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.
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-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
- Commit
ddcaa2a
- Last updated
- First committed
Is this your skill?
If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.