---

name: remote-execution-cli description: "Configure ClearML and build safe remote execution commands or SDK launch flows with clearml-task, clearml-init, clearml-debug, Task.create, Task.clone, Task.enqueue, and Task.execute_remotely." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

ClearML Remote Execution CLI

Use this sub-skill when an agent needs to configure ClearML, launch code on a clearml-agent, create or clone a remote task, enqueue an existing task, import an offline session, or diagnose why a clearml-task/remote execution request is not running.

Start Here

1. Confirm the user has a ClearML server target and credentials configured via environment variables or a ClearML config file; use scripts/validate_clearml_environment.py for a read-only signal check that does not print secret values.
2. Decide whether the launch is CLI-first (clearml-task) or SDK-first (Task.create, Task.clone, Task.enqueue, Task.execute_remotely). Use CLI-first for CI/CD and command construction; use SDK-first when code already owns a Task object or needs programmatic clone/enqueue behavior.
3. Build commands with scripts/build_clearml_task_command.py, which validates incompatible options and prints a shell-safe command without contacting a ClearML server.
4. Use references/cli-reference.md for command flags and templates, references/workflows.md for launch patterns, and references/troubleshooting.md for failure triage.

Route Related Work

• For metrics, plots, logs, models, or artifacts inside the launched script, use ../experiment-tracking/SKILL.md.
• For clearml-data, Dataset, StorageManager, or dataset storage workflows, use ../data-storage/SKILL.md.
• For pipelines, pipeline CLI flags, schedulers, services, or HPO, use ../automation-pipelines/SKILL.md.

Safe Operating Rules

• Do not run clearml-task, clearml-init, clearml-debug token, or SDK launch/enqueue code unless the user explicitly wants side effects against a ClearML server.
• Prefer producing a reviewed command and validation checklist; remote execution requires credentials, network access, and a polling clearml-agent on the selected queue.
• Never echo ClearML secret values in instructions, logs, or generated scripts. Redact or report presence only.
• Treat repository, Docker image, package, requirements, queue, and output URI as deployment decisions; ask when any are missing or ambiguous.
• If no --queue is provided, clearml-task creates a draft task but does not launch it.

Minimum Launch Checklist

• --project and --name are set unless using --import-offline-session.
• Exactly one entry style is chosen: --script, --module, --base-task-id, or --import-offline-session.
• Code source is explicit: --repo for a remote repository, --folder for a local git checkout, or --skip-repo-detection when source capture is intentionally disabled.
• Environment is explicit enough for the agent: --requirements, --packages, --force-no-requirements, or --skip-python-env-install.
• Queue name matches a live ClearML agent pool; otherwise the task remains queued.
• Use --skip-task-init only when the script already calls Task.init() or should not be patched by clearml-task.