Connect to SageMaker MLflow

Establishes the connection to SageMaker Managed MLflow (the sagemaker-mlflow plugin + an ARN tracking URI + AWS credentials), then hands off to the requested MLflow skill (instrumenting-with-mlflow-tracing, agent-evaluation, retrieving-mlflow-traces, querying-mlflow-metrics, etc.), which inherit the connection via MLFLOW_TRACKING_URI.

Requires Python >= 3.10 (mlflow >= 3.8). Validate and report the fix — never auto-install 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/verify_connection.py also checks this, via boto3.)