How do I trigger this skill in Claude?

You can trigger it using natural language phrases like 'detect anomalies', 'find outliers', or 'identify unusual patterns' followed by a reference to your dataset.

What data formats are compatible?

The skill can process datasets in common formats such as CSV, JSON, and standard database query outputs.

Do I need a specific environment to use this skill?

You need a Python environment with scikit-learn or similar machine learning libraries installed for the Bash tool to execute the detection logic.

Can it handle time-series data?

Yes, the skill includes specific instructions for splitting and analyzing temporal data to identify time-based deviations.

Which algorithms does this skill support?

It supports popular machine learning algorithms including Isolation Forest, One-Class SVM, Local Outlier Factor (LOF), and Autoencoder-based approaches via scikit-learn.

Data Anomaly Detection

Name: Data Anomaly Detection
Author: Brmbobo

byBrmbobo

•

Data Science & ML

Identifies outliers and unusual patterns in datasets using machine learning algorithms to ensure data integrity and security.

This skill provides automated assistance for detecting statistical anomalies and outliers within complex datasets using machine learning. It streamlines the entire diagnostic workflow—from loading and cleaning data to feature scaling and algorithm application—leveraging models like Isolation Forest, One-Class SVM, and Local Outlier Factor. Ideal for developers and data scientists, it helps pinpoint fraudulent transactions, system errors, or sensor malfunctions by analyzing deviations from normal behavior directly within the Claude Code environment.

Key Features

01Detailed reporting on contamination rates and confidence scores

02Automated data preprocessing and feature scaling

03Temporal data splitting for time-series anomaly analysis

041 GitHub stars

05Seamless integration with Python-based scikit-learn workflows

06Support for multiple ML algorithms including Isolation Forest and LOF

Use Cases

01Detecting hardware malfunctions or sensor errors in IoT datasets

02Cleaning machine learning training sets by removing noisy outliers

03Identifying fraudulent activity or suspicious patterns in financial records

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add brmbobo/web2podcast detecting-data-anomalies

For use in Claude.ai and ChatGPT

Download Skill