---

name: deployments-and-agents description: "Build, adapt, validate, and troubleshoot ZenML pipeline deployments, model or agent services, DeploymentSettings, DockerSettings packaging, deployer selection, service discovery, and credential-free ML or LLM workflow smoke patterns." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

ZenML Deployments and Agents

Use this sub-skill when a task mentions ZenML pipeline deployments, deployed HTTP services, real-time inference, serving ML models, deployed agents, LLM or agent examples, DeploymentSettings, deployment DockerSettings, deployers, model deployers, services, dashboard/static UI files, deployment invocation, or credential-free adaptation of production examples.

Route First

1. Read Agent and deployment workflows to choose between pipeline deployments, model-deployer/service workflows, agent examples, deterministic fallbacks, and production-readiness checks.
2. Read Deployment settings before editing DeploymentSettings, DockerSettings, CORS, secure headers, endpoint paths, dashboard files, app extensions, or deployer-specific settings.
3. Read Troubleshooting before running examples, changing stack/deployer configuration, debugging local-vs-remote behavior, or interpreting missing API keys, Docker, registries, services, and cloud credentials.
4. Run validate_deployment_settings.py with --example or --json <settings.json> for safe schema-level validation without deploying anything.

Boundary Rules

• Own example-backed production model, agent, and service workflows; pipeline deployments; DeploymentSettings; deployer/model-deployer/service usage; HTTP invocation shape; embedded UI patterns; credential-free smoke adaptation; and deployment-specific troubleshooting.
• Route low-level stack component implementation, integration flavor imports, service connectors, image builders, container registries, Docker daemon/backend issues, and cloud component registration to stacks-and-integrations.
• Route basic @step, @pipeline, artifacts, materializers, schedules, hooks as authoring primitives, and local pipeline semantics to pipeline-authoring.
• Route FastAPI server/store internals, REST routers, SQLModel schemas, migrations, RBAC, triggers, and server dependency wiring to server-and-stores.
• Route CLI/client resource audits, zenml deployment list/describe/invoke command behavior, login/connect, filters, secrets, and project/stack listing to cli-and-client.
• Route repository test selection, formatting, linting, docs maintenance, and CI-equivalent checks to maintenance.

Working Pattern

• Classify the serving target first: pipeline deployment for a long-running HTTP service, batch/snapshot execution for one-off work, or legacy/specialized model deployer only when an integration explicitly requires model-server semantics.
• Make deployable pipelines accept explicit JSON-serializable parameters with defaults, return JSON-serializable outputs or stable artifacts, and keep heavyweight models/resources loaded through startup hooks or deployment-service state.
• Keep agent workflows credential-aware: use deterministic fallbacks, stubs, or skipped branches when API keys, network, LLM providers, Docker, services, or cloud credentials are unavailable.
• Treat DeploymentSettings as ASGI application configuration and DockerSettings as image/runtime packaging; validate both before changing stack/deployer infrastructure.
• Prefer local, credential-free checks first: static code review, py_compile, settings validation, import checks, and small synthetic payloads. Deploy, invoke, start services, build images, or call LLMs only after the user explicitly authorizes the environment and credentials.

Safe Validation

• python scripts/validate_deployment_settings.py --help shows supported validation modes.
• python scripts/validate_deployment_settings.py --example prints and validates a tiny, credential-free deployment plus Docker settings payload.
• python scripts/validate_deployment_settings.py --json deployment_settings.json validates a user-supplied JSON payload containing deployment and/or docker settings without loading source hooks, running pipelines, starting servers, using Docker, or reading credentials.