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.
git clone --depth 1 https://github.com/matlab/agent-skills-playground /tmp/matlab-plan-grader-adoption && cp -r /tmp/matlab-plan-grader-adoption/demos/assessment-generation-for-matlab-grader/skills/matlab-plan-grader-adoption ~/.claude/skills/matlab-plan-grader-adoptionSKILL.md
# 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, or Object usage. 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. Provide an instructor-ready generation prompt for the `matlab-generate-grader-assessments` skill. 6. Define review gates for `description.txt`, `solution.m`, `template.m`, `function_call.m` when the recommended type is Function, and `tests.m`. 7. Define QTI 3 export and sharing guidance when portability is requested. 8. Propose a first pilot with one generated item, one review pass, and one revision loop. 9. Include a concise instructor checklist. Read [references/setup-guide-template.md](references/setup-guide-template.md) for context mapping, output format, prompt patterns, and adaptation rules. Read [references/research-summary.md](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.
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.