/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 <jeremy@intentsolutions.io>
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
• Comparing isolation forest, one-class SVM, LOF, or threshold-based anomaly workflows
• Turning suspicious records into a shortlist for human inspection

Not For / Boundaries

• Null/duplicate/schema/range validation: use `exploratory-data-analysis`
• Full model training or end-to-end pipeline ownership: use `scikit-learn` or `ml-pipeline-workflow`
• Publication-grade figure production: use `scientific-visualization`

Typical Outputs

• Candidate anomaly-detection methods and thresholds
• A review checklist for false positives and false negatives
• Suggested tables or plots for the suspicious subset

Related Skills

• `scikit-learn` as the governed routed owner for classical anomaly-detection workflows
• `creating-data-visualizations` after anomalies are identified