sagemaker-mlflow
Connect to SageMaker Managed MLflow (mlflow-app or mlflow-tracking-server ARN) as an MLflow backend, then hand off to the other MLflow skills. Triggers on a SageMaker MLflow ARN (arn:aws:sagemaker:...:mlflow-app/... or arn:aws:sagemaker:...:mlflow-tracking-server/...) or "SageMaker Managed MLflow".
Install
With your agent
Install OMGSkills skill "mlflow/skills:sagemaker-mlflow" into this agent's normal skills directory: call the OMGSkills MCP get_install_plan tool for your agent and run the script it returns (it installs only the pinned commit and verifies SKILL.md), and stop if MCP or the pin is unavailable. Do not overwrite or change existing skills without asking me.
Manual command
git clone https://github.com/mlflow/skills /tmp/skills && ln -s /tmp/skills/sagemaker-mlflow ~/.claude/skills/sagemaker-mlflowFrom README
Connect to SageMaker MLflow Connect using the sagemaker-mlflow plugin, an ARN tracking URI and AWS credentials. Other MLflow skills inherit the connection through MLFLOWTRACKINGURI. Requires Python >= 3.10 (mlflow >= 3.8). Validate and report the fix — never auto- the plugin or acquire credentials (mirrors the repo's Databricks pattern). Step 1: Preconditions python --version -> must be 3.10+. If older, STOP and tell the user. Confirm AWS credentials resolve (env vars, aws configure / SSO, or an IAM role). If they error (ExpiredToken / no creds), STOP and ask the user to configure them. (scripts/verifyconnection.py also checks this, via boto3.) Step 2: Check the plugin If missing, stop and give the user this command: Required even if MLFLOWTRACKINGURI is already an ARN — without it, mlflow.settrackinguri() fails with UnsupportedModelRegistryStoreURIException.
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