Individual skill
detecting-data-anomalies
Process identify anomalies and outliers in datasets using machine learning algorithms. Use when analyzing data for unusual patterns, outliers, or unexpected deviations from normal behavior. Trigger with phrases like "detect anomalies", "find outliers", or "identify unusual patterns".
skills/.curated/detecting-data-anomalies/SKILL.md
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- ai Repository topic
- automation Repository topic
- claude-code Repository topic
- devops Repository topic
- mcp Repository topic
- ai-agents Repository topic
- anthropic Repository topic
- claude-code-plugins Repository topic
- developer-tools Repository topic
- llm Repository topic
- ai Declared skill tag
- ml Declared skill tag
- detecting-data Declared skill tag
- engineering Derived category
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SKILL.mdcore · 5,578 bytesassets/README.mddoc · 404 bytesreferences/README.mddoc · 67 bytesreferences/errors.mddoc · 1,160 bytesreferences/examples.mddoc · 12,709 bytesreferences/implementation.mddoc · 1,936 bytesscripts/README.mddoc · 639 bytes
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# Detecting Data Anomalies
## Overview
Identify anomalies and outliers in datasets using statistical and machine learning algorithms including Isolation Forest, One-Class SVM, Local Outlier Factor, and autoencoders. This skill handles the full detection pipeline from data ingestion and feature scaling through algorithm selection, threshold tuning, and result interpretation with anomaly scoring.
## Prerequisites
- Python 3.9+ with scikit-learn >= 1.3 (`pip install scikit-learn`)
- pandas and NumPy for data manipulation (`pip install pandas numpy`)
- matplotlib or seaborn for anomaly visualizations (`pip install matplotlib seaborn`)
- Dataset in CSV, JSON, Parquet, or database-queryable format
- Minimum 500 data points for statistical significance (1000+ recommended)
- Optional: PyTorch or TensorFlow for autoencoder-based detection on complex patterns
