개발 스킬

Use MATLAB Visual Inspection Toolbox to plan and build machine vision inspections for manufactured parts. Choose rule-based, AI-based, or combined methods for defect detection, measurement, counting, and part location, then prepare deployment paths for supported targets.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Choosing an inspection method for visible defects
• Measuring part dimensions in physical units
• Counting or locating parts in images

Analyze DC voltage and current across power nets in an imported PCB layout. The skill configures source, load, and sense components, checks IR drop and design limits, and supports batch analysis across multiple rails to help identify power integrity issues.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Checking voltage drop and current distribution on a PCB power rail
• Finding power nets and their connected components
• Checking current density, voltage margins, and via current limits

Investigate MATLAB errors and unexpected results by reading relevant code, checking variables, tracing calls, and testing focused hypotheses in a live session. It helps locate likely causes of runtime failures and verify practical fixes.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Trace the cause of a MATLAB error message
• Inspect variable values and dimensions at failure
• Find why a function returns an unexpected result

Plan and build a MATLAB app from its requirements. The skill helps choose between UIFigure and UIHTML, selects a layout pattern such as a dashboard or wizard, and can save UIFigure apps as App Designer files or plain-text formats.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Choosing an architecture for a MATLAB GUI
• Building a dashboard, wizard, or interactive tool
• Saving a UIFigure app in an App Designer format

Inspect a specified MATLAB installation folder and list the installed products and support packages. Use it to check whether a particular toolbox or package is present, or to review the installation contents for a given MATLAB release.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Listing installed MATLAB products and toolboxes
• Checking whether a specific toolbox is installed
• Reviewing support packages for a MATLAB release

Design and analyze reflector antennas, reflectarrays, reconfigurable intelligent surfaces, and antennas mounted on platforms with MATLAB Antenna Toolbox. Select an electromagnetic solver, inspect antenna performance, or calculate radar cross section for a target.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Designing a parabolic or dual-reflector antenna
• Evaluating a reflectarray or reconfigurable surface
• Analyzing an antenna mounted on an aircraft or vehicle

Use a measured, seven-step workflow to reduce memory use in MATLAB programs. Establish a baseline, profile allocations to find bottlenecks, apply focused code changes, compare memory use, verify results, and summarize the findings.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigate MATLAB out-of-memory errors
• Find functions that allocate large amounts of memory
• Reduce memory use when processing larger datasets

Build and solve finite element models in MATLAB PDE Toolbox, from geometry and mesh setup to thermal, structural, or electromagnetic analysis. Extract values such as stress and heat flux, and visualize results to inspect how a model behaves.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Creating a geometry and setting up a finite element model
• Solving a thermal, structural, or electromagnetic problem
• Inspecting stress, displacement, heat flux, or other results

Update legacy MATLAB code that uses removed or discouraged functions and patterns. Map older APIs to current alternatives for tasks such as file import, dates, strings, and dynamic evaluation, and address patterns that Code Analyzer may not flag.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Replacing removed MATLAB functions in legacy code
• Migrating csvread or xlsread to current file-reading functions
• Finding discouraged patterns that Code Analyzer misses

Turn a folder of MATLAB code into an installable `.mltbx` toolbox. The workflow guides you through defining its public interface, checking dependencies, preparing documentation and a build plan, assessing readiness, creating the package, and publishing a versioned release, with review points along the way.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Packaging a MATLAB code folder as a toolbox
• Preparing MATLAB code for sharing as an installable add-on
• Reviewing dependencies and build readiness before release

Diagnose MATLAB `parfor` errors by running the code analyzer, matching its messages to variable classification issues, and applying focused fixes. Use it to repair a loop or convert a `for` loop while preserving its results, then verify the changes with the analyzer.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Fixing a `parfor` variable classification error
• Converting a `for` loop to `parfor`
• Reviewing a MATLAB file with a reported `parfor` problem

Prepare MATLAB parallel pool workers with paths, environment variables, connections, libraries, or data before parallel work begins. The guidance helps manage per-worker resources and setup, and modernize spmd-based setup patterns for parfor and parfeval workflows.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Make a library available to each parallel worker
• Load a data file once per worker for parfor
• Set paths or environment variables on pool workers

Use this skill to define or review MATLAB function inputs with `arguments` blocks. It helps choose size and type checks, handle repeating and name-value arguments, avoid unintended conversions, and migrate validation from `inputParser` or `validateattributes`.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Adding input checks to a MATLAB function
• Migrating validation from `inputParser`
• Reviewing type and size behavior

Review MATLAB code with static analysis and MathWorks coding guidelines. The skill checks naming, function structure, input patterns, and common performance or maintainability issues, then reports findings by severity with suggestions for handoff or publication.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Reviewing MATLAB code quality
• Checking MathWorks style conventions
• Preparing code for handoff

Read and write CSV, Excel, Parquet, JSON, XML, and text files in MATLAB. Get guidance on choosing import options, handling URLs and encodings, checking for data changes during import, and diagnosing common file errors.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Importing a spreadsheet with mixed text and numeric columns
• Reading a CSV that uses European decimal and delimiter conventions
• Checking imported Excel or Parquet data for silent value changes

Analyze RF PCB components in MATLAB by extracting S-parameters, checking insertion and return loss, and viewing fields or currents. Compare electromagnetic solvers, tune mesh density, and use frequency sweeps to balance analysis speed and accuracy.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Checking RF component S-parameters and insertion loss
• Comparing MoM and FEM solver results
• Viewing electric fields or current distributions

Improve MATLAB code performance through a measured seven-step workflow: establish a baseline, profile execution, identify bottlenecks, optimize, measure again, verify results, and report. It helps focus changes on proven slow sections while checking that outputs remain equivalent.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Finding why a MATLAB function runs slowly
• Profiling code before making changes
• Comparing timing before and after optimization

Try a thread-based MATLAB parallel pool when local `parfor`, `parfeval`, or `spmd` runs slowly, especially when large data transfers or process-pool startup add overhead. It explains how to test thread compatibility, interpret errors, and make Threads the persistent default.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Improving slow local `parfor` performance
• Reducing overhead from large broadcast variables
• Checking whether code can run on a thread pool

Add OpenTelemetry tracing to MATLAB functions to follow work across parent and child spans. The skill guides span creation, context propagation, error recording, attributes, events, and naming conventions so traces provide useful detail when reviewing a workflow.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Adding spans to MATLAB functions
• Following parent and child operations
• Recording errors and useful attributes

Migrate C, C++, or Fortran MATLAB MEX source from the Separate Complex API to the Interleaved Complex API. Replace legacy data access calls, optionally retain compatibility with both API modes, and build and compare outputs to verify the conversion.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Migrating legacy MEX code to the Interleaved Complex API
• Replacing mxGetPr and mxGetPi in C or C++ MEX files
• Updating Fortran MEX files for -R2018a builds

Creates repeatable MATLAB performance tests with `matlab.perftest.TestCase` and `runperf`. The tests measure execution time using warmups and multiple samples, helping compare input sizes and detect regressions over time while keeping setup outside the measured section.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Adding repeatable performance tests to MATLAB code
• Benchmarking a function across input sizes
• Checking for performance regressions over time

Improve an existing MATLAB design file for GPU Coder by compiling, profiling, and benchmarking generated CUDA code. The workflow checks numerical results after changes and addresses GPU performance diagnostics such as memory transfers and limited parallelism.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Speeding up an existing MATLAB function on a GPU
• Profiling generated CUDA code or GPU MEX
• Investigating GPU memory transfers and parallelism

Generate C, C++, or CUDA code from MATLAB functions, check that it matches MATLAB results, and tune settings or measure compiled MEX performance. It also helps prepare MATLAB code for code generation and address compatibility constraints.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Preparing MATLAB functions for C or C++ generation
• Generating CUDA code from MATLAB
• Checking generated results against MATLAB

Analyze an OpenSCENARIO 1.x .xosc file and produce a structured description for recreating its actors, positions, routes, actions, and triggers in RoadRunner. It resolves references and flags unsupported constructs before scenario authoring proceeds.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Recreating a partner-provided .xosc scenario in RoadRunner
• Translating scenario positions and routes
• Generating a MATLAB-ready scenario description