detecting-data-anomalies

Files (11)

• SKILL.md
• README.md · assets/
• README.md · references/
• errors.md · references/
• examples.md · references/
• implementation.md · references/
• README.md · scripts/
• algorithm_selector.py · scripts/
• anomaly_visualizer.py · scripts/
• data_loader.py · scripts/
• report_generator.py · scripts/

SKILL.md (first lines)

# 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

From foryourhealth111-pixel/Vibe-Skills, MIT.

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