datarobot-workload-api

# DataRobot Workload API Run container images as managed, autoscalable services on DataRobot. One skill, four jobs — pick the section by user intent: 1. **Create / configure / scale** — deploy a container; change replicas, resources, autoscaling, bundle; inject credentials 2. **Diagnose** — workload is stuck, errored, or crash-looping 3. **Observe** — logs, traces, metrics, service stats for a running workload 4. **Artifact lifecycle** — iterate drafts, build images, lock for production, roll out new versions ## Prerequisites `DATAROBOT_ENDPOINT` (must end in `/api/v2`) and `DATAROBOT_API_TOKEN` must be set. Run `datarobot-setup` if not. Auth header: `Authorization: Bearer ${DATAROBOT_API_TOKEN}`. The Workload API is not in the `datarobot` Python SDK — call REST directly. **Transport.** Examples use Python `httpx` (`pip install httpx`). The API is plain HTTP, so equivalent calls work via `curl` or the `pulumi-datarobot` Pulumi provider declaratively. The skill teaches the model; transport is interchangeable. ## Bundled scripts Runnable Python in `scripts/` (this skill's folder). Each uses `httpx` and reads `DATAROBOT_ENDPOINT` + `DATAROBOT_API_TOKEN`: - `wait_for_running.py <workload_id>` — poll until `running`; exit 2 on terminal failure, 3 on timeout - `diagnose_workload.py <workload_id>` — run the 5-step debug flow, print a structured diagnosis (`--json` for machine-readable) - `wait_for_build.py <artifact_id> <build_id>` — poll a server-side image build; dumps las...