Extract features from signals collected on rotating machinery components, including motors, pumps, fans, gears, bearings, and shafts. Signals can include vibration, electrical, or environmental sensor measurements.
SkillScore86Maintained
Automatic
◆Data Analyst pack
Last verified 8/16/2026
What it changes
Passed 19 of 22 test cases with the skill, and 10 of 22 without it.
Without skill45%
With skill86%
Checks it passed that the model alone missed
Prompts the user for missing physical parameters.
Refrains from generating a feature extraction table workflow.
Uses fileEnsembleDatastore to manage the batch file collection.
and 9 more in the full report
9 fixed10 passed either way3 still failing0 broke
Pass-rate lift
+41 pp
Tokens
+51%
Agent turns
0%
Response time
-45%
Model gemini-3.6-flashMeasured 8/16/2026v119 of 22 cases passed with skill
Tokens, turns and time describe resource use, not a dollar price.
See it in action
One of 9 test cases the skill fixed. Same model, same prompt, with and without the skill.
1 of 9
Prompt
I defined custom fault frequency bands for a shaft using `faultBands`. Now I want to compute peak amplitude, peak frequency, and total band power for each defined band from a power spectrum. Which MATLAB function automates this calculation?
What was checked, without and with the skill
Checked
Without skill
With skill
Uses faultBandMetrics to compute metrics across defined fault bands.
Passed
Passed
Evaluates fault band metrics on a power spectrum computed via pspectrum.
Missed
Passed
case-19 · gemini-3.6-flash · 8/16/2026 · answers are capped and redacted
What's inside
The instructions your model reads when the skill loads.
SKILL.md123 lines6 files
Rotating Machinery Feature Extraction
Extract predictive features from rotating machinery data for condition monitoring and fault detection applications. This skill covers the essential feature extraction workflow steps and algorithms specialized to rotating machinery.
When to Use
User has uniformly-sampled time series data representing rotating machinery sensor measurements stored as matrix, timetable, or cell array variables.
User is building a condition monitoring or fault detection system for rotating machinery.
User wants to process rotating machinery data for feature extraction.
User wants to select most predictive features and construct a health indicator from features.
User is deploying condition monitoring and fault detection systems.
User has high-frequency vibration data and wants feature-based data reduction for efficient condition monitoring.
When NOT to Use
User has non-time-series data such as images, videos, or tabular (unordered) data.
User wants fault classification (as opposed to fault detection) with two or more fault classes. Use classificationLearner instead.
User has time series data (labeled or unlabeled) and wants to build anomaly detection models. Use timeSeriesAnomalyDetector instead.
User has very low-frequency data not suitable for statistical or spectral feature extraction.
Feature Extraction Workflow
Follow these 5 steps interactively:
Task Progress:
- [ ] Step 1: Process data for feature extraction
- [ ] Step 2: Extract features from data
- [ ] Step 3: Rank and select features
- [ ] Step 4: Develop a health indicator using selected features
- [ ] Step 5: Deploy the application
Do NOT silently choose parameters or make assumptions. Engage the user at each decision point. In particular, ask the user to provide important system parameters such as
Fit SimBiology model parameters to data — fitproblem, population NLME, virtual patients, and NCA. Use when asked to fit, estimate, calibrate, or compute PK metrics.