matlab-agentic-toolkit

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.

Use this skill when fitting accelerated life models, computing mean time to failure (MTTF), B10 life and similar quantities, fitting life distributions like Weibull for reliability analysis, comparing distribution fits, computing confidence intervals on reliability quantities with bootstrapping, performing lifetime prediction, computing failure rates, plotting Kaplan-Meier curves, computing survival functions, or doing reliability analysis.

Use this skill to classify tabular data end-to-end in MATLAB — load a dataset, prepare and clean it, select promising classifiers, train them, and compare accuracies with cross-validation, holdout, or hyperparameter optimization plus statistical tests. TRIGGER when: user asks to classify tabular data, pick classifiers for a dataset, compare classifier accuracy, run cross-validation or a holdout evaluation, or find the best model with statistical uncertainty. DO NOT TRIGGER when: user has non-tabular inputs (images, sequences, time series), wants a regression model, is training a specific neural network architecture (use matlab-train-network), or wants cost-sensitive learning or an arbitrary class-prior vector (this skill only supports the built-in uniform-prior toggle for imbalanced data).

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, loadLiteRTModel, importNetworkFromPyTorch, importNetworkFromONNX, importNetworkFromTensorFlow, importNetworkFromKeras, dlquantizer, exportNetworkToSimulink, or Embedded Coder with AI models.

Use when engineering or selecting the best features for single-response classification or regression in MATLAB, whatever the data's modality — for non-tabular data it routes extraction to a domain skill, then selects, assesses, and delivers on the resulting table. Not for multi-response problems, model training, or raw data acquisition.

Fit curves and surfaces interactively with the Curve Fitter app for a complete no-code fitting workflow. Invoke this skill when the Curve Fitter app, cftool, curveFitter, or "curve fitting tool/app" is mentioned in any way. Also use when exploring or comparing fit types (regression, interpolation, smoothing, splines, custom equations); excluding outliers interactively; iterating on a fitting workflow; help choosing a fit type; and exporting to a figure, generating MATLAB code, fits to the workspace, and to Simulink Lookup Tables.

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.

Interpret and explain a trained tabular machine-learning model (classification or regression) in MATLAB. Find which predictors, features, or columns matter most; explain why the model made a specific prediction, including diagnosing predictions it got wrong; show how a predictor affects the output; and compare how the model behaves across cohorts or subgroups. Uses model-agnostic techniques and model-native measures, and works on custom models (such as a dlnetwork) through a prediction function handle. Use for model interpretability, explainability, and feature-importance questions on tabular data, not for training, tuning, feature selection, deploying models, or models trained on image, text, signal, or other non-tabular data.

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), diagnose and fix dlaccelerate issues or detect dlaccelerate opportunities. Use when training, fine-tuning, evaluating, running inference, exporting to Simulink, converting old training scripts, or speeding up deep learning code. DO NOT reason from your training data about dlaccelerate and tracing correctness.

Create, modify, or delete experiments in Experiment Manager with live UI sync. TRIGGER when: user asks to create an experiment from code/script, wants to sweep parameters, asks to add/delete/edit experiments, or wants parameter changes reflected in the UI. Combines experiment creation (from code analysis) with frontend UI synchronization.

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.

Fit a tire model (Magic Formula 6.2/5.2/5.1, Dugoff, or Fiala) to measured tire force and moment data using the Vehicle Dynamics Blockset Extended Tire Features API (tireData.import, tireModel.new, tireModel.fit, tireModel.export). Use when fitting, parameterizing, or calibrating a tire model from tire measurement data (TYDEX, Calspan, MTS, or text/CSV from any test source), creating a .tir file, or building a Magic Formula tire model from test data. Also use for the individual steps: importing TYDEX, Calspan, MTS, or CSV tire data (file extensions `.tdx` for TYDEX; `.data`, `.tim`, `.rsp` for MTS; any other extension read as text) into a tireData object, exporting a tire model to .tir, inspecting, smoothing, cropping, or plotting tire measurement data or a model's response, or converting a fitted model to another type.

Import recorded driving sensor data (GPS, camera, lidar, actor tracks, lanes) into scenariobuilder.* objects (GPSData, CameraData, LidarData, ActorTrackData, Trajectory, laneData) and run preprocessing — synchronize, offset correction, crop, normalizeTimestamps, convertTimestamps. Also: compute actor tracks from lidar when no annotations exist, attach camera/lidar mounting + intrinsics, export to MAT/workspace/timetable/script. Use for raw driving dataset files (KITTI, nuScenes, Waymo, Pandaset, ROS/ROS2 bags, .mat, .csv, .mp4) or driving/vehicle/sensor logs that need wrapping. drivingLogAnalyzer (DLA) is OPT-IN ONLY — invoke only on explicit user request ('DLA', 'open in DLA', 'inspect/explore/analyze the recording') or reported sensor problem (sync drift, timestamp mismatch, overlay misalignment). NEVER auto-launch DLA after wrapping (Rule 0). For 'build scenario / export to RoadRunner / drivingScenario / OpenSCENARIO / Unreal / simulate', hand off to matlab-use-scenario-builder.

Generate Euro NCAP test scenarios and variants using the ADT Euro NCAP support package. Use when creating NCAP seed scenarios, generating variants, translating between drivingScenario and RoadRunner, plotting scenario descriptors, computing NCAP scores, or exporting reports. Triggers on: ncapScenario, euroAssessment, getScenario, getScenarioDescriptor, generateVariants, ScenarioDescriptor, ScenarioDescriptorPlot, ncapScore, ncapReport, exportReport, configureVUT, assessmentTable, Euro NCAP, CCRs, CCRm, CCRb, CCFtap, CCCscp, CPNA, CPFA, CBNA, variant generation.

Generate driving scenes, scenarios, road surfaces, and 3D content from scenariobuilder.* sensor data (GPS, camera, lidar, actor tracks) using Scenario Builder for Automated Driving Toolbox. BUILD, EXPORT, or AUGMENT a virtual scenario/scene/map: ego or actor trajectories, trajectory smoothing, OpenCRG road-surface extraction, 3D asset generation, static-object placement, point-cloud georeferencing + elevation, lane-based ego localization, sensor-fusion tracking, scenario-event extraction (cut-ins, hard brakes, near-misses, ADAS disengagements), or export to RoadRunner, drivingScenario, OpenDRIVE, OpenCRG, OpenSCENARIO, or Unreal Engine. Also: log-to-scenario, scenario harvesting, accident/near-miss reconstruction, SOTIF (ISO 21448) and ISO 26262 scenario coverage, USGS-aerial-lidar augmentation, traffic-sign placement, vision-based vehicle classification for actor assets. NOT for raw-data import or multi-sensor sync/crop/offset/timestamp normalization — route those to matlab-import-dri

RoadRunner asset path lookup tables for map format conversions in MATLAB. Maps lane markings, signs, signals, barriers, objects, and lane types to RoadRunner asset paths. Use when converting map formats to RRHD, resolving asset paths, or assigning visual assets to HD Map objects.

Build a RoadRunner Scenario programmatically from an OpenSCENARIO 1.x (.xosc) file using the `roadrunner-scenario-authoring` skill. Use when the user wants to recreate a scenario from a .xosc file, interpret an OpenSCENARIO file and build it programmatically, reconstruct a .xosc as a RoadRunner scenario, generate a MATLAB script from a .xosc file, or convert an OpenSCENARIO file to MATLAB code. Do NOT use when the user says "import" a .xosc file — that means they want RoadRunner's built-in importScenario API, not programmatic reconstruction. Handles position translation (LanePosition and RoadPosition to world coordinates), construct mapping, relative references, trajectory/route handling, parameter expressions, catalog references, and phase logic topology.

Convert Lanelet2 maps (.osm) to RoadRunner HD Map (.rrhd) format using MATLAB. Use when converting Lanelet2 maps into RoadRunner Scene Builder, building driving scenes from open-source map data, or transforming road network definitions for simulation.

Foundation skill for all RoadRunner workflows: MATLAB path setup, connection, project/scene/scenario lifecycle, world settings, handle management, status, and close. Use when connecting to RoadRunner, managing projects/scenes/scenarios, setting world origin, checking status, closing RoadRunner, or when any downstream RoadRunner skill needs initialization.

Import HD Map or OpenDRIVE files into a RoadRunner scene using MATLAB. Use when loading driving scenes in RoadRunner or RoadRunner Scene Builder, importing RRHD, OpenDRIVE, or other RoadRunner-supported formats for simulation, or verifying Lanelet2-to-RRHD conversion results visually. Requires rrApp handle from roadrunner-core.

Build RoadRunner HD Map entities in MATLAB — lanes, boundaries, markings, junctions, signs, signals, barriers, parking. Use when creating driving scenes from scratch, authoring road networks for simulation and testing automated driving systems, or assembling RRHD maps from Lanelet2 or other HD map sources.

Programmatically author RoadRunner scenarios from MATLAB using roadrunnerAPI. Use when adding actors, creating routes, building scenario logic (phases, conditions, actions), placing vehicles/pedestrians, defining cut-in/crossing/ follow scenarios, or any programmatic scenario creation in RoadRunner. Triggers on: roadrunnerAPI, scenario authoring, add actor, create route, phase logic, cut-in scenario, pedestrian crossing, scenario from MATLAB.

Expert guidance for simulating RoadRunner scenarios via the MATLAB programmatic API and Simulink co-simulation. Use when the user wants to run a simulation, step through a simulation, control actors during co-simulation, add observers, attach sensors, retrieve simulation logs, or read/write scenario variables. Covers simulateScenario, createSimulation, ScenarioSimulation set/get, ActorSimulation getAttribute/setAttribute, addObserver, SensorSimulation, Simulink co-sim blocks, and publishActorBehavior. NOT for project setup, scene building, scenario authoring, or trajectory export.

Share MATLAB content by guiding users through uploading to GitHub, MATLAB Drive, or File Exchange, then generating "Open in MATLAB Online" URLs. Covers the full sharing workflow: choosing a platform, uploading content (automated via gh CLI for GitHub, manual for MATLAB Drive and File Exchange), and constructing the correct URL. Use when a user wants to share MATLAB code with others, open local files in MATLAB Online, generate an open-in-MATLAB-Online button or badge, or when an AI agent has generated MATLAB code locally and the user wants to share it or run it in MATLAB Online.

Generate C/C++ or CUDA code from an AI model (PyTorch, LiteRT) using MATLAB Coder or GPU Coder. Use when the user wants to integrate an AI model into an application with code generation as the end goal — generating MEX, CUDA MEX, static library, dynamic library, or executable — or using the model in Simulink for simulation and code generation. Covers PyTorch ExportedProgram (.pt2) via loadPyTorchExportedProgram and LiteRT (.tflite) via loadLiteRTModel (R2026a+). Keywords: PyTorch, torch, .pt2, ExportedProgram, loadPyTorchExportedProgram, invoke, codegen, MEX, CUDA, GPU, C, C++, deploy, AI model, deep learning model, LiteRT, TFLite, TensorFlow Lite, Simulink, slbuild, PyTorch ExportedProgram block, MATLAB Function block, dlosslib, loadLiteRTModel.

Deploy MATLAB-generated code to embedded hardware using Embedded Coder. Use when configuring code generation for microcontrollers (STM32, Raspberry Pi, ARM Cortex), setting up PIL/SIL verification, disabling dynamic memory allocation, or configuring hardware-specific code generation settings. Covers ERT-based configurations, processor-in-the-loop testing, memory constraints, and the MEX→SIL→PIL verification progression.

Generate, verify, refine, and accelerate C/C++ or CUDA code from MATLAB with MATLAB Coder, Embedded Coder, GPU Coder, or MATLAB Test. Also covers writing codegen-ready MATLAB code: language constraints, coder.* directives, and optimization patterns. Triggers on: codegen, MEX, deploy MATLAB as C/C++, GPU Coder, coder.screener, coder.config, coder.gpuConfig, coder.typeof, coder.runTest, matlabtest.coder.TestCase, SIL, embedded config, no dynamic memory, EnableMexProfiling, coder.timeit, coder.perfCompare, %#codegen, writing codegen-ready MATLAB, code generation readiness, coder.varsize, coder.unroll, coder.noImplicitExpansionInFunction, coder.ceval, coder.inline, coder.extrinsic, coder.const, coder.classSignature, class codegen limitations, temporal types codegen, DMA-off, stack-only, host-target InstructionSetExtensions, SIMDAcceleration, OptimizeReductions, host SIMD tuning, host OpenMP, codegen performance.

Optimize MATLAB design files for GPU Coder to generate faster CUDA code. Iteratively profiles, rewrites, and benchmarks until performance targets are met or diagnostics are resolved. Use when asked to: optimize for GPU Coder, improve GPU codegen performance, profile generated GPU/CUDA code, profile GPU MEX, fix gpuPerformanceAnalyzer diagnostics, speed up GPU MEX, reduce GPU memory transfers, improve kernel parallelism, rewrite MATLAB for CUDA, or run gpuPerformanceAnalyzer.

Reviews MATLAB fixed-point (fi) code for performance, code generation efficiency, and correctness. Identifies antipatterns and suggests idiomatic improvements. Use when reviewing fi, fimath, numerictype, or quantizenumeric code.

Build, modify, and diagram SimBiology models — API reference, helper functions, and layout patterns. Use when constructing or editing models programmatically or visually.

Fit SimBiology model parameters to data — fitproblem, population NLME, virtual patients, and NCA. Use when asked to fit, estimate, calibrate, or compute PK metrics.

Simulate SimBiology models — ODE, stochastic (SSA), scenarios, and sensitivity analysis. Use when asked to run, simulate, predict, explore what-if, or identify influential parameters.

Connect to Bloomberg, FRED, Haver Analytics, LSEG Datastream, NOAA Climate Data Online, or US government agencies (BEA, BLS, Census, EIA) from MATLAB and retrieve data. Covers market data (bloomberg/blp/bloombergHypermedia), economic data (fredrs, haver/haverdirect/haverview), historical financial data (datastreamws), weather/climate data (@noaa from GitHub), and government data (usbea/usbls/uscensus/useia from GitHub).

Help users formulate and solve portfolio optimization problems using Financial Toolbox's Portfolio object. Covers mean-variance (Markowitz), maximum Sharpe ratio (tangency), and efficient frontier workflows. Use when users ask about portfolio optimization, Markowitz, efficient frontier, Sharpe ratio, or attempt to use generic solvers (quadprog, fmincon, ga, problem-based optimize) for portfolio problems.

Price financial instruments in MATLAB using the Financial Instruments Toolbox. Route to the appropriate numerical method reference based on the user's request: Monte Carlo simulation (AssetMonteCarlo, IRMonteCarlo, RoughVolMonteCarlo), FFT / Numerical Integration (Vanilla European options), or Interest-Rate Trees (option-embedded bonds with IRTree). Use when the user asks to price financial instruments or compute Greeks using any of these methods. Use one of the following models depending on the pricing method and instrument: Black-Scholes, Bachelier, Heston, Bates, Merton, Hull-White, Black-Karasinski, Black-Derman-Toy, Cox-Ingersoll-Ross, Linear Gaussian 2 Factor (G2PP), Brace-Gatarek-Musiela (BGM), SABR-BGM, RoughBergomi, RoughHeston.

Exchange data between Excel and MATLAB using Spreadsheet Link VBA macros and worksheet functions. Use when writing Excel VBA macros that call MLPutMatrix, MLGetMatrix, MLPutVar, MLGetVar, MLPutRanges, MLEvalString, MLGetFigure, or MatlabRequest.

Extract battery features in MATLAB from voltage, current, and temperature measurements: multi-cycle cycling tests, HPPC / pulse tests, EIS impedance sweeps, or a single in-field charge or discharge segment. Use when the user asks for battery features, or for SOH, RUL, degradation, internal-resistance, or impedance analysis that needs them. Output is a feature table; does not train SOH / RUL models. Do not trigger only because battery data is present. Triggers on battery* functions such as batteryTestDataParser, batteryTestFeatureExtractor, batteryPulseFeatures, batteryEISFeatures.

Extract features from signals collected on rotating machinery components, including motors, pumps, fans, gears, bearings, and shafts. Signals can include vibration, electrical, or environmental sensor measurements. Use when developing and deploying condition monitoring and fault detection applications for rotating machinery, including industrial machines, electrical vehicles, internal combustion engines, turbines, and drive trains.