detecting-data-anomalies
This skill helps identify outliers and suspicious data points using methods like isolation forest, one-class SVM, and local outlier factor. Use it when manually reviewing potentially fraudulent transactions, equipment sensor anomalies, or rare failure events, and you need candidate detection methods, false positive checklists, and visualizations of suspicious records before routing to a full machine learning pipeline.
git clone --depth 1 https://github.com/foryourhealth111-pixel/Vibe-Skills /tmp/detecting-data-anomalies && cp -r /tmp/detecting-data-anomalies/bundled/skills/detecting-data-anomalies ~/.claude/skills/detecting-data-anomaliesSKILL.md
# 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
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