Loading skill
Install any skill in seconds. Free to start, no credit card required.
Get Started Free →mkurman/mlflow
MLflow — open-source MLOps platform. Experiment tracking, model registry, packaging, deployment, and evaluation. Multi-cloud ML workflows with reproducible runs and artifact logging.
Passed 21 of 22 test cases with the skill, and 20 of 22 without it.
1 fixed20 passed either way1 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
A test case the skill fixed. Same model, same prompt, with and without the skill.
Provide the CLI command to launch a local HTTP endpoint for 'models:/MyModel/1' listening specifically on port 5001.
| Checked | Without skill | With skill |
|---|---|---|
| Includes --port 5001 in the mlflow models serve command | Missed | Passed |
The instructions your model reads when the skill loads.
MLflow is the leading open-source MLOps platform covering experiment tracking, model registry, packaging (MLflow Models format), and deployment (MLflow Serving). Supports PyTorch, TensorFlow, scikit-learn, ONNX, XGBoost, and custom models across cloud and on-prem.
uv pip install mlflow
import mlflow
mlflow.set_experiment("my_project")
with mlflow.start_run(run_name="experiment_1"):
mlflow.log_param("learning_rate", 0.01)
mlflow.log_param("batch_size", 32)
mlflow.log_metric("accuracy", 0.92)
mlflow.log_metric("loss", 0.35)
mlflow.log_artifact("model.pth")
mlflow.pytorch.log_model(model, "model")
mlflow.register_model("runs:/<run_id>/model", "MyModel")
mlflow models serve --model-uri models:/MyModel/1 --port 5001
mlflow ui --host 0.0.0.0 --port 5000
test cases passed, vs 20 / 22 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.