majiayu000 Anomaly Detector
Purpose
Use this skill when the user needs to identify abnormal rows, drift, spikes, or rare-event behavior in data.
When to Use
Use this skill when:
- Investigating outlier transactions, sensor spikes, fraud candidates, or rare failures
- Comparing statistical, distance-based, or density-based anomaly detection approaches
- Setting anomaly thresholds and reviewing false positives or false negatives
Not For / Boundaries
- Generic schema/null/range validation: use
data-quality-checker - Publication-grade figure polishing: use
scientific-visualization - End-to-end supervised model training: use
training-machine-learning-models
Typical Outputs
- Candidate anomaly rules or model choices
- Thresholding and review workflow
- Follow-up plots or tables showing suspicious records
Related Skills
data-quality-checkerfor dataset sanity checks before anomaly reviewcreating-data-visualizationsfor general charts after anomalies are identified