「toolkit」の検索結果

Translate NI-DAQmx C function signatures into correct calldaqlib MATLAB calls. Use when the user mentions calldaqlib, any DAQmx* C function name (DAQmxCfgSampClkTiming, DAQmxExportSignal, DAQmxSetStartTrigRetriggerable, DAQmxGetSampClkRate, etc.), or needs NI-DAQmx functionality not exposed by the daq object. Covers: projection rules (taskHandle implicit, output pointers become return values, buffer-size args, enum-as-string), multi-task semantics, start() clobber, device-level empty-daq workaround, dictionary returns, string getter placeholder, array getter placeholder. Trigger contexts: low-level DAQ, escape hatch, retriggerable acquisition, shared timebase, export signal, connect terminals, external sample clock, trigger configuration, NI driver, C function translation.

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Integrate antennas into RF systems using MATLAB Antenna Toolbox and RF Toolbox. Covers impedance matching network design (L/Pi/Tee topologies, evaluation parameters, Richards transformation), measured antenna creation (E-field, directivity-only, EmbeddedE, ffsReader import), RF propagation and site planning (txsite/rxsite, coverage, SINR, ray tracing, link budget), and SAR estimation (birdcage+Phantom, conformalArray+Custom3D, direct EHfields). Use when the user wants to match an antenna, create a measuredAntenna, compute coverage or signal strength, perform ray tracing, or estimate SAR.

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Design, select, and analyze waveforms for radar, sonar, and active sensing using the Phased Array System Toolbox. Covers LFM, NLFM, FMCW, phase-coded, CW, stepped FM, custom IQ, ambiguity functions, sidelobe reduction, and Doppler tolerance. Key objects: phased.LinearFMWaveform, phased.NonlinearFMWaveform, phased.CustomFMWaveform, phased.PhaseCodedWaveform, phased.FMCWWaveform, phased.SteppedFMWaveform, phased.MFSKWaveform, phased.RectangularWaveform, nlfmspec2freq, shapespectrum, ambgfun, pambgfun, sidelobelevel, legendreseq, mlseq, radarWaveformGenerator.

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Design antennas, arrays, and PCB antennas using MATLAB Antenna Toolbox. Covers catalog antenna design and pattern analysis, custom antenna construction (customAntenna + shape.*), PCB antenna design (pcbStack + antenna.*), finite and infinite array design, AI-accelerated design exploration (AIAntenna, patternFromAI), and optimization (SADEA/TR-SADEA). Use when the user wants to design, create, model, analyze, optimize, or fabricate an antenna or array.

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S-parameters, insertion loss, fields, currents, mesh control, and solver selection for RF PCB performance validation. TRIGGER: user asks to compute S-parameters, analyze insertion/return loss, extract fields or currents, compare MoM vs FEM, or control mesh for any RF PCB component. Invoke BEFORE writing sparameters() or solver code — API is non-obvious. SKIP: designing or creating components (use the specific matlab-design-pcb-* skill), material/stackup setup only (use matlab-manage-pcb-material), optimization sweeps (use matlab-optimize-pcb-design), PDN/IR-drop analysis (use matlab-analyze-pcb-pdn).

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Store, retrieve, and pass credentials securely in MATLAB using the built-in MATLAB Vault (setSecret, getSecret, importSecrets, secretID) instead of hardcoding. Handles connections to any authenticated service: REST APIs, databases, cloud storage (S3/Azure/GCS), SFTP, and others. Covers API keys, tokens, passwords, SSH passphrases, CI/batch/scheduled jobs, and "keep credentials out of code" requests. Does NOT cover third-party secret managers (HashiCorp Vault, AWS Secrets Manager), OS-level key management, or the connection/query logic itself.

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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.

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Call Python libraries from MATLAB using the py. interface, pyrun, pyrunfile, or pyenv. Use when writing or executing MATLAB code that calls Python functions or passes data between MATLAB and Python. REQUIRED when triaging Python errors from MATLAB (ModuleNotFoundError, ImportError, "Unable to resolve the name 'py.*'"). REQUIRED when setting up Python environments for MATLAB, creating virtual environments, or installing Python packages for use with MATLAB.

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Read BEFORE optimizing any MATLAB code for speed. Without this workflow, agents commonly optimize the wrong target, fabricate speedup claims without measurement, or introduce regressions. Guides the 7-step workflow: baseline, profile, identify, optimize, measure, verify, report.

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Design and analyze electrically large antenna structures using MATLAB Antenna Toolbox. Covers reflector antennas (parabolic, Cassegrain, Gregorian, offset, corner, cylindrical, spherical, custom STL), reflectarrays and reconfigurable intelligent surfaces (RIS), antennas installed on platforms (vehicles, aircraft, ships, satellites), and radar cross section (RCS) analysis. Includes solver selection (MoM-PO, PO, MoM, FMM), mesh control, and GPU acceleration. Use when the user wants to design a dish/reflector antenna, reflectarray, analyze an antenna on a platform, or compute RCS.

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Configure, simulate, debug, and analyze radarDataGenerator within radarScenario. Use for: interactively building radar detection scenarios from datasheets or performance requirements; diagnosing missed detections and configuration errors; interpreting sensor spherical, body, and scenario-frame outputs; deriving ReferenceRange from hardware specs via link budget; scan mode configuration (mechanical, electronic/AESA, hybrid); and validating simulation results against analytical predictions.

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Use DuckDB from MATLAB via Database Toolbox (R2026a+) as a non-math operations engine on large tabular files (CSV/Parquet/JSON) and as a zero-config embedded database. Use when connecting to DuckDB, querying CSV, Parquet, and JSON files directly with SQL, reducing or profiling large data before MATLAB analysis, creating portable development databases, or installing DuckDB extensions. Triggers on: DuckDB, duckdb(), large CSV/Parquet/JSON, file too large for readtable, filter/aggregate at source, deduplicate, reduce before analysis, profile large file, persistent file import, analytical engine, SQL on CSV, SQL on Parquet, SQL on JSON, query CSV with SQL, query Parquet with SQL, run SQL on files, SQL queries on files, query files directly, SQL without database, in-process SQL.

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Reads from, writes to, and manages relational databases using MATLAB Database Toolbox. Use when connecting to databases, reading data with sqlread or fetch, filtering with rowfilter, writing with sqlwrite, updating with sqlupdate, executing SQL statements, managing transactions with commit and rollback, mapping MATLAB classes to tables with ORM (Mappable, ormread, ormwrite, ormupdate), or performing any database operation from MATLAB. Triggers on: database, SQL, sqlread, sqlwrite, sqlupdate, fetch, execute, rowfilter, RowFilter, ORM, Mappable, ormread, ormwrite, ormupdate, orm2sql, transaction, commit, rollback, Database Toolbox, PostgreSQL, MySQL, SQLite, SQL Server, Oracle, database connection, database table, query database, insert data, update rows, delete rows, stored procedure, prepared statement, odbc, databaseConnectionOptions, datasource, data source, DSN, connection string, multithreaded, parallel.

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Unified skill for all MATLAB and Simulink report generation tasks. Covers report generation, PDF report, document generation, mlreportgen, Report Generator, template, TitlePage, Chapter, multipage HTML, HTMLPage, html-multipage, custom reporter, customizeReporter, template hole, page header, page border, company style package, branded reporters, buildTemplates, Simulink report, model documentation, slreportgen, block diagram report, model report, subsystem documentation, SystemDiagramFinder, SystemIO, ExecutionOrder, LookupTable, template style, page footer, TemplateSrc, report package, table variants, multiple HTML pages, PDF report generation, Word report, DOM API, FormalTable, BaseTable, Figure, FormalImage, Section.

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。

Build custom PCB structures with pcbComponent, shapes, Boolean ops, feeds, and multi-layer stackups for non-catalog geometries. TRIGGER: user asks to build, modify, or customize a pcbComponent — add/remove shapes, edit polygons, place feeds, add metal layers, cut slots, or create non-catalog RF structures. Also when modifying geometry of an existing catalog-designed component (e.g., adding pads, removing elements, editing vertices). Invoke BEFORE writing pcbComponent code — layer/shape/feed API is non-obvious. SKIP: designing catalog components like filters/couplers/txlines (use the specific matlab-design-pcb-* skill), material/stackup definition only (use matlab-manage-pcb-material), EM analysis (use matlab-analyze-em), importing PCB files (use matlab-read-pcb-layout).

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PDN DC voltage/current analysis, IR drop, design rule checking, and multi-net batch analysis on imported PCB layouts. TRIGGER: user asks about power integrity, PDN analysis, IR drop, voltage distribution, current density, power nets, or design rule checking on a PCB. Invoke BEFORE writing code — the PDN API chain is specialized and non-obvious. SKIP: importing a PCB file (use matlab-read-pcb-layout), EM field/S-parameter extraction (use matlab-analyze-em), material/stackup setup only (use matlab-manage-pcb-material), transmission line design (use matlab-design-pcb-transmission-line).

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。

Guides the 7-step MATLAB memory optimization workflow: baseline, profile, identify, optimize, measure, verify, report. Use when asked to reduce MATLAB memory usage, find memory bottlenecks, fix out-of-memory errors, or optimize memory-intensive code.

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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.

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Analyze AMS waveform data using Mixed-Signal Blockset utilities: phase noise measurement, clock jitter, anti-aliased resampling, timing measurements, lock time, INL/DNL, ADC/DAC calibration, HSpice import. Use when analyzing time-domain voltage from PLL/VCO/clock simulations, measuring phase noise from variable-step solver output, computing jitter, or resampling non-uniform data.

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Turn raw MATLAB code into a published .mltbx toolbox — full pipeline from scope definition through packaging and release. Drives 8 phases with human checkpoints, enforcing ordering and dependencies. Use when asked to package, create a toolbox, or run the files-to-package pipeline.

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Read or write data files in MATLAB. Use when the task involves tables, spreadsheets, delimited text, or structured files in CSV, Excel, Parquet, JSON, or XML format — including but not limited to importing, exporting, loading, parsing, converting, validating, configuring import options, reading from URLs, handling locales or encodings, diagnosing file errors, and modernizing legacy file I/O code. MATLAB provides built-in functions for these workflows with no additional products required.

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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.

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Build machine vision inspection systems with MATLAB Visual Inspection Toolbox. Covers the full arc from problem selection through deployment: anomaly detection (Student-Teacher, PatchCore, FastFlow, FCDD), object detection (YOLOX), counting (CounTR, shape matching), gauging and measurement (caliper, fitcircle, fitangle), blob analysis (regionprops, calibrated measurement), shape matching for fixturing and localization, synthetic data generation for training augmentation, and deployment to standalone apps, embedded C/C++, NVIDIA GPUs (TensorRT), NPUs (ONNX), and Beckhoff PLCs. Routes to rule-based tools when problems are geometric and deterministic, AI-based tools when defect appearance varies, and hybrid pipelines combining both.

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Use when writing, solving, or debugging MATLAB optimization code — formulating problems (optimproblem, optimvar, fcn2optimexpr), selecting and configuring solvers (fmincon, linprog, quadprog, intlinprog, lsqnonlin, ga, surrogateopt, optimoptions), or validating results (exitflag, convergence, constraint violations). Covers problem-based and solver-based approaches, solver tuning, and solution verification.

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。