setup-env

Automate environment setup and validation for quantum ML projects.

Updated Jun 17, 2025

Search Tags:#devops #dependencies #setup #environment #cuda #colab #quantum-ml

Ready to run

npx skills add https://github.com/necatiincekara/Quanvolutional-Neural-Network --skill setup-env-necatiincekara

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: setup-env
Source: https://github.com/necatiincekara/Quanvolutional-Neural-Network/tree/main/.agents/skills/setup-env
Command: npx skills add https://github.com/necatiincekara/Quanvolutional-Neural-Network --skill setup-env-necatiincekara

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Automates the setup and validation of development environments for local and Colab-based quantum ML projects, reducing configuration errors and onboarding time.

Core Features & Use Cases

Environment detection: identify Python version, platform, and available hardware (CPU/CUDA/TPU) and verify compatibility.
Dependency validation: check and install required packages listed in the project configuration, ensuring correct versions.
Sanity checks: perform a lightweight forward pass to confirm basic model execution and verify dataset paths.
Troubleshooting: generate actionable blocker reports with minimum fixes to get experiments running quickly.

Quick Start

Run a quick environment check to verify Python version, dependencies, and CUDA availability.

Frequently Asked Questions about setup-env

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I set up a quantum ML environment and verify CUDA availability?▼

To set up a quantum ML environment, the process automates platform detection, verifies Python versions, installs required dependencies, and checks CUDA availability to ensure your local or Colab hardware is ready for experiments.

What is the best way to check if my Colab dependencies are configured correctly for quantum machine learning?▼

The best way to check Colab dependencies is running an automated validation that compares installed packages against project configurations, ensuring correct versions and cross-platform compatibility for your quantum ML workflow.

Does this environment setup process support both local CPU and CUDA quantum backends?▼

Yes, the environment setup process supports both local CPU and CUDA backends by detecting available hardware and verifying backend availability, alongside checking compatibility for quantum-specific execution environments.

How do I troubleshoot quantum ML dependency installation errors and environment blockers?▼

To troubleshoot quantum ML environment blockers, the setup process generates actionable blocker reports detailing minimum fixes required, helping you resolve dependency installation errors and configuration issues quickly.

Can I perform a sanity check or forward pass to validate my quantum ML dev environment?▼

Yes, you can validate your quantum ML dev environment by running a lightweight forward pass as a sanity check, which confirms basic model execution and verifies dataset paths before starting full experiments.