matlab-deploy-embedded-ai
Deploy AI models to embedded hardware using MathWorks tools (MATLAB, Simulink, Embedded Coder). Covers two workflow patterns: (1) MathWorks-native or imported models rebuilt as dlnetwork for lean hardware, (2) direct C/C++ code generation from PyTorch and LiteRT models. Both patterns support all targets (Cortex-M/A/R, x86, GPU). Trigger when: user wants to deploy AI to embedded targets; generate C/CUDA from neural networks; compress AI models for MCU; integrate AI in Simulink for system-level simulation; import PyTorch/ONNX/TensorFlow models for embedded deployment; optimize AI for resource-co
npx skills add matlab/matlab-agentic-toolkit --skill matlab-deploy-embedded-ai --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Embedded AI for Engineered Systems Deploy AI models to embedded hardware using MATLAB® and Simulink®. This skill is written specifically for **MATLAB R2026a** and uses APIs, functions, and workflows introduced in that release. It covers the complete lifecycle: model creation or import, verification, compression, system-level simulation, and code generation for resource-constrained targets. Requires MATLAB R2026a or newer. Core toolboxes: Deep Learning Toolbox, Statistics and Machine Learning Toolbox, MATLAB Coder, Embedded Coder, Simulink, and Fixed-Point Designer. Workflow-specific support packages are checked during Environment Discovery. The MATLAB and Simulink Agentic Toolkits must be available so the agent can drive a live MATLAB and Simulink session through MCP tools. ## When to Use - Deploying a trained neural network (MATLAB-native or imported) to embedded hardware - Generating C or CUDA code from a deep learning model for ARM Cortex-M/A/R, x86, or GPU targets - Deploying imported PyTorch, ONNX, TensorFlow, or LiteRT models to embedded targets (import step handled by `/matlab-import-external-ai-model`) - Compressing AI models (quantization, pruning, projection) to
- When to Use
- When NOT to Use
- Workflow Pattern Selection
- Decision Tree
- Pattern Summary
- Pattern 1 vs Pattern 2 Capability Comparison
- Common Start: Prerequisites
- Do Not Use (Legacy Functions)
- Global Rules
- Advisory vs Execution Mode
- ALWAYS
- ASK FIRST
- NEVER
- Related Workflows (Out of Scope)
What does the matlab-deploy-embedded-ai skill do?
Deploy AI models to embedded hardware using MathWorks tools (MATLAB, Simulink, Embedded Coder). Covers two workflow patterns: (1) MathWorks-native or imported models rebuilt as dlnetwork for lean hardware, (2) direct C/C++ code generation from PyTorch and LiteRT models. Both patterns support all targets (Cortex-M/A/R, x86, GPU). Trigger when: user wants to deploy AI to embedded targets; generate C/CUDA from neural networks; compress AI models for MCU; integrate AI in Simulink for system-level simulation; import PyTorch/ONNX/TensorFlow models for embedded deployment; optimize AI for resource-co
How do I install it?
Run `npx skills add matlab/matlab-agentic-toolkit --skill matlab-deploy-embedded-ai --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From matlab/matlab-agentic-toolkit, a repository with 868 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.