# Simulink Streaming Dsp

Building or editing Simulink (.slx) models that stream signals in frames using DSP...

- Repository: https://github.com/matlab/simulink-agentic-toolkit (1305 stars)
- Category: AI and agents
- License: see repository
- Last updated: 2026-10-29
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add matlab/simulink-agentic-toolkit --skill simulink-streaming-dsp -g -a claude-code -y`
Codex: `npx skills add matlab/simulink-agentic-toolkit --skill simulink-streaming-dsp -g -a codex -y`
Cursor: `npx skills add matlab/simulink-agentic-toolkit --skill simulink-streaming-dsp -g -a cursor -y`
Gemini CLI: `npx skills add matlab/simulink-agentic-toolkit --skill simulink-streaming-dsp -g -a gemini-cli -y`
Claude Code plugin marketplace: `/plugin marketplace add matlab/simulink-agentic-toolkit`

## Description

Building or editing Simulink (.slx) models that stream signals in frames using DSP System Toolbox, audio, vibration, radar, comms. Use when the prompt names a Simulink block (Discrete FIR Filter, Buffer, Unbuffer, Rate Transition, Downsample, Upsample, Sample-Rate Converter, FIR Decimation, Time Scope, Spectrum Analyzer, From Multimedia File) or an action (frame-based processing, InputProcessing, buffering, overlap, windowing, STFT, short-time Fourier, decimate, downsample, upsample, resample, multirate, anti-aliasing, tunable filter). Prevents silent numerical errors from sample-vs-frame mismatch, wrong block choice, or missing anti-aliasing.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# Simulink Streaming DSP

Build streaming (frame-based) signal processing models in Simulink using
DSP System Toolbox. This skill prevents silent errors from wrong block
selection, missed parameter settings, and outdated filter architectures.

The most damaging failures here are **silent**: the model compiles, simulates,
and produces output, but the numbers are wrong, with no error or warning.
Apply the Block Selection and Frame-Based Configuration rules on every build,
even for tasks a strong model would usually get right, they are the cheap
insurance against silent corruption and they hold across models and effort levels.

## When to Use

- Building a Simulink model that processes signals in frames (not sample-at-a-time)
- Choosing source, sink, or visualization blocks for a DSP pipeline
- Adding filters to a streaming model (FIR, IIR, tunable)
- Buffering signals (scalar→frame, frame resizing, overlapping windows, unbuffering)
- Converting sample/frame rates (decimate, interpolate, fractional resample)
- Building multirate models (dual-path, different frame sizes, mixed rates)
- Working with variable-size signals (packet-based, adaptive frame sizes)
- Building a tunable/adjustable filter with runtime parameter control
- Visualizing signals or filter responses (Time Scope, Spectrum Analyzer, Filter Visualizer)
- Computing buffering latency or frame timing

## When NOT to Use

- MATLAB-only scripting (no Simulink model), use standard MATLAB functions
- Continuous-time Simulink modeling (no discrete/DSP blocks)

## Model Construction, Use the MCP Model Tools Only (mandatory)

The rest: https://github.com/matlab/simulink-agentic-toolkit/blob/HEAD/skills-catalog/signal-processing/simulink-streaming-dsp/SKILL.md