The MATLAB Agentic Toolkit brings trusted MATLAB capabilities to AI agents, making engineering and scientific workflows agent-ready.
- ✓License: NOASSERTION
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git clone https://github.com/matlab/matlab-agentic-toolkit && cp matlab-agentic-toolkit/*.md ~/.claude/agents/24 items en este repositorio
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-scenario-builder.
Generate driving scenes, scenarios, road surfaces, and 3D content from already-wrapped scenariobuilder.* sensor data (GPS, camera, lidar, actor tracks) using Scenario Builder for Automated Driving Toolbox. Use to 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 scene augmentation, traffic-sign placement from camera+lidar logs. NOT for raw-data import or multi-sensor sync/crop/offset/timestamp normalization — route those to matlab-driving-data-importer.
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.
Display images and annotations for image processing, computer vision, and visual inspection. Use when displaying images with imageshow, creating image viewers with viewer2d, adding Regions of Interest (ROI) or annotations, overlaying masks or segmentations, streaming video frames, or building apps with image display.
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.
Patterns for using blockedImage to process large images, harness parallel compute for image processing, and write custom adapters. Use when writing code that creates, processes, or visualizes blockedImage objects, when implementing images.blocked.Adapter subclasses, or when a user needs help with large image data. Always use this skill when working with TIFF,GeoTIFF, .svs, .ndpi, .czi or other WSI, satellite imagery or microscopy volume image formats.
Build MATLAB apps programmatically using uifigure, uigridlayout, UI components, callbacks, and uihtml for web integration. Use when creating GUIs, dashboards, interactive tools, apps with sliders/buttons/dropdowns, or embedding HTML/JavaScript components.
Create plain-text MATLAB Live Scripts (.m files) with rich text formatting, LaTeX equations, section breaks, and inline figures. Use when generating tutorials, analysis notebooks, reports, documentation, or educational content. Requires R2025a+.
Diagnose MATLAB errors and unexpected behavior. Breakpoints, workspace inspection, try-catch diagnostics, and common error patterns. Use when debugging functions, tracing errors, inspecting variables, or diagnosing runtime failures.
Deterministic workflow to download MATLAB Package Manager (mpm) and install MathWorks products from the OS command line with consistent, repeatable behavior. Use when installing MATLAB, Simulink, toolboxes, or support packages via command line, or setting up scripted installations for CI/CD, containers, or fleet provisioning.
Show all installed MATLAB products and support packages for a given MATLAB installation folder. Use when listing, checking, or verifying what products or support packages are in a MATLAB installation.
Review MATLAB code for quality, performance, maintainability, and adherence to MathWorks coding standards. Uses check_matlab_code and matlab_coding_guidelines. Use when reviewing code, checking style, finding code smells, assessing quality, or preparing code for handoff or publication.
Generate and run MATLAB unit tests using matlab.unittest and matlab.uitest. Parameterized tests, fixtures, mocking, coverage analysis, CI/CD with buildtool, app testing with gestures. Use when creating tests, writing test classes, running test suites, checking coverage, testing apps, or validating MATLAB code.
Analyze tabular data using MATLAB. Use when the task involves tables, timetables, or time-series data — including but not limited to exploring, filtering, sorting, cleaning, transforming, aggregating, smoothing, and answering questions about data. MATLAB provides extensive, easy-to-use built-in functions for these workflows with no additional products required.
Analyze the effective toolbox file set to produce a Dependency Manifest — classify all transitive dependencies as included, product, add-on, or external-unresolved, then present resolution options with tradeoffs. Use after matlab-define-toolbox-api when the spec is approved.
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¿Qué es matlab/matlab-agentic-toolkit?
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matlab/matlab-agentic-toolkit es subagents para el ecosistema de Claude AI. The MATLAB Agentic Toolkit brings trusted MATLAB capabilities to AI agents, making engineering and scientific workflows agent-ready. Tiene 618 estrellas en GitHub y se actualizó por última vez 8d ago.
¿Cómo se instala matlab-agentic-toolkit?
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Puedes instalar matlab-agentic-toolkit clonando el repositorio (https://github.com/matlab/matlab-agentic-toolkit) o siguiendo las instrucciones del README en GitHub. ClaudeWave también te ofrece bloques de instalación rápida en esta misma página.
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Nuestro agente de seguridad ha analizado matlab/matlab-agentic-toolkit y le ha asignado un Trust Score de 87/100 (tier: Trusted). Revisa el desglose completo de comprobaciones superadas y flags en esta página.
¿Quién mantiene matlab/matlab-agentic-toolkit?
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matlab/matlab-agentic-toolkit es mantenido por matlab. La última actividad registrada en GitHub es de 8d ago, con 9 issues abiertos.
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Sí. En ClaudeWave puedes explorar subagents similares en /categories/agents, ordenados por popularidad o actividad reciente.
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