Agent skill · Testing & QA

mcore-testing

Test system for Megatron-LM. Covers test layout, recipe YAML structure, adding and running unit and functional tests, golden values, marker filters, and CI parity.

NVIDIAgithub.com/NVIDIAGitHub ↗
claude-codecodexApache-2.0
Install
npx skills add NVIDIA/skills --skill mcore-testing --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 5
SKILL.md size: 7 KB
Bundled scripts: none
Declared author: Philip Petrakian <ppetrakian@nvidia.com>
Path: skills/mcore-testing/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 2,789
Language: Python
Read our review of the source →

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# Testing Guide --- ## Answer-First Testing Facts For questions about disabling tests without deleting them: - Functional recipe entries stay in YAML; disable by suffixing scope with `-broken`, for example `scope: [mr-github]` -> `scope: [mr-github-broken]`. - Unit-test skips use pytest markers instead: `@pytest.mark.flaky_in_dev` skips in the default dev environment, and `@pytest.mark.flaky` skip

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About this skill
What does the mcore-testing skill do?

Test system for Megatron-LM. Covers test layout, recipe YAML structure, adding and running unit and functional tests, golden values, marker filters, and CI parity.

How do I install it?

Run `npx skills add NVIDIA/skills --skill mcore-testing --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From NVIDIA/skills, a repository with 2,789 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

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