matlab

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.

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.

Connect to RoadRunner and import HD Map or OpenDRIVE files into a new 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.

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.

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

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

Display 3-D image volumes, medical image volumes, surface meshes, and annotations for 3-D image processing. Use when displaying 3-D images or isosurfaces with volshow, creating volume viewers with viewer3d, adding Regions of Interest (ROI) or annotations, overlaying masks or segmentations, streaming volumetric data, or building apps with volume display.

Build OCR pipelines in MATLAB using the ocr() function. Use this skill when the user wants to read text from images, documents, signs, meters, displays, license plates, gauges, receipts, or seven-segment displays. Covers image preprocessing, text detection (CRAFT, MSER), ROI-based recognition, multi-language OCR, and custom model training. Use when: OCR, text recognition, extract text from image, character recognition, document scanning, meter reading, gauge reading, receipt scanning, digitize text from photo.

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.

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.

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 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.

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.

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

Generate a standalone bug report that another developer can use to reproduce, investigate, and fix an issue. Use when the user says 'file a bug', 'write a bug report', 'report this issue', or asks to document a defect for handoff.

Generates draft requirements from Simulink models. Use when drafting or updating requirement artifacts from a model. Prefers Requirements Toolbox (.slreqx) when available; falls back to structured YAML.

Runs Simulink models programmatically for data exploration, parameter sweeps, and custom analysis using sim() with SimulationInput/SimulationOutput. Use when calling sim(), parsim, setExternalInput, setModelParameter, setVariable, or accessing logsout — any task producing simulation results for analysis (not pass/fail tests).

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.

Creates persistent Gherkin-based pass/fail tests for Simulink models and individual subsystems using model_test. Use when verifying expected behavior, writing regression tests, reproducing issues, or validating bug fixes with structured assertions. Requires Simulink Test.