テスト・セキュリティのスキル

Decompose total effects into direct and indirect paths through mediators using mediation, CMAverse 4-way, HIMA/HIMA2 high-dimensional, BAMA, two-step / MVMR mediation, or double-ML medDML. Use when testing whether a molecular phenotype (expression, methylation, protein) mediates a treatment-outcome relationship, decomposing exposure-mediator interaction via VanderWeele 4-way, screening high-dimensional EWAS mediators, or running MR-based mediation when sequential ignorability is implausible.

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Market positioning strategy using the April Dunford framework, enriched with JTBD discovery, Moore positioning statement, and Neumeier's Onliness Test. Produces a complete positioning document, positioning statement, competitive alternatives map, and market category analysis. Use when the user wants to define or refine their market positioning, find their unique position, differentiate from competitors, craft a positioning statement, choose a market category, or figure out "how should we position this product." Triggers for "positioning", "how to position", "market position", "differentiation strategy", "positioning statement", "competitive positioning", "category strategy", "where do we fit in the market", "how are we different", "unique value proposition", or any request to define, sharpen, or rethink positioning. Works standalone — no prior startup-design or startup-competitors session needed, but leverages their output if available.

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Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.

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Generate GNSS baseband waveforms (GPS, Galileo, NavIC) with physically realistic or user-specified channel impairments using the Satellite Communications Toolbox. Use when generating GPS L1 C/A, L1C, L2C, L5, Galileo E1, E1C, E5a, E5b, E5, or NavIC L5, S, L1 signals. Covers gpsWaveformGenerator, galileoWaveformGenerator, satelliteScenario, Doppler/delay from orbital dynamics or custom values, navigation data encoding with ephemeris, and RINEX integration. Triggers on: GPS waveform, Galileo waveform, NavIC waveform, GNSS signal, satellite scenario, GNSS simulation, receiver test signal, baseband GNSS, L-band satellite signal, navigation signal generation.

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Transmit and capture RF waveforms using Wireless Testbench with NI USRP radios (X410, X310, N310, N320, N321, N300, X300, E320). Use when generating test signals, transmitting over the air, capturing IQ data, performing loopback tests, configuring multi-antenna setups, or troubleshooting dropped samples and gain settings. Covers basebandTransceiver, basebandTransmitter, basebandReceiver, continuous and once transmit modes, foreground and background capture, and UseRadioBuffer options. Also use when the user mentions transmit waveform, capture signal, IQ data, loopback, RF gain, sample rate, or antenna configuration.

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Generate standard-compliant IEEE 802.11 waveforms using MATLAB WLAN Toolbox. Use when creating WLAN waveforms, PPDU packets, or the transmit side of a link-level simulation. Covers all formats: Non-HT (802.11a/g), HT (802.11n), VHT (802.11ac), HE-SU/HE-MU/HE-TB (802.11ax), EHT-MU/EHT-TB (802.11be), UHR-MU/UHR-TB/UHR-ELR (802.11bn). Handles single-user, MU-MIMO, OFDMA, trigger-based uplink, extended range, preamble puncturing, UEQM, and DRU. Use when asked to generate test waveforms, create packets with MAC frames, configure OFDMA resource units, build trigger-based uplink transmissions, target a specific transmit duration, or build multi-packet waveforms.

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Discover and connect to Bluetooth Low Energy (BLE) peripheral devices from MATLAB. Use this skill when the user wants to scan for BLE devices, connect to a BLE peripheral, read or write BLE characteristics, subscribe to notifications, or any task that requires a BLE connection as a prerequisite (e.g., read sensor data over BLE, monitor heart rate, log accelerometer, stream data from wearable, IoT device communication). Triggers on: BLE, Bluetooth Low Energy, blelist, ble device, BLE characteristic, GATT, service UUID, scan for devices, BLE sensor, subscribe notify, BLE read, BLE write, peripheral device, BLE connect, wireless sensor.

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Connect to and acquire images from cameras in MATLAB using Image Acquisition Toolbox. Use this skill when the user wants to: use a webcam, acquire images, capture video, stream from a camera, connect to a GigE Vision camera, preview live video, list available cameras, configure camera properties, use a CoaXPress frame grabber, or use a Camera Link frame grabber. Covers USB webcams, GigE Vision, USB3 Vision, CoaXPress, Camera Link, and GenICam-compliant cameras. Always prefer videoinput from Image Acquisition Toolbox over webcam support package or gigecam.

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Use when setting up vehicle network communication in MATLAB using Vehicle Network Toolbox. Covers CAN/CAN FD (fully implemented), with architecture for J1939, XCP, and future protocols. Handles hardware discovery, channel creation, bus configuration, message exchange, signal encoding/decoding, and analysis across all supported vendors. (Vector, Kvaser, PEAK-System, NI, SocketCAN, MathWorks Virtual).

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Generate 3GPP-compliant 5G NR downlink and uplink baseband waveforms. Use to create NR signals, test model (TM) waveforms, fixed reference channels (FRC), test and measurement (T&M) signals, or test vectors for conformance testing. Covers configuring data, control, and broadcast channels and signals: PDSCH, PUSCH, PDCCH, PUCCH, SRS, SSBurst, CSI-RS, DM-RS, PT-RS, CORESET, and BWP parameters including bandwidth, subcarrier spacing (SCS), modulation (QPSK, QAM), numerology, FR1, FR2, TDD, FDD, and multi-bandwidth-part setups. Use for signal generation, RF instrument playback, or IQ baseband synthesis. Requires 5G Toolbox.

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Port MATLAB Data Acquisition Toolbox code from the discouraged (legacy) session-based interface (daq.createSession, addAnalogInputChannel, startBackground, DataAvailable listeners, queueOutputData, wait) to the recommended DataAcquisition interface (daq("ni"), addinput, start, ScansAvailableFcn, write, preload). Use when migrating legacy DAQ scripts, converting session-API calls, working with DataAcquisition objects, writing multi-feature DAQ scripts that combine triggers, callbacks, continuous acquisition, or analog output. Also use when a user says their DAQ script "used to work" or "errors on R20XX", since modernizing legacy session-API code is a frequent fix for those failures. Trigger keywords: daq, DAQ, NI, session interface, addtrigger, addclock, ScansAvailableFcn, ScansRequiredFcn, daq.createSession, addAnalogInputChannel, startBackground, queueOutputData, DataAvailable, evt.Data, evt.TimeStamps, wait(d), preload, discouraged, legacy, modernize, R2020a.

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Discover OPC UA servers, connect MATLAB clients, and browse/navigate server nodes using opcuaserverinfo, the Local Discovery Service (LDS), opcua, connect, setSecurityModel, certificate-trust functions, findNodeById, opcuanode, and Namespace/Children traversal. Use when finding OPC UA servers on the network, querying endpoints and security policies, connecting to a server, authenticating with username/password or certificates, configuring security modes, handling certificate-trust or hostname-mismatch errors, troubleshooting failed connections or empty discovery, inspecting an OPC UA certificate (.der/.pem), browsing an address space, finding nodes by name or NodeId, navigating node hierarchies, invoking method nodes, or batch-processing large namespaces. Trigger on: opcuaserverinfo, LDS, opcua, opc.ua.Client, setSecurityModel, opc.ua.trustServerCertificate, OPC UA certificate, findNodeById, opcuanode, browse OPC UA, find node, OPC UA namespace, OPC UA method node, Industrial Communication Toolbox.

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Model, simulate, and optimize Serializer/Deserializer (SerDes) systems — serial and parallel links — using MATLAB SerDes Toolbox. Design NRZ and PAM-N links (PAM3 through PAM16) — explore equalization architectures (FFE, CTLE, DFE), sweep or optimize parameters with genetic algorithms, and characterize channels from loss models, S-parameter files, or crosstalk scenarios. Process captured waveforms through equalization chains, build eye diagrams, and decompose jitter. Deliver IBIS-AMI models for Tx, Rx, Redriver, or Retimer by exporting to Simulink and compiling .ami/.ibs/.dll/.so files. Covers the full arc from initial design exploration and parameter optimization to compliance testing and compiled model validation, including custom datapath blocks for nonstandard equalization.

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Read BEFORE troubleshooting or enhancing camera image quality. Diagnoses and enhances image quality from cameras connected via Image Acquisition Toolbox or USB Webcams support package. Discovers camera capabilities at runtime, analyzes captured images for quality issues (brightness, contrast, sharpness, noise, color balance, backlighting), suggests hardware setting adjustments tailored to the specific camera, and applies Image Processing Toolbox enhancement functions. Use when a user wants to improve camera image quality, troubleshoot dark/blurry/noisy/grainy/ overexposed/washed out/color cast images, or optimize camera settings.

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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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Use when the user wants to use Arduino sensors or peripherals from MATLAB that are not directly supported by the MATLAB Support Package for Arduino Hardware. Triggers on requests to create, wrap, or expose a custom Arduino library to MATLAB. Also triggers on indirect questions like "How do I read humidity in MATLAB?" or "How do I display text on an LCD from MATLAB?" where the answer requires a custom Arduino add-on library. Covers: custom addon creation, third-party Arduino library integration, LibraryBase class, C++ header files, DependentLibraries, arduinoioaddons namespace, I2C/SPI/GPIO peripheral wrappers.

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Use when importing or exporting vehicle data from/to log files (MDF/MF4/DAT, BLF, ASC/TXT), decoding CAN/CAN FD/LIN messages to signals via DBC, ARXML, or LDF databases, writing timetable data to MDF or BLF files, or calling blfread, blfinfo, blfwrite, mdfRead, mdfWrite, mdfCreate, mdfInfo, canSignalImport, canMessageImport, canMessageTimetable, canFDMessageTimetable, canSignalTimetable, or linMessageTimetable.

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Build industrial-grade SCADA/HMI dashboards in MATLAB following industrial-HMI conventions (ISA-101-aligned): gray-field philosophy, alarms at source, write safeguards, fixed-range trends, drill-down layout. Produces a real App Designer app (.mlapp, or plain-text .m+.xml on R2026b+) by handing serialization to the matlab-build-app skill when available, and falls back to a programmatic .m app otherwise. Use when wrapping OPC UA / Modbus / MQTT / OSI PI / PI AF monitoring scripts into a live operator app, building plant overviews, designing operator dashboards, or any time a user asks for a "SCADA dashboard", "HMI", "plant dashboard", "operator screen", or "industrial monitoring app" in MATLAB. Trigger on: SCADA, HMI, industrial dashboard, plant overview, operator screen, uigauge, uilamp, alarm banner, gray-field, ISA-101, OPC UA dashboard, setpoint, write safeguards, alarm visualization, OSIsoft PI, AVEVA PI, PI Server, PI Data Archive, PI AF, PI Asset Framework, piclient, afclient.

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。

Discover, configure, and connect to Arduino boards from MATLAB using the Arduino Support Package. Use this skill when the user wants to set up an Arduino board, connect to Arduino hardware, find connected boards, scan serial ports, configure Arduino libraries, or any task that requires an Arduino connection as a prerequisite (e.g., blink LED, read sensor, read digital pin, read analog pin, write pin, plot sensor data, scan I2C, use ultrasonic sensor, control servo, read temperature, read voltage, data logging from hardware). Triggers on: arduino, board setup, COM port, serial port, hardware connection, Arduino Nano, Arduino Uno, Arduino Mega, Arduino Micro, Grove, sensor setup, I2C scan, ultrasonic, servo, readDigitalPin, writeDigitalPin, readVoltage, writePWMVoltage, temperature sensor, air pressure, pin read, pin write, analog input.

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Discovers connected MATLAB-supported hardware devices, searches by capability, and checks required support packages and add-ons. Use when the user asks what hardware is connected, finds a device that supports a capability (e.g., CAN, analog input), or checks if required support packages or add-ons are installed. Does NOT connect to, configure, or acquire data from devices — defers to device-specific skills for usage workflows.

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Find and query PI assets using MATLAB's Industrial Communication Toolbox. Two paths: (1) direct PI tag lookup via piclient + tags when user provides a tag name or description (R2022a+), (2) Asset Framework navigation via afclient when user needs element/attribute hierarchy (R2026a+). Use when working with PI AF servers, PI Data Archive tags, asset hierarchies, element templates, attribute lookup, or historical data from OSIsoft PI systems.

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Run MATLAB test suites and analyze results, including test filtering, parallel execution, result diagnostics, code coverage collection, gap analysis, and CI/CD with buildtool. Use when running tests, filtering test suites, analyzing test failures, checking, collecting, or analyzing coverage, justifying uncovered code, or configuring CI pipelines. Do NOT use for writing, generating, or structuring tests. Do NOT use for Simulink coverage or Simulink testing workflows.

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Generate and structure MATLAB unit tests using matlab.unittest and matlab.uitest features, including class-based tests, parameterized testing, fixtures, mocking, and app testing with gestures. Use when writing, generating, or adding tests, creating test classes, adding test methods, parameterizing tests, setting up fixtures, mocking dependencies, or testing App Designer apps. Do NOT use for running tests, collecting coverage, or CI/CD configuration.

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Writes MATLAB performance tests using the matlab.perftest.TestCase framework. Use when asked to write, create, or add performance tests for MATLAB code, benchmark functions, measure execution time with statistical rigor, or use runperf.

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。