detecting-data-anomalies
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.
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tessl review fix ./bundled/skills/detecting-data-anomalies/SKILL.mdDetecting 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-learnorml-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-learnas the governed routed owner for classical anomaly-detection workflowscreating-data-visualizationsafter anomalies are identified
- Repository
- foryourhealth111-pixel/Vibe-Skills
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