matlab-plan-course-adoption
Use when an instructor asks for a setup guide, adoption guide, planning guide, implementation checklist, prerequisite check, MATLAB Course Designer course structure plan, Common Cartridge workflow, Simulink starter and solution model planning, LMS import plan, or starter prompt for creating a MATLAB and Simulink enabled course with the matlab-generate-course skill.
git clone --depth 1 https://github.com/matlab/agent-skills-playground /tmp/matlab-plan-course-adoption && cp -r /tmp/matlab-plan-course-adoption/demos/course-generation/skills/matlab-plan-course-adoption ~/.claude/skills/matlab-plan-course-adoptionSKILL.md
# MATLAB Course Instructor Setup Guide Generate a short instructor-facing setup guide for adopting the MATLAB Course Designer Generator skill package. The guide prepares an instructor to run `matlab-generate-course`, review MATLAB and Simulink activities, use the `matlab-create-course-activity` skill to wrap `matlab-generate-grader-assessments` and validate MATLAB Exercise `.m` files, use `simulink-create-course-activity` for Simulink Exercise starter and solution model files, and review the final Common Cartridge package as the persisted course representation. The guide must frame generated content around the MATLAB Course Designer organization model: a course contains ordered modules, and modules contain ordered learning activities. Common Cartridge is the persistence format used outside the web application until future API work creates course instances directly. ## Input Handling Treat the user's course title, syllabus idea, module description, audience, lab sequence, existing course, or rollout goal as the guide input. If the input is missing, ask for one sentence describing the course or rollout context. Infer: - Course context and learner level - MATLAB and Simulink scope - Likely assessment approach - Common Cartridge persistence target and any downstream LMS import target - Course Designer module and learning activity structure - Prerequisite skill/toolkit readiness - Whether MATLAB Exercises are likely needed and should be generated through `matlab-create-course-activity` - Whether Simulink Exercises need starter and solution `.slx` files generated by `simulink-create-course-activity` Do not ask follow-up questions unless the input is too vague to identify a course goal. ## Guide Workflow 1. Restate the inferred course goal. 2. Identify the best-fit course generation context. 3. List prerequisite skill packages and MathWorks access assumptions, always including IDStack, the MATLAB MCP Server, and the activity skills by name. 4. Recommend a `matlab-generate-course` starter prompt. 5. Recommend when and how to use `matlab-create-course-activity` for MATLAB Exercise component generation, MATLAB MCP validation, and Course Designer-only fields. 6. Recommend when and how to use `simulink-create-course-activity` for Simulink Exercise model artifacts. 7. Define Course Designer module and learning activity review gates. 8. Define Common Cartridge export and persisted course review checks. 9. Propose a first pilot with one module or one short course shell. 10. Include a concise instructor checklist. Read [references/setup-guide-template.md](references/setup-guide-template.md) for output format, context mapping, prompt patterns, and checklist content. ## Output Rules - Default to Markdown unless the user asks for another format. - Keep the guide practical enough for an instructor to act on immediately. - Include a concrete `matlab-generate-course` starter prompt. - Include concrete `matlab-create-course-activity` prompts when MATLAB Exercises are likely. - State that Common Cartridge persists the course shell and instructions outside MATLAB Course Designer; MATLAB and Simulink execution happens in the appropriate MathWorks environment. - Keep institutional policy language adjustable unless the user provides local requirements.
Use this skill when the user wants to author, design, scope, or refine an Agent Skill (a SKILL.md file). Trigger phrases include "build a new skill", "design an agent skill", "scope a SKILL.md", "how should I structure this skill", "write a skill for X", "my skill isn't working well", or any request to improve an existing SKILL.md. Walks the user through an empirical, test-first process — probe the agent for real failures, design only for genuine knowledge gaps, iterate against runnable examples, and verify across models.
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Use when a learner asks for help with MATLAB homework, labs, projects, graded assignments, take-home exams, quizzes, or any programming task where academic integrity, course policy, or instructor constraints may limit direct solutions. Use to provide policy-aware hints, conceptual coaching, partial feedback, and assignment-safe MATLAB tutoring.
Use when tutoring a learner through MATLAB debugging, error interpretation, failed tests, incorrect outputs, array-shape problems, indexing mistakes, function argument issues, or code repair practice. Use for guided debugging sessions, debugging drills, teach-the-agent critique, and evidence-based MATLAB troubleshooting.
Use when an AI tutor session concerns MATLAB programming concepts, MATLAB syntax, MATLAB errors, MATLAB code style, MATLAB projects, or MATLAB toolbox workflows.
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.
Create MATLAB Course Designer MATLAB Exercise learning activities by wrapping the existing matlab-generate-grader-assessments skill, then validating generated solution.m, template.m, and tests.m files with MATLAB MCP Server tool calls. Use when the user asks to create a MATLAB Exercise, MATLAB course activity, Course Designer MATLAB activity, validated MATLAB solution file, or MATLAB Exercise component folder.
Use when prompting a learner to complete hands-on MATLAB coding exercises, guided practice, debugging drills, code tracing, small MATLAB projects, or MATLAB-script assessment during tutoring. Use when the tutor should create a complete runnable MATLAB script, execute it through MATLAB tools, compare the produced outputs with expected outputs, and evaluate MATLAB programming style.