Anomaly Detection
Identify unusual patterns, outliers, and anomalies in data using statistical methods, isolation forests, and autoencoders for fraud detection and quality monitoring
npx skills add majiayu000/claude-skill-registry --skill anomaly-detection-aj-geddes-useful-ai-prompts --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Anomaly Detection ## Overview Anomaly detection identifies unusual patterns, outliers, and anomalies in data that deviate significantly from normal behavior, enabling fraud detection and system monitoring. ## When to Use - Detecting fraudulent transactions or suspicious activity in financial data - Identifying system failures, network intrusions, or security breaches - Monitoring manufacturing quality and identifying defective products - Finding unusual patterns in healthcare data or patient vital signs - Detecting abnormal sensor readings in IoT or industrial systems - Identifying outliers in customer behavior for targeted intervention ## Detection Methods - **Statistical**: Z-score, IQR, modified Z-score - **Distance-based**: K-nearest neighbors, Local Outlier Factor - **Isolation**: Isolation Forest - **Density-based**: DBSCAN - **Deep Learning**: Autoencoders, GANs ## Anomaly Types - **Point Anomalies**: Single unusual records - **Contextual**: Unusual in specific context - **Collective**: Unusual patterns in sequences - **Novel Classes**: Completely new patterns ## Implementation with Python ```python import pandas as pd import numpy as np import matplotlib.pyplot as plt imp
- Overview
- When to Use
- Detection Methods
- Anomaly Types
- Implementation with Python
- Method Selection Guide
- Threshold Selection
- Deliverables
What does the Anomaly Detection skill do?
Identify unusual patterns, outliers, and anomalies in data using statistical methods, isolation forests, and autoencoders for fraud detection and quality monitoring
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill anomaly-detection-aj-geddes-useful-ai-prompts --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.