What problem does it solve?
Writing tests for vllm-omni requires knowing its L1-L4 test levels, pytest marker taxonomy (core_model, advanced_model, full_model, omni/tts/diffusion), fixture conventions, and Buildkite pipeline files. This Skill encodes all of those rules so generated tests land in the right directory, carry the right markers, and wire into the correct CI pipeline on the first try.
Core Features & Use Cases
- Test level and marker selection: Classifies work as bugfix regression, feature coverage, or perf/benchmark, then maps it to L1-L4 levels with the correct level, model-type, hardware, and cross-cutting markers.
- Scenario-based test skeletons: Provides ready patterns for offline inference e2e, online serving e2e, documentation example tests, perf benchmarks, and invalid-parameter validation, including fixture scope rules and mocking conventions (mocker/monkeypatch only, no unittest.mock).
- CI wiring guidance: Shows how to register tests in Buildkite files (test-ready.yml for L1/L2, test-merge.yml for L3, test-nightly.yml for L4, test-weekly.yml for invalid-param), including source_file_dependencies and agents/plugins blocks.
- Use Case: A contributor fixing a bug in Qwen3-Omni serving asks for a regression test; the Skill classifies it, generates an L2 online-serving test with core_model + omni markers, and outputs copy-paste local and CI-like pytest commands.
Quick Start
Ask the assistant to generate a regression test for a specific vllm-omni issue or PR, stating the model, scenario, and available hardware.