Orchestrate Runpod GPU tasks via transcript-backed SSH sessions with a pod_id.
npx skills add https://github.com/jimezsa/opencolab --skill runpod-job
Please help me install this Agent Skill. Skill: runpod-job Source: https://github.com/jimezsa/opencolab/tree/main/projects/SKILLS/runpod-job Command: npx skills add https://github.com/jimezsa/opencolab --skill runpod-job
This Skill helps teams manage Runpod GPU tasks with a manual, human-in-the-loop workflow instead of automatic provisioning, by guiding the user to supply a pod_id, SSH access details, and a saved profile, then executing the GPU work through transcript-backed SSH sessions.
Provide the pod_id and SSH details to start a manual Runpod Pod workflow.
High-intent search queries and answers about installing and using this skill.
To manage Runpod GPU jobs manually via SSH, you provide a pre-created pod_id and SSH access details to establish a human-in-the-loop workflow. The agent then executes your GPU tasks through bounded, transcript-backed SSH sessions for safe operations.
A manual Runpod Pod workflow is a process where you control GPU resources by supplying a pod_id and SSH details rather than relying on automatic provisioning. Use it when you prefer human-in-the-loop control over pre-created Pods for executing GPU tasks.
Yes, you must pre-create your Pod and provide the specific pod_id and SSH access details. This Skill enforces a manual workflow by requiring these pre-existing resources instead of automatically provisioning new GPU instances.
Yes, you can opt into an OpenColab-managed lifecycle for run_id tracking on explicit request. By default, the workflow uses transcript-backed SSH sessions, but the managed path is available when you need automated run tracking.
Your Runpod agent uses bounded SSH sessions to ensure safe, controlled operations during manual GPU task execution. This prevents open-ended shell access, keeping the human-in-the-loop workflow secure and limiting commands to the defined session scope.
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