Create and pair pytest test files for machine learning experiments.
npx skills add https://github.com/vathymut/copilot-skills --skill test-ml-pipeline
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
This Skill automates the creation of test files and manages the pairing between machine learning experiments and their corresponding tests.
Use the test-ml-pipeline skill to create a smoke test for a new experiment named 'NN_new_experiment'.
High-intent search queries and answers about installing and using this skill.
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.
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.
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.
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.
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.
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