for graded work, or when an instructor wants one policy that applies consistently across homework, labs, projects, quizzes, exams, and instructor-facing materials.

Interactive Interview

Run the interview in short rounds. Ask at most three questions at a time and summarize choices before generating the policy. If the instructor supplies a syllabus, assignment description, or existing policy, extract answers from it first and ask only about gaps.

Required policy requirements:

Course title, term, instructor, and contact or support path.
Course-wide AI-use stance: encouraged, allowed with limits, restricted, or prohibited except when explicitly authorized.
Rules by activity type: homework, labs, projects, quizzes, exams, take-home assessments, and instructor-facing content.
Allowed AI tutor help: concept explanations, analogous examples, debugging, code review, tests, reflection, transcript logs, and session reports.
Restricted AI tutor help: final solutions, full programs, answer keys, hidden test bypassing, unauthorized collaboration, and polishing work before a meaningful learner attempt.
Attribution requirements: whether learners must disclose tutor use, include prompts/transcripts, cite AI assistance, or submit session reports.
Data and privacy boundaries: what learners should avoid sharing.
Local enforcement level for MATLAB AI Tutor guardrails.
Effective date and review cadence.

Read references/policy-interview.md for the interview sequence, enforcement levels, and policy decision matrix.

Read references/ai-policy-template.md before writing AI-POLICY.md.

Read references/policy-examples.md when the instructor asks for examples, wants help choosing policy strictness, or needs calibrated wording for homework, labs, projects, quizzes, exams, or instructor-facing solution generation.

Output Workflow

Interview the instructor until required policy requirements are known.
Summarize the interpreted policy choices and ask for confirmation when anything is ambiguous or high stakes.
Generate AI-POLICY.md in the current working directory unless the user specifies another writable course folder.
Use learner-facing language: clear, direct, and suitable for an LMS.
Include a "Local MATLAB AI Tutor Enforcement" section that assignment guardrails can read.
Include a "Policy Summary for Tutor Guardrails" block with compact rules for tutoring sessions.
Tell the user where the file was written and how to use it with the tutor.

Local Installation Rules

The policy filename must be AI-POLICY.md.
The preferred local install location is the course or tutoring session working directory.
When a tutoring session starts, matlab-apply-assignment-guardrails should look for AI-POLICY.md in the current working directory and apply it before general guardrail defaults.
If multiple policies are present, use the nearest policy in the current course/session directory and state which file is active.
If no policy is present, use conservative default guardrails and ask whether the task is graded or policy-constrained when unclear.

Output Constraints

Do not invent institutional policy, honor-code language, or legal claims.
If the instructor is unsure, mark the policy item as "Instructor default: conservative" and write a clear placeholder for later revision.
Keep the policy actionable for learners and enforceable by the tutor.
Do not create separate README files. The policy artifact is AI-POLICY.md.

Examples

This demo includes an example learner-facing policy at assets/examples/ai-policy-intro-matlab-coached.md, relative to the demo folder that contains skills/ (not relative to this skill folder). Use it as a structural example only; replace the course name, activity rules, disclosure requirements, and local enforcement settings with the instructor's confirmed policy choices.

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 Create Ai Policy AI skill do?

Why use Matlab Create Ai Policy on TypingMind?

Because you install it once and use it with any model. Matlab Create Ai Policy 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 Create Ai Policy in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/matlab/agent-skills-playground/tree/main/demos/ai-tutoring/skills/matlab-create-ai-policy. 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 Create Ai Policy?

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 Create Ai Policy?

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

Is the Matlab Create Ai Policy 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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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

MATLAB AI Tutor Course Policy

Purpose

Interactive Interview

Output Workflow

Local Installation Rules

Output Constraints

Examples

Bundled files

Frequently asked questions

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