MATLAB Development & AI Coding Projects

Statically analyze a Simulink model for Rapid Accelerator (raccel) mode incompatibilities. Identifies unsupported blocks (Interpreted MATLAB Function, Level-2 S-Functions without TLC), configuration issues, and data type restrictions that prevent code generation. Classifies each issue as BLOCKER (build error) or WARNING (build warning). Use BEFORE calling sim() in rapid-accelerator mode, when buildRapidAcceleratorTarget fails, or when the user asks "why won't my model run in raccel?" TRIGGER: any request explicitly involving rapid accelerator mode, raccel, raccel build errors, or buildRapidAcceleratorTarget failures.

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Use when you need to verify a Simulink model is compatible with SLCI code inspection, or to apply fixes for identified incompatibilities. Checks model configuration, identifies incompatibilities, and explains why each constraint is required for verification. Also invoked by slci-inspection-incompatibility-analysis to fix constraints and obtain compatibility results. Trigger keywords: checkCompatibility, SLCI compatibility, code inspection readiness, Model Advisor compatibility report.

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Set up signal capture for a Simulink Real-Time application using `slrealtime.Instrument`. Use when choosing which signals a real-time application captures and where the data goes — test-pointing or canvas-badging a signal before build (`TestPoint`, `DataLogging`), constructing an Instrument from an `.mldatx`, calling `addSignal`, `addInstrumentedSignals`, `addFileLogSignals`, `validate`, `connectCallback`, `copy`, or `generateScript`, attaching an instrument to `tg.Instruments` via `addForFileLog`, `addForSDI`, or `addForMATLAB`, setting up live-streaming signals, dynamic file-log workflows, or persisting an instrument as M-code. Selects and routes signals only — not for configuring, building, running, or connecting applications onto targets.

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Collect and assess quality metrics for Simulink models using the metric.Engine API. Use when evaluating model design quality (maintainability, complexity, architecture) or testing completeness (unit testing, SIL, PIL, coverage, pass rates) after edits or test runs. Covers metric discovery, collection, report generation, and diagnostics. Triggers on: "collect metrics", "quality metrics", "metric.Engine", "testing metrics", "maintainability metrics", "SIL metrics", "PIL metrics", "what metrics are available", "assess quality", "coverage metrics", "generate metric report", "diagnose metric errors".

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Configure a Simulink model for Simulink Real-Time code generation and deployment. Use when setting up a model for Simulink Real-Time, selecting the correct system target file, fixing TLC/STF mismatches, applying fixed-step solver settings, validating target-platform compatibility, or configuring Simulink Real-Time build options before model build.

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Use when SLCI code inspection has failed due to compatibility issues and you need to diagnose the incompatibilities. Traces inspection failures back to root-cause incompatibility settings or known limitations by matching failed code lines against known incorrect code generation patterns and documented limitation scenarios. Hands off to slci-compatibility-analysis for fixes. Trigger keywords: inspect, inspection failed, trace inspection errors, why did inspection fail, SLCI verification failures, limitation.

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Manage Simulink Real-Time target computers from MATLAB — list available targets, check whether a target is connected, read its lifecycle status, add/remove/rename targets, set IP addresses and credentials, choose the default target, install TLS material, configure a startup application, and run target lifecycle ops (connect, reboot, RTOS update). Use whenever the user asks "which targets are available", "is my target connected", "what's the status of the target", "add a Speedgoat", "switch default target", "change the target IP", "reboot the box", "update the target", or works with `slrealtime.Targets`, `tg = slrealtime`, `addTarget`, `removeTarget`, `getTargetNames`, `getDefaultTargetName`, `setDefaultTargetName`, `connect`, `isConnected`, `status(tg)`, `setipaddr`, `setupTLSCertificate`, `setStartupApp`, `reboot`, `update`, or `slrealtime.testSetup`. Covers Speedgoat target computers and Simulink Real-Time Linux devices.

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Build, deploy, run, monitor, tune, and stop Simulink Real-Time applications. Use when the user wants to build a Simulink Real-Time model, load/start/stop an `.mldatx` on a target, set stop time or start options, drive root inports/playback/parameter stimulation, inspect installed or running apps, tune parameters with `getparam`/`setparam`, read ad hoc signal values with `getsignal`, manage parameter sets, control recording, or choose between file logging, live streaming, and file-log import. Covers `slbuild`, `install`, `load`, `start`, `stop`, `setStopTime`, `tg.Stimulation`, `startRecording`/`stopRecording`, `tg.FileLog.import`, `slrealtime.exportRun`, `getparam`/`setparam`, `getsignal`, and installed app management for Speedgoat targets and Simulink Real-Time Linux devices.

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Customize A2L (ASAP2) files generated from Simulink models using coder.asap2 and coder.mapping APIs. Use this skill when adding COMPU_METHODs, creating GROUPs, converting STD_AXIS to COM_AXIS, adding BIT_OPERATIONs, creating VARIANT_CODING, excluding measurements or struct elements, removing DEFAULT_EVENT_LIST, or any A2L customization for calibration tool compatibility (INCA, CANape). Also use when the user mentions ASAP2, ECU calibration data, A2L export.

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Common steps for building multi-layer system architecture models using System Composer. Use when implementing architecture models or when interacting with interface dictionaries, allocation sets, stereotypes, and requirements for architecture components.

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Use this skill to run multiple Simulink simulations in parallel with parsim, batchsim, and DesignStudy. TRIGGER when: user asks for parameter sweep, sweeping/varying parameters across simulations, Monte Carlo study, batch simulation, running multiple simulations, parallel simulations, large-scale multiple simulations, any task involving repeated simulations with different parameter values, user mentions having an existing script/function for parameter sweeps, or user wants to migrate from parfor/set_param/for-loop patterns to recommended workflows.

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Use when a Simulink model is incompatible with Simulink Design Verifier (sldvcompat returns false, or an SLDV analysis errors out on an unsupported construct) and needs to be made analyzable. Diagnoses the incompatibility, applies the matching fix to a copy, and confirms the copy is compatible; optionally raises confidence the fix preserved behavior via a back-to-back consistency replay (agreement on the generated tests, not a proof of equivalence). Do NOT use for running test generation, design error detection, or requirement verification themselves — only for making a model compatible so those can proceed.

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Use this skill when the user asks to summarize, or asks why Simulink Coverage objectives are missing, unsatisfied, or uncovered on a coverage result they already have — why specific decision, condition, MCDC, relational-boundary, or saturation/overflow outcomes weren't exercised, whether uncovered logic is dead code, or how to close the gap (a test, a coverage filter, or a design change). It reads an existing cvdata, .cvt file, sim-with-coverage output, or Simulink Test result; it never runs cvsim to collect coverage.

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Tests Simulink models using either ephemeral Gherkin-based tests (model_test) for quick validation or persistent tests (Simulink Test API) authored from requirements or behavioral specs. Requires Simulink Test.

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Generates draft requirements from Simulink models. Use when drafting or updating requirement artifacts from a model. Prefers Requirements Toolbox (.slreqx) when available; falls back to structured YAML.

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Converts a double-precision Simulink system or subsystem to single precision using DataTypeWorkflow.Single (Fixed-Point Designer). The single conversion replaces all user-specified double-precision data types, as well as output data types that compile to double precision, with single-precision data types. Use this skill when converting Simulink systems to single precision, reducing memory usage of a Simulink system, optimizing for embedded targets. Do NOT use for standalone MATLAB .m code single conversion.

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Synthesize new waveforms from scratch for Simulink inports using createInputDataset. Use ONLY when the user asks to generate, create, or synthesize signals (step, ramp, sine, chirp, pulse, noise) to populate a Dataset for External Inputs or Signal Editor. Covers timeseries and timetable formats with correct data type, interpolation, units, and dimensions. Also covers function-call and trigger inport timing setup. Do NOT use when the user wants to load, import, or read existing data from files (MAT, CSV, spreadsheet) — even if that data will be used as model input. Do NOT use for running simulations or plotting outputs.

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Optimizes Simulink models for Embedded Coder generated code. Use when asked to optimize or improve generated code, or reduce code metrics for a Simulink model. Targets: execution time, memory footprint (RAM, ROM, stack, data copies), code size, MISRA compliance, or any semantically similar generated-code metric. Works iteratively — measures baseline, suggests changes, applies, and re-measures to confirm improvement. Triggers can be prompts similar to: optimize generated code runtime, reduce runtime, shrink code size, improve code efficiency, reduce memory usage, speed up generated code, follow MISRA compliance and so on. CAUTION: Do NOT attempt to optimize Simulink models for generated code efficiency without following this skill — the iterative measurement, gating, and rollback workflow is essential for safe optimization.

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Use the skill to control and verify the code interface configuration of your model — how Simulink® model elements are represented in the generated C or C++ code. This includes — (1) specifying names, types (storage classes), and placement of variables that represent model elements (for example, making a model parameter tunable as a global extern variable); (2) specifying names, types, and placement of functions that represent model algorithms; (3) selecting the deployment type (Component, Subcomponent, or Automatic); (4) selecting the interface configuration type (data or service interface); (5) linking a shared Embedded Coder dictionary to a model; (6) creating Embedded Coder dictionary entries and setting their properties; (7) for service interface configuration — specifying service interface definitions, including sender, receiver, client, and server services. Items 1 and 2 can be set per element or as a category-wide default. Item 1 applies to GRT and ERT models; the rest to ERT models only.

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Generate a standalone bug report that another developer can use to reproduce, investigate, and fix an issue. Use when the user says 'file a bug', 'write a bug report', 'report this issue', or asks to document a defect for handoff.

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Use when writing or editing Simscape Language (.ssc) source files — components, domains, or functions. Triggers include: model a physical component in .ssc; write physical equations for a Simscape block; add or modify physics or parameterization options in a Simscape component; build a composite component. Also use for validating .ssc syntax, fixing Simscape Language errors, or converting governing equations into working Simscape component code. Do NOT use for configuring or connecting existing Simscape blocks in a Simulink model — that is model building, not language authoring.

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Sanitize and anonymize a Simulink model for external sharing. Use when stripping proprietary content, anonymizing block/signal names, removing metadata, or creating a shareable reproduction model for bug reports. Do NOT use for simply copying or sharing a model internally.

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Use this skill when authoring, reworking, or validating a Classic AUTOSAR software component (SWC) in Simulink, including autosar.api mappings, runnables, timing events, sender-receiver ports, client-server operations, IRVs, BSW service callers, and component ARXML updates.

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Guide users through creating and managing .satk/block-policy.json for controlling which blocks the agent can use, which are excluded, and which block parameters the agent should not modify. Use when setting up block usage policy for a project.

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。