anomaly-detector

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 <[e-mail]>

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 review
• creating-data-visualizations for general charts after anomalies are identified