Does it support external factors like holidays?

Absolutely. You can specify external regressors and holiday effects during the requirements gathering phase to include that logic in the generated pipeline.

What files does the skill generate?

The skill produces a complete Python module, a requirements.txt file for dependencies, a .env.example for API credentials, and a README with execution instructions.

Is the generated code suitable for production environments?

Yes, the pipelines follow best practices including structured logging, try-except error handling blocks, and configuration management to ensure stability in production.

Can it handle multiple time series at once?

Yes, the skill generates code specifically designed to handle multi-series forecasting by utilizing 'unique_id' columns to differentiate between different series.

What do I need to use the TimeGPT Pipeline Builder?

To run the generated code, you will need a time-series dataset (CSV, database, or API) and a valid Nixtla TimeGPT API key.

TimeGPT Pipeline Builder

Name: TimeGPT Pipeline Builder
Author: intent-solutions-io

byintent-solutions-io

Data Science & ML

Generates production-ready Python forecasting pipelines using Nixtla's TimeGPT API to automate complex time-series analysis.

About

The TimeGPT Pipeline Builder is a specialized skill for Claude Code designed to accelerate the development of robust time-series forecasting workflows. It scaffolds complete, production-grade Python modules that include data validation, multi-series support, external regressors, and cross-validation, allowing developers to move from raw data to actionable forecasts quickly. By leveraging Nixtla's foundation models, it ensures your forecasting infrastructure follows industry best practices, including comprehensive error handling, logging, and automated visualization of results.

Key Features

0 GitHub stars
Comprehensive project scaffolding including requirements.txt and environment configuration
Production-ready Python code generation for TimeGPT API integration
Support for advanced forecasting features like external regressors and holidays
Automated data validation and schema checking for time-series datasets
Built-in visualization tools for forecast results and confidence intervals

Use Cases

Creating multi-series demand planning systems for large-scale inventory management
Developing backtesting frameworks using built-in time-series cross-validation
Building automated daily sales forecasting pipelines with external market data

About

Key Features

0 GitHub stars
Comprehensive project scaffolding including requirements.txt and environment configuration
Production-ready Python code generation for TimeGPT API integration
Support for advanced forecasting features like external regressors and holidays
Automated data validation and schema checking for time-series datasets
Built-in visualization tools for forecast results and confidence intervals

Use Cases

Creating multi-series demand planning systems for large-scale inventory management
Developing backtesting frameworks using built-in time-series cross-validation
Building automated daily sales forecasting pipelines with external market data