test-ml-pipeline

Create and pair pytest test files for machine learning experiments.

Updated Apr 19, 2026

Search Tags:#test automation #pytest #machine learning #workflow orchestration #file pairing

Ready to run

npx skills add https://github.com/vathymut/copilot-skills --skill test-ml-pipeline

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: test-ml-pipeline
Source: https://github.com/vathymut/copilot-skills/tree/main/.github/skills/test-ml-pipeline
Command: npx skills add https://github.com/vathymut/copilot-skills --skill test-ml-pipeline

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pytest, and includes scripts (resource) components.

What problem does it solve?

This Skill automates the creation of test files and manages the pairing between machine learning experiments and their corresponding tests.

Core Features & Use Cases

Test File Creation: Generates empty test files based on the experiment name.
Pairing Rule Enforcement: Ensures a strict 1:1 relationship between experiments and tests.
Subskill Dispatch: Routes to the appropriate subskill for writing the test's implementation.

Quick Start

Use the test-ml-pipeline skill to create a smoke test for a new experiment named 'NN_new_experiment'.

Frequently Asked Questions about test-ml-pipeline

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

FAQPage Schema

How do I automate test file creation for machine learning experiments?▼

Automating test file creation for machine learning experiments involves generating empty test files based on the experiment name. This process enforces a strict 1:1 pairing relationship between each experiment and its corresponding test.

Can I enforce a 1:1 pairing between ML experiments and pytest tests?▼

Yes, you can enforce a 1:1 pairing between ML experiments and pytest tests. The workflow ensures each experiment is associated with a test, requiring pytest and a structured file system for strict iteration control and test management.

What is the best way to manage test pairing in machine learning workflows?▼

The best way to manage test pairing in machine learning workflows is using an orchestration approach that routes to subskills for implementation. It maintains a structured file system to pair tests with experiments and dispatches execution through pytest.

Do I need pytest to manage test creation in ML pipelines?▼

Yes, you need pytest to manage test creation in ML pipelines. The test execution relies on pytest to run the generated test files that are paired with your machine learning experiments.

How does subskill dispatch work for writing test implementations?▼

Subskill dispatch for writing test implementations works by routing the workflow to the appropriate subskill after the initial empty test file is created. This ensures the specific test logic is written for the paired ML experiment.