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smoke-test-ml-pipeline

Run minimal pytest smoke tests on machine learning pipelines.

Updated Apr 19, 2026
Search Tags:#pytest#ml pipeline#machine learning#smoke test#pipeline testing
One-click install
npx skills add https://github.com/vathymut/copilot-skills --skill smoke-test-ml-pipeline
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: smoke-test-ml-pipeline
Source: https://github.com/vathymut/copilot-skills/tree/main/.github/skills/smoke-test-ml-pipeline
Command: npx skills add https://github.com/vathymut/copilot-skills --skill smoke-test-ml-pipeline

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

This Skill automates the smoke testing of Machine Learning pipelines, ensuring the integrity of the predictive models before deployment.

Core Features & Use Cases

  • Smoke Testing: Automatically verifies the structure and performance of machine learning models by running a minimal pytest.
  • Diagnostic by Construction: Identifies issues with data handling and model predictions early in the development cycle.
  • Use Case: Prior to deploying a new machine learning model, this Skill can be used to verify that the model behaves as expected and meets the necessary performance criteria.

Quick Start

Execute the smoke test by running pytest tests/smoke/test_NN_<short_name>.py to verify the structure and performance of the model.

Frequently Asked Questions about smoke-test-ml-pipeline

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

FAQPage Schema
How do I run a smoke test on my machine learning pipeline?▼

You can run a smoke test on your ML pipeline by executing a minimal pytest command to verify structural correctness and validate predictive performance during development.

Why does my ML pipeline fail during data handling before deployment?▼

Your ML pipeline may fail due to data handling issues, which a diagnostic smoke test identifies early to verify model behavior and structural integrity before deployment.

Can I use pytest to verify the predictive performance of machine learning models?▼

Yes, you can use pytest to verify predictive performance by running a minimal smoke test that ensures models behave as expected and meet necessary performance criteria.

What's the best way to automate ML pipeline smoke testing?▼

The best way to automate ML pipeline smoke testing is using a script-based approach that executes a minimal pytest to verify model structure and identify data handling issues.

When do I need to perform a smoke test on a machine learning pipeline?▼

You need to perform a smoke test on a machine learning pipeline prior to deploying a new model to verify it behaves as expected and meets necessary performance criteria.

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