matlab-plan-tutor-adoption
Use when an instructor asks for a MATLAB AI tutor setup guide, adoption guide, pilot plan, course-specific rollout, or recommended tutor configuration based on a learning objective, course title, course description, module description, or lab description.
git clone --depth 1 https://github.com/matlab/agent-skills-playground /tmp/matlab-plan-tutor-adoption && cp -r /tmp/matlab-plan-tutor-adoption/demos/ai-tutoring/skills/matlab-plan-tutor-adoption ~/.claude/skills/matlab-plan-tutor-adoptionSKILL.md
# MATLAB AI Tutor Instructor Setup Guide ## Purpose Generate a short instructor-facing setup guide that fits the provided learning objective, course title, course description, module description, or lab description. Treat the user's provided objective or course description as the guide input. If the input is missing, ask for one sentence describing the course, module, or learning objective before generating the guide. ## Input Handling Use the input argument to infer the best-fit teaching context: - introductory MATLAB programming course; - engineering computation course; - data analysis lab; - graded homework, lab, project, quiz, or exam support; - 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 a course goal. ## Guide Workflow 1. Restate the inferred course or learning objective in one sentence. 2. Identify the best-fit teaching context and why it fits. 3. Recommend the MATLAB AI tutor skill sequence for that context. 4. Provide an instructor-ready starter prompt that can be used with students. 5. Define guardrails for assignment or assessment use. 6. Recommend `matlab-create-ai-policy` when the instructor needs a learner-facing AI-use policy or local tutoring-session enforcement file. 7. Propose a first-week pilot activity with one MCQ, one hands-on task, and one reflection or transcript/report review step. 8. Explain how to review session reports and transcripts. 9. Include a short revision loop: what the instructor should adjust after the first 5-10 sessions. Read [references/setup-guide-template.md](references/setup-guide-template.md) for context mapping, output format, 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 why the recommended tutor behaviors are used. ## 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 prompt text, not only advice. - When the instructor asks for a demo, pilot, or adoption walkthrough, point to `assets/demo-kit/instructor-demo-kit-guide.md` (relative to the demo folder that contains `skills/`) and select only the demo-kit pieces that match their course context. - Use course-appropriate guardrails: stricter for graded work, lighter for optional practice. - Include `matlab-log-tutor-sessions`, `matlab-report-tutor-sessions`, and `matlab-evaluate-tutor-quality` when the guide involves instructor review. - Do not invent course policies. If policy is not provided, write a conservative default and mark it as adjustable. - Include a brief research-basis section only when requested or when the guide is intended for instructor adoption, departmental review, or pilot approval.
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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.
Use this skill for any work involving a MATLAB Project (.prj file) — creating a new project, tracking files, managing the project path, configuring Simulink cache and code-generation folders, running project health checks, or writing build scripts that keep the project in sync with the file system. Trigger phrases include "set up a MATLAB project", "create a .prj", "track this file in the project", "project health check", "build script conventions". This skill is the generic foundation; domain-specific skills (e.g. `mbse-workflow`) build on it.
Use this skill for the architecture phases of an MBSE workflow in MATLAB, when writing idempotent buildXxx.m scripts that produce a three-layer RFLPV architecture (Functional, Logical, Physical) with interface dictionaries, stereotype profiles, allocation sets, and requirements Implement links. Trigger for defining stereotype properties, functional-to-logical / logical-to-physical allocation, mapping requirements to components via slreq Implement links, or running quantitative roll-up analysis on the architecture. Do NOT trigger for ad-hoc structural edits to an already-built System Composer model (adding one component, rewiring a port) — use `building-simulink-models` with `model_edit` for that. Works alongside the `system-composer` skill for detailed SC API patterns.
Use this skill for guided MBSE work in MATLAB — starting a new project, resuming work mid-workflow on an existing project, or answering orientation questions about how the MBSE skills fit together. Trigger when the user says they want to create, start, or set up a new MBSE project; work on a model-based systems engineering / RFLPV project; or asks which skill covers which phase. Walks through phases one at a time — propose → approve → generate → run → confirm. Use proactively whenever someone mentions starting or continuing an MBSE project.
Use this skill for all requirements-related work in a MATLAB MBSE project using the Requirements Toolbox (slreq). Covers creating and populating requirement sets, derivation links, test case requirements, verification coverage, reading and tracing links across requirement sets and models, checking link health, allocating requirements to components (Implement links), and building traceability reports. Trigger when the user asks about slreq API, slreqx files, slmx link files, outLinks/inLinks, traceability matrices, coverage analysis, broken links, or mapping requirements to architecture components. Use proactively for any requirements or traceability task.
Use this skill when authoring reusable, idempotent MATLAB scripts that build System Composer architecture models via the architecture-modeling API — `systemcomposer.createModel`, `addComponent`, `addPort`, `setInterface`, `connect(srcPort, dstPort)`, interface dictionaries (.sldd) with `addInterface`/`addElement`, profiles/stereotypes with `Profile.createProfile` and `addStereotype`, or `systemcomposer.allocation.createAllocationSet`. Also trigger when debugging these APIs (connections that don't appear, interfaces that don't resolve, profile save errors, `createAllocationSet` signature-mismatch errors). Do NOT trigger for ad-hoc structural edits to an already-built model (adding one SubSystem, rewiring a port) — use `building-simulink-models` with `model_edit` for that.
Optimize MATLAB code for better performance through vectorization, memory management, and profiling. Use when user requests optimization, mentions slow code, performance issues, speed improvements, or asks to make code faster or more efficient.