detecting-data-anomalies

name: detecting-data-anomalies description: | Investigate outliers, rare events, spikes, and suspicious records in datasets. Use as an explicit anomaly-analysis helper when you want concrete anomaly-detection workflow guidance, not generic data validation or end-to-end ML ownership. allowed-tools: Read, Bash(python:*), Grep, Glob version: 1.0.0 author: Jeremy Longshore [email protected] license: MIT

Detecting Data Anomalies

Positioning

Treat this skill as an explicit/manual helper. In governed ML routing, anomaly-detection ownership normally belongs to scikit-learn.

When to Use

Use this skill when:

• Reviewing outlier transactions, fraud candidates, sensor spikes, or rare failures