# Detecting Data Anomalies

> Investigate outliers, rare events, spikes, and suspicious records in datasets. Use as an explicit anomaly-analysis helper when you want concrete anomaly-detection workflow guidance, not generic data validation or end-to-end ML ownership.

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

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# Detecting Data Anomalies

## Positioning

Treat this skill as an explicit/manual helper.
In governed ML routing, anomaly-detection ownership normally belongs to `anomaly-detector`.

## 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 `data-quality-checker`
- Full model training or end-to-end pipeline ownership: use `training-machine-learning-models`
- 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

- `anomaly-detector` as the governed routed owner
- `creating-data-visualizations` after anomalies are identified