Agent skill · data analytics · aj-geddes
Anomaly Detection
Identify unusual patterns, outliers, and anomalies in data using statistical methods, isolation forests, and autoencoders for fraud detection and quality monitoring
Why this skill is useful
Provides specific implementation examples and statistical methods for anomaly detection that the AI wouldn't reliably generate on its own.
What it needs
Requires matplotlib, numpy, pandas, scikit-learn, scipy, seaborn installed locally. About 4k tokens when loaded. Last updated 2026-03-04. 307 stars on the source repository.
What this skill does
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 Method Selection Guide Z-score: Simple, fast, assumes normal distribution IQR: Robust, non-parametric, good for outliers Isolation Forest: Efficient, good for high dimensions LOF: Density-based, finds local anomalies Autoencoders: Complex patterns, deep learning Threshold Selection Conservative: Fewer false positives, more false negatives Aggressive: More anomalies flagged, more false positives Data-driven: Use validation set to optimize threshold Deliverables Anomaly detection results Anomaly scores visualization Comparison of methods Identified anomalous records Recommendation for production deployment Threshold optimization analysis
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills aj-geddes/anomaly-detection