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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.

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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.

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Identify a linear dynamic model from input-output or time-series data using MATLAB System Identification Toolbox. Use when estimating transfer function, state-space, ARX, ARMAX, BJ, OE polynomial or process models from measurement data.

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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-driving-data.

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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.

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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.

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Read, write, and manipulate medical imaging data (DICOM, NIfTI, NRRD) in MATLAB. Covers Image Processing Toolbox functions (dicomreadVolume, niftiread, dicomContours, dicomanon) and Medical Imaging Toolbox enhanced APIs (medicalVolume, medicalImage, medicalref3d, extractSlice, updateOrientation). Use when reading medical files, listing DICOM series, extracting spatial referencing, changing orientation, working with RT structures, or anonymizing DICOM data. Some features require Medical Imaging Toolbox — see skill body and references for details.

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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.

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Build conversational AI voice agents on the ElevenLabs platform. Configure agent + tools + knowledge base, integrate SDK (React / React Native / Swift / JS / server-side), test, deploy. Use whenever the user mentions ElevenLabs, building a voice agent, an AI phone system, an AI receptionist, conversational AI, or troubleshooting deprecated @11labs packages, webhook errors, CSP violations, localhost allowlist, or tool parsing errors.

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Use when generating or transforming audio with ElevenLabs models on Scenario via MCP: text-to-speech with inline emotion tags, music from a prompt or sectioned songs, sound effects and loops, dubbing audio or video into another language, re-voicing a recording with a preset or cloned voice, or isolating speech from noise. Keywords: ElevenLabs, Eleven v3, Multilingual v2, Turbo 2.5, Music v2, Sound Effects 2, Dubbing, Voice Changer, Speech to Speech, Voice Isolator, TTS, SFX, localization.

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Expert TISAX (Trusted Information Security Assessment Exchange) advisor for the automotive supply chain — the ENX/VDA assessment regime that OEMs like VW, BMW, and Mercedes-Benz require from suppliers and service providers. Covers the VDA ISA 6 catalogue (current through 2026) and the ISA2027 transition (published July 1, 2026; mandatory for assessments ordered from January 1, 2027), assessment levels AL1/AL2/AL3, all 12 assessment objectives/labels (Confidential, Strictly Confidential, High/Very High Availability, Proto Parts/Vehicles, Test Vehicles, Proto Events, Data, Special Data), maturity scoring (0–5, target 3, cutback rules), the ENX portal process, scoping, audit-provider selection, corrective action plans and the 9-month window, 3-year label validity, and ISO 27001 mapping. Use for any TISAX, VDA ISA, ENX, automotive information-security assessment, prototype protection, or OEM supplier-security requirement question — gap assessments, label selection, readiness, and audit prep.

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Run an attorney-facing knockout/availability trademark search across the United States and Canada. Use whenever a user (founder, client, or attorney) asks whether a brand, mark, name, logo, slogan, or product name is "available," "free to use," "clearable," or "already taken" — or asks Claude to "search the trademark register," "check USPTO," "check CIPO," "do a knockout search," "clearance search," "availability search," or "see if [mark] is trademarked." Trigger even when the user phrases it casually ("can I call my company X?", "is this name taken?"). Covers federal USPTO (TESS/TSDR), federal Canada (CIPO), common-law/web/state, and WIPO Madrid international. Output is a knockout availability memo with green/yellow/red verdict for attorney review. Includes professional-responsibility gating (AI-use disclosure, conflict check, scope confirmation) before substantive analysis. Not legal advice — produces work product for attorney sign-off.

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Design AI-supported collaborative tasks that structure group interaction and address participation problems. Use when students struggle to collaborate effectively on group tasks.

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Expert knowledge for Azure Education Hub development including troubleshooting, and limits & quotas. Use when managing Azure for Students credits, yearly quotas, renewals, or Dev Tools for Teaching sign-in issues, and other Azure Education Hub related development tasks. Not for Azure Lab Services (use azure-lab-services), Azure DevTest Labs (use azure-devtest-labs), Azure Virtual Desktop (use azure-virtual-desktop).

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Build brand partnership strategy — co-branding campaigns, brand collaborations, licensing deals, partner brand alignment, and joint marketing. Use when the user says "brand partnership", "brand collab", "co-branding", "brand collaboration", "partnership marketing", "brand alliance", "co-branded product", "licensing deal", "partner with another brand", "brand collaboration strategy", "how do we partner with brands", "co-marketing partnership", "joint campaign", or wants to build credibility, reach, or revenue through alignment with another brand.

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编写MATLAB程序实现自定义霍夫变换，用于离散点（含浮点坐标）的直线检测，输出斜率和截距。不使用拟合或MATLAB自带函数，需解决索引非整数问题。

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Build GitLab CI/CD pipelines with multi-stage workflows, caching, and distributed runners for scalable automation. Use when implementing GitLab CI/CD, optimizing pipeline performance, or setting up automated testing and deployment.

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Use for ElevenLabs provider-specific dubbing, localization, voice changer, speech-to-speech, and voice isolation/enhancement workflows. Applies when an agent must prepare source media, choose ElevenLabs dubbing versus voice conversion, manage language/speaker/timing decisions, handle voice identity and consent, call or plan against documented ElevenLabs APIs, export dubs/transcripts, estimate cost/limits, or quality-control localized speech while avoiding generic text-to-speech guidance.

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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.

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Use when a learner or instructor asks for a report, summary, reflection, progress note, performance recap, activity metrics, instructor-shareable record of a MATLAB AI tutoring session, aggregate report across multiple MATLAB tutoring session reports, or instructor dashboard artifact with metric drilldowns. Supports optional start and end datetime arguments for multi-session report and dashboard ranges.

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Use when reviewing, auditing, scoring, or improving a real or synthetic MATLAB AI tutor transcript, tutoring prompt, generated lesson, exercise, feedback sequence, or skill behavior for MATLAB accuracy, active learning, assignment guardrails, feedback quality, debugging support, transfer prompts, and instructor-facing quality recommendations.

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Generate beautiful, distinctive HTML/CSS/JS control panels for MATLAB uihtml components. 8 built-in styles (Clean, Material, Cosmic Dark, Neumorphic Dark, 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.

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Use when tutoring a learner through MATLAB debugging, error interpretation, failed tests, incorrect outputs, array-shape problems, indexing mistakes, function argument issues, or code repair practice. Use for guided debugging sessions, debugging drills, teach-the-agent critique, and evidence-based MATLAB troubleshooting.

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Use when an instructor asks for a MATLAB AI tutor setup guide, adoption guide, pilot plan, course-specific rollout, or recommended tutor configuration based on a learning objective, course title, course description, module description, or lab description.

日本語の概要は準備中です。原文の説明を表示しています。