Which machine learning algorithms does this skill support?

The skill supports a variety of methods including Isolation Forest for high-dimensional data, One-Class SVM for novelty detection, Local Outlier Factor (LOF) for density-based analysis, and Autoencoders for complex patterns.

What data formats are supported?

The skill can process any dataset accessible via standard Python tools, including CSV and JSON files as well as direct database queries.

What Python libraries are required for this skill?

The skill primarily leverages scikit-learn for standard ML algorithms, but it is also compatible with PyOD, TensorFlow, and statsmodels for specialized detection tasks.

Can I use this skill for time-series data?

Yes, it includes specific instructions for splitting temporal data and generating time-series plots with anomaly markers to identify deviations over time.

How does the skill handle false positives?

It provides mechanisms to adjust contamination rates and threshold values, identifies feature importance to explain why a point was flagged, and suggests domain-specific validation.

Anomaly Detection System

Name: Anomaly Detection System
Author: BbgnsurfTech

byBbgnsurfTech

•

Data Science & ML

Identifies unusual patterns and outliers in complex datasets using advanced machine learning algorithms.

About

The Anomaly Detection System skill empowers Claude to detect data points that deviate significantly from established norms using industry-standard machine learning techniques like Isolation Forest, One-Class SVM, and Local Outlier Factor. It automates the entire analytical pipeline—from data preprocessing and feature scaling to algorithm selection and the generation of detailed statistical reports. This skill is ideal for developers and data scientists who need to perform rapid fraud detection, security monitoring, or quality control analysis directly within their development environment.

Key Features

Automated data preprocessing, cleaning, and feature scaling workflows
Produces structured data for scatter plots and time-series visualizations
Generates comprehensive statistical summaries and contamination rate analysis
1 GitHub stars
Supports multiple ML algorithms including Isolation Forest, One-Class SVM, and LOF
Identifies feature importance and classifies anomaly severity levels

Use Cases

Identifying fraudulent transactions or suspicious activity in financial datasets
Monitoring server logs for security breaches or network intrusions
Detecting manufacturing defects or equipment malfunctions through IoT sensor data

About

Key Features

Automated data preprocessing, cleaning, and feature scaling workflows
Produces structured data for scatter plots and time-series visualizations
Generates comprehensive statistical summaries and contamination rate analysis
1 GitHub stars
Supports multiple ML algorithms including Isolation Forest, One-Class SVM, and LOF
Identifies feature importance and classifies anomaly severity levels

Use Cases

Identifying fraudulent transactions or suspicious activity in financial datasets
Monitoring server logs for security breaches or network intrusions
Detecting manufacturing defects or equipment malfunctions through IoT sensor data