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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