| name | anomaly-detector |
| description | Detect outliers, spikes, rare events, and abnormal records in tabular or time-series data.
Use when the task is anomaly detection or suspicious-pattern review, not generic data-quality linting or full ML pipeline ownership.
|
| allowed-tools | Read, Write, Edit, Bash, Grep |
| version | 1.0.0 |
| license | MIT |
| author | Jeremy Longshore <jeremy@intentsolutions.io> |
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-checker for dataset sanity checks before anomaly reviewcreating-data-visualizations for general charts after anomalies are identified
