How does it handle highly imbalanced datasets?

The skill uses specialized evaluation metrics like Precision-Recall (PR) AUC and Precision@K, which are more effective than standard accuracy for datasets where anomalies represent less than 1% of the data.

Which algorithms does the Anomaly Detector support?

It supports a wide range of methods including statistical baselines (Z-score, IQR), Isolation Forest, One-Class SVM, Local Outlier Factor (LOF), and deep learning Autoencoders.

Can this skill be used for real-time fraud detection?

Yes, it includes specialized Fraud Detection Pipelines that provide probability scores, feature importance, and cost-benefit analysis for real-time applications.

What is the benefit of SpecWeave integration for ML?

SpecWeave integration provides a structured folder hierarchy for every detection increment, tracking experiments, model files, and evaluation reports for full reproducibility.

Anomaly & Outlier Detector

Name: Anomaly & Outlier Detector
Author: anton-abyzov

byanton-abyzov

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データサイエンスとML

Detects unusual patterns and outliers in data using statistical methods and machine learning algorithms integrated with the SpecWeave workflow.

概要

The Anomaly Detector skill empowers Claude to identify rare events, fraud, and system irregularities across diverse datasets using industry-standard algorithms. By leveraging methods such as Isolation Forest, One-Class SVM, and deep-learning Autoencoders, it helps developers build robust monitoring, security, and quality control systems. This skill is uniquely optimized for the SpecWeave framework, ensuring that detection models are developed within a structured increment workflow complete with specifications, experiment tracking, and business-driven evaluation metrics.

主な機能

Multi-algorithm support including Isolation Forest, One-Class SVM, and LOF
Native SpecWeave integration for documenting ML increments and deployment rules
13 GitHub stars
Deep learning-based detection using Autoencoders for complex data patterns
Automated statistical baselining using Z-score and Interquartile Range (IQR)
Comprehensive evaluation suite featuring Precision@K and PR AUC metrics

ユースケース

Financial fraud detection for identifying suspicious transactions
Industrial quality control and preventive maintenance via sensor data
Real-time system health and network intrusion monitoring

概要

主な機能

Multi-algorithm support including Isolation Forest, One-Class SVM, and LOF
Native SpecWeave integration for documenting ML increments and deployment rules
13 GitHub stars
Deep learning-based detection using Autoencoders for complex data patterns
Automated statistical baselining using Z-score and Interquartile Range (IQR)
Comprehensive evaluation suite featuring Precision@K and PR AUC metrics

ユースケース

Financial fraud detection for identifying suspicious transactions
Industrial quality control and preventive maintenance via sensor data
Real-time system health and network intrusion monitoring