Does it support CI/CD integration?

Yes, it can guide you through the setup of optional CI/CD files like GitHub Actions for automated smoke testing.

How does this skill help with TimeGPT costs?

It provides instructions on how to enable or disable specific experiments in your configuration file to manage the number of API calls made during testing.

What are the prerequisites for using this skill?

You need Python 3.9+, a valid Nixtla API key, and the repository files including setup docs and validation scripts.

Does this skill execute Python scripts automatically?

No, this is a read-only skill that provides guidance and instructions based on your local files without executing scripts or making external API calls.

Can it help fix 'ImportError: No module named nixtla'?

Yes, it identifies missing dependencies and provides the specific pip install commands needed to resolve the issue.

TimeGPT Lab Bootstrap

Name: TimeGPT Lab Bootstrap
Author: intent-solutions-io

byintent-solutions-io

Ciencia de Datos y ML

Guides the setup, validation, and experiment orchestration of Nixtla TimeGPT forecasting environments.

Acerca de

The TimeGPT Lab Bootstrap skill is an essential onboarding and troubleshooting assistant for developers working with Nixtla's forecasting ecosystem. It streamlines the creation of local lab environments by inspecting setup documentation and validation scripts to provide tailored, step-by-step instructions. From configuring API keys and Python dependencies to running smoke tests and systematic experiment workflows, this skill ensures your environment is correctly tuned for time series analysis without directly executing code, maintaining a safe and guided development experience.

Características Principales

Automated inspection of validation and smoke test scripts
Detailed experiment harness and workflow walkthroughs
Guided configuration for Nixtla TimeGPT API access
0 GitHub stars
Step-by-step environment setup and dependency guidance
Actionable troubleshooting for common setup pitfalls

Casos de Uso

Troubleshooting Python environment, package, or API authentication errors
Onboarding new developers to a TimeGPT-based forecasting project
Configuring and interpreting systematic time series experiments

Acerca de

Características Principales

Automated inspection of validation and smoke test scripts
Detailed experiment harness and workflow walkthroughs
Guided configuration for Nixtla TimeGPT API access
0 GitHub stars
Step-by-step environment setup and dependency guidance
Actionable troubleshooting for common setup pitfalls

Casos de Uso

Troubleshooting Python environment, package, or API authentication errors
Onboarding new developers to a TimeGPT-based forecasting project
Configuring and interpreting systematic time series experiments