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name: serving-and-projects description: "Use when working with MLflow CLI commands, tracking servers, auth/security, database migrations, MLproject execution, local model serving/prediction, deployments, MCP or agent setup, and local development server workflows." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Serving And Projects

Use this sub-skill when the task is about operating MLflow from the command line or hosting/serving MLflow services. It covers command discovery, mlflow server, mlflow run, mlflow deployments, mlflow models serve/predict, auth app setup, database migrations, MCP/agent CLI setup, and development-server caveats.

Route First

• Use references/cli-reference.md to choose CLI groups, inspect help safely, and distinguish read-only probes from side-effectful commands.
• Use references/server-and-auth.md for tracking server storage, artifact serving, allowed-host/CORS security, auth app setup, and auth DB migration guidance.
• Use references/projects-and-deployments.md for MLproject execution, deployment target commands, local model serving/prediction, Docker/SageMaker/cloud cautions, MCP, agent setup, and repo development server workflows.
• Use references/troubleshooting.md when diagnosing backend/artifact store mismatches, auth DB problems, host/origin blocks, deployment credentials, env-manager failures, CLI input-file formats, or long-running process behavior.

Safety Defaults

• Prefer help/version probes before running operational commands: python scripts/cli_probe.py --commands mlflow "mlflow server" "mlflow models serve"; use --python-module /path/to/python when the mlflow console script is not on PATH.
• Do not start mlflow server, mlflow models serve, mlflow mcp run, mlflow gateway start, or the repo dev server unless the user explicitly asks for a long-running process and provides ports/log handling.
• Do not run deployment create/update/delete, SageMaker, Docker build/run, Kubernetes, Databricks, or cloud commands unless credentials, target, network, and cost/side effects are explicitly approved.
• Do not run auth DB migrations or tracking DB migrations against shared databases without a backup and the exact database URI from the user.
• For model flavor internals, serialization, signatures, and environment inference, route to models-and-flavors; for experiment/run semantics and registry objects, route to tracking-and-registry; for GenAI tracing/evaluation semantics, route to genai-observability.

Safe Probe Script

scripts/cli_probe.py runs only --help and --version style probes with timeouts. It is suitable for checking an installed MLflow CLI surface before choosing commands, and it intentionally refuses arbitrary command execution.