Run agent sandboxes and GPU jobs with Beam
Use Beam when an agent needs remote Python sandboxes, serverless GPU inference endpoints, or retryable background tasks without managing infrastructure.
npx skills add agentskillexchange/skills --skill run-agent-sandboxes-and-gpu-jobs-with-beam
Use Beam when an agent workflow needs to execute generated code in an isolated remote container, deploy a GPU-backed inference endpoint, or queue background work without provisioning its own scheduler and compute layer. The operator installs the Beam client, defines sandboxes, endpoints, or task queues in Python, and lets the agent submit or deploy bounded workloads with explicit resource settings. Invoke this instead of using local execution or a normal cloud console when the user needs repeatable agent-operated sandboxes, GPU inference, or background tasks from source-controlled Python. The scope boundary is running controlled Beam workloads for code execution, inference, and queued tasks; it is not a generic cloud platform or broad serverless framework listing.
What this skill actually does
Inputs and prerequisites: Beam client, Python, Beam account or self-hosted Beam runtime, container/GPU resources as needed.
Setup notes: Install the Python client with pip install beam-client, complete the Beam onboarding or self-hosted setup, then define Sandbox, endpoint, or task_queue workloads in Python and deploy or invoke them with the Beam tooling.
Source and verification boundary: use https://docs.beam.cloud as the canonical reference before running the workflow; keep commands, API calls, CLI usage, and generated outputs reviewable against that upstream source.
Framework fit: publish this as a Multi-Framework workflow only when the operator can invoke the documented toolchain directly, rather than treating the upstream project as a generic product listing.