What file format does the anomaly detector accept?

The skill processes CSV files that include the standard Nixtla columns: 'unique_id' for the series name, 'ds' for the timestamp, and 'y' for the numeric value.

Does this skill require an external API key?

Yes, you must have a valid NIXTLA_TIMEGPT_API_KEY set in your environment variables to communicate with the Nixtla processing engine.

Do I need to train a machine learning model to use this skill?

No, this skill uses TimeGPT's zero-shot capabilities, allowing it to detect anomalies in your data immediately without any prior training phase.

Can I detect anomalies across multiple different products or metrics at once?

Yes, by using the 'unique_id' column in your CSV, you can analyze hundreds of different time series simultaneously in a single command.

What kind of output does the skill provide?

The skill generates a detailed CSV of detected anomalies, a summary text report, and a PNG visualization highlighting the anomalies in red.

Nixtla Anomaly Detector

Name: Nixtla Anomaly Detector
Author: intent-solutions-io

byintent-solutions-io

Data Science & ML

Detects outliers, level shifts, and trend breaks in time series data using TimeGPT's zero-shot capabilities.

About

The Nixtla Anomaly Detector skill empowers Claude to perform advanced statistical analysis on time series data without the need for manual model training or complex feature engineering. By leveraging the TimeGPT API, it automatically identifies unusual patterns such as sudden spikes, persistent shifts in data levels, and structural trend breaks across multi-series datasets. This skill is particularly useful for data scientists and developers who need to quickly audit data integrity, monitor system metrics, or identify business-critical events in financial, operational, or IoT data streams.

Key Features

Detection of multiple anomaly types: outliers, level shifts, and trend breaks
Support for multi-series CSV data using standard Nixtla schemas
Fast processing of large datasets via the TimeGPT cloud engine
Zero-shot anomaly detection requiring no model training
Automated generation of visual reports and plots
0 GitHub stars

Use Cases

Monitoring IoT sensor data for malfunctions or environmental anomalies
Detecting structural shifts in financial sales data or market trends
Identifying sudden spikes or drops in website traffic and server performance logs

About

Key Features

Detection of multiple anomaly types: outliers, level shifts, and trend breaks
Support for multi-series CSV data using standard Nixtla schemas
Fast processing of large datasets via the TimeGPT cloud engine
Zero-shot anomaly detection requiring no model training
Automated generation of visual reports and plots
0 GitHub stars

Use Cases

Monitoring IoT sensor data for malfunctions or environmental anomalies
Detecting structural shifts in financial sales data or market trends
Identifying sudden spikes or drops in website traffic and server performance logs