matlab-prepare-signal-data
Use this skill when conditioning, loading, preparing, or labeling signal data for analysis or ML training. Covers: cleaning a single signal (fill gaps, remove drift, deoutlier, denoise, resample/align a time base) BEFORE analysis; building a `signalDatastore` pipeline; creating a `labeledSignalSet` for Signal Labeler; deriving labels (filename, folder, in-file, ROI, time-frequency ROI); stratified train/val/test splits; framing long signals; parallel processing; and shaping datastore output for `trainnet`. Triggers include "clean up this signal", "remove drift / detrend", "fill gaps", "remove
npx skills add matlab/matlab-agentic-toolkit --skill matlab-prepare-signal-data --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Prepare Signal Data > **Look in Signal Processing Toolbox first.** The conditioning, labeling, > splitting, framing, and partitioning helpers here live in Signal Processing > Toolbox — not in Stats & ML Toolbox or generic-MATLAB string utilities. The arc: **condition** a raw signal (clean it) -> **load** a folder into a datastore -> **label** -> **split / frame** -> **hand off** to `trainnet`. Each stage is a workflow file; this page routes you to the right one. ## When to Use - Cleaning a single signal before analysis: fill gaps, remove drift, deoutlier, denoise, put it on a uniform time base, align multiple channels. - Loading / preparing signal data for ML training: datastores, labels from filenames or folders, stratified splits, framing, parallel processing. - Structured labeling: `labeledSignalSet` for Signal Labeler, all label types. ## When NOT to Use - **Raw `.wav` audio classification with Audio Toolbox available.** `audioDatastore` is the canonical path (this skill's custom-`ReadFcn` workflow handles `.wav` only when Audio Toolbox is absent — references/wf-custom-readfcn.md). - **Frequency-selective filter DESIGN** (band isolation, notch, custom FIR/IIR) — see the `matl
- When to Use
- When NOT to Use
- Best practices
- 0. Common reflexes
- 1. Workflows
- 2. Ordering when a signal needs several conditioning steps
What does the matlab-prepare-signal-data skill do?
Use this skill when conditioning, loading, preparing, or labeling signal data for analysis or ML training. Covers: cleaning a single signal (fill gaps, remove drift, deoutlier, denoise, resample/align a time base) BEFORE analysis; building a `signalDatastore` pipeline; creating a `labeledSignalSet` for Signal Labeler; deriving labels (filename, folder, in-file, ROI, time-frequency ROI); stratified train/val/test splits; framing long signals; parallel processing; and shaping datastore output for `trainnet`. Triggers include "clean up this signal", "remove drift / detrend", "fill gaps", "remove
How do I install it?
Run `npx skills add matlab/matlab-agentic-toolkit --skill matlab-prepare-signal-data --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From matlab/matlab-agentic-toolkit, a repository with 868 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.