matlab

Compute aerospace environment properties including atmosphere (ISA, COESA, NRLMSISE-00, non-standard, CIRA), gravity (spherical harmonic, WGS84, zonal, centrifugal), horizontal wind (HWM), magnetic field (WMM, IGRF), geoid height, geocentric radius, space weather data, planetary ephemeris, Earth orientation (polar motion, nutation, delta-UT1, CIP). Use when computing atmospheric density, temperature, pressure, gravity vectors, wind profiles, magnetic field components, geoid undulation, solar flux indices, planet positions, or Earth orientation parameters for aerospace vehicle analysis, spacecraft environment modeling, or navigation corrections.

Perform aerospace unit conversions, time conversions, coordinate frame transformations, and rotation representations using Aerospace Toolbox. Use when converting units (length, velocity, angle, acceleration, angular velocity, force, mass, pressure, temperature, density), computing Julian dates or decimal years, transforming between coordinate frames (ECEF, ECI, LLA, flat Earth, geodetic/geocentric, NED, body, wind, stability), or working with rotation representations (Euler angles, DCM, quaternion, Rodrigues vector). Also use when the user asks about aerospace coordinate systems, reference frames, or rotation conventions.

Create an experiment for the MATLAB Experiment Manager app from user code, script, or problem description. TRIGGER when: user asks to create an experiment from a script/function, wants to sweep parameters or hyperparameters, asks to compare configurations, or describes a problem suitable for experimentation. DO NOT TRIGGER when: user is debugging an existing experiment, wants to run/resume trials, or already has a working experiment set up.

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-constrained hardware; or use loadPyTorchExportedProgram, importNetworkFromPyTorch, dlquantizer, exportNetworkToSimulink, or Embedded Coder with AI models.

Import PyTorch, ONNX, or Keras 3 / TensorFlow 2.16+ deep learning models into MATLAB as dlnetwork objects. Use when importing .pt2 exported programs, traced .pt files, .onnx models, or Keras 3 models via matlabsaver. Covers importNetworkFromPyTorch, importNetworkFromONNX, importNetworkFromKeras, importNetworkFromTensorFlow, torch.export.export, PyTorchInputSizes, InputDataFormats, matlabsaver, tf_keras downgrade, numeric validation against PyTorch or ONNX Runtime, and placeholder/custom layer implementation. Applies when user mentions any of these functions, file formats, or encounters import errors, unsupported operator warnings, 0 learnables, or uninitialized networks.

Train, evaluate, and export neural networks to Simulink in MATLAB. Migrate legacy (fitnet, patternnet) and discouraged (trainNetwork, DAGNetwork) code to modern, recommended R2024a+ APIs (trainnet, dlnetwork, testnet, imagePretrainedNetwork). Use when training, fine-tuning, evaluating, running inference, exporting to Simulink, or converting old training scripts.

Use when the user wants to train, compare, or export machine learning models using Classification Learner or Regression Learner — including opening the app, loading data, training models, evaluating metrics, comparing results, visualizing plots, testing on held-out data, exploring model interpretability, and exporting trained models. Programmatic access to Classification Learner and Regression Learner apps via AppController.

Build Simulink models that co-simulate with Eclipse SUMO traffic simulator. Use when creating SUMO-Simulink co-simulation, traffic simulation, TraCI connection, vehicle-in-the-loop testing, or ADAS scenario validation with SUMO. Covers Server/Client setup, Reader/Writer/Actor block configuration, random traffic generation, ego vehicle control, and SUMO file creation. Also use when the user mentions SumoInterfaceLibrary, .sumocfg files, or wants to connect Simulink to an external traffic simulator.

Builds and edits Simulink, System Composer, Stateflow, and Simscape models. Use when modifying model structure, parameters, ports, connections, or Stateflow chart internals.

Manages MATLAB projects for Simulink workflows: path management, file registration, labels, source control configuration, and project lifecycle. Use when creating projects, adding models/dictionaries/requirements to projects, configuring labels for automation, fixing broken model references, or setting up source control for Simulink artifacts.

Declare, add, or update custom Simulink block libraries and configure block policy and knowledge index. Use when a user wants to register, set up, configure, or add new files to their custom libraries for agent-assisted model building.

Specify algorithms for Model-Based Design: system specs, architecture specs, implementation plans, test plans. Use when creating specifications for controllers, signal processing, diagnostics, estimators, or other algorithms authored in Simulink, Stateflow, System Composer, or MATLAB Function blocks.

Specify plant models for closed-loop simulation: system specs, architecture, build plans, validation plans. Use when creating, updating, or reviewing plant model specifications, planning plant model architecture, or planning plant model validation.

Synthesize new waveforms from scratch for Simulink inports using createInputDataset. Use ONLY when the user asks to generate, create, or synthesize signals (step, ramp, sine, chirp, pulse, noise) to populate a Dataset for External Inputs or Signal Editor. Covers timeseries and timetable formats with correct data type, interpolation, units, and dimensions. Also covers function-call and trigger inport timing setup. Do NOT use when the user wants to load, import, or read existing data from files (MAT, CSV, spreadsheet) — even if that data will be used as model input. Do NOT use for running simulations or plotting outputs.

Author or upgrade Model Advisor checks for Simulink and System Composer models. Use when creating new checks (edit-time, standard batch, config-parameter, auto-fix) or converting legacy StyleOne/StyleTwo/StyleThree checks to modern DetailStyle. Covers DetailStyle callbacks, ResultDetail reporting, sl_customization registration, and edit-time EdittimeCheck classes.

Checks a Simulink model against a compliance standard (MISRA, MAB, JMAAB, ISO 26262, ISO 25119, DO-178C, DO-254, IEC 61508, IEC 62304, EN 50128, CERT C/CWE, AUTOSAR) using Model Advisor, then summarizes findings and suggests fixes. Use when the user asks whether their model is compliant, wants to run standard checks, or needs a compliance report.

Deploy AI models to embedded hardware using MathWorks tools (MATLAB, Simulink, Embedded Coder). Covers two workflow patterns: (1) MathWorks-native or 3P-imported models rebuilt as dlnetwork for lean hardware (Cortex-M, DSP), (2) direct C/C++ code generation from PyTorch and LiteRT models for high-performance hardware (Cortex-A, x86, GPU). Trigger when: user wants to deploy AI to embedded targets; generate C/CUDA from neural networks; compress AI models for MCU/DSP; integrate AI in Simulink for system-level simulation; import PyTorch/ONNX/TensorFlow models for embedded deployment; optimize AI for resource-constrained hardware; or use loadPyTorchExportedProgram, importNetworkFromPyTorch, dlquantizer, exportNetworkToSimulink, or Embedded Coder with AI models.

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.

Build interactive web applications using HTML/JavaScript interfaces with MATLAB computational backends via the uihtml component. Use when creating HTML-based MATLAB apps, JavaScript MATLAB interfaces, web UIs with MATLAB, interactive MATLAB GUIs, or when user mentions uihtml, HTML, JavaScript, web apps, or web interfaces.

Generate beautiful, distinctive HTML/CSS/JS control panels for MATLAB uihtml components. 8 built-in styles (Clean, Material, Cosmic Dark, Neumorphic, Dashboard Light, Midnight Gradient, Minimal Mono, Warm Dark) plus custom aesthetics. Produces production-grade UI with sliders, buttons, toggles, and panels. Use when building visually polished MATLAB app UIs with uihtml.

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.

Generate correct MATLAB code using the Symbolic Math Toolbox. Use when the user asks for symbolic computations, analytical solutions, symbolic differentiation/integration, equation solving, or converting symbolic results to numeric MATLAB functions. Also use when converting differential equations to transfer functions or state-space form.

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 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.