# Anomaly Detector

> 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.

- Skill: `majiayu000/anomaly-detector` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds add majiayu000/anomaly-detector`
- Raw SKILL.md: https://api.skillmd.com/api/skills/majiayu000/anomaly-detector/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: DevOps & Infra
- License: MIT
- Author: majiayu000 (https://skillmd.com/u/majiayu000)
- Updated: 2026-09-09
- Page: https://skillmd.com/skills/majiayu000/anomaly-detector

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# 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