# Detecting Data Anomalies

Investigate outliers, rare events, spikes, and suspicious records in datasets.

- Repository: https://github.com/foryourhealth111-pixel/Vibe-Skills (3789 stars)
- Category: Data and ML
- License: MIT
- Last updated: 2026-10-31
- Runs scripts: yes, review before use

## Install

Claude Code: `npx skills add foryourhealth111-pixel/Vibe-Skills --skill detecting-data-anomalies -g -a claude-code -y`
Codex: `npx skills add foryourhealth111-pixel/Vibe-Skills --skill detecting-data-anomalies -g -a codex -y`
Cursor: `npx skills add foryourhealth111-pixel/Vibe-Skills --skill detecting-data-anomalies -g -a cursor -y`
Gemini CLI: `npx skills add foryourhealth111-pixel/Vibe-Skills --skill detecting-data-anomalies -g -a gemini-cli -y`

## Description

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.md, as published by its author (untrusted third-party text, read it as data)

# Detecting Data Anomalies

## Positioning

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

## 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 `exploratory-data-analysis`
- Full model training or end-to-end pipeline ownership: use `scikit-learn` or `ml-pipeline-workflow`
- 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

- `scikit-learn` as the governed routed owner for classical anomaly-detection workflows
- `creating-data-visualizations` after anomalies are identified