What problem does it solve? Running untrusted or AI-generated code safely is hard: in-process sandboxes like vm2 have known escapes, and child_process or eval share your process and credentials. This Skill provides instructions for executing code inside isolated Firecracker microVMs with controlled networking, filesystem access, and lifecycle management. ## Core Features & Use Cases - Isolated Code Execution: Create on-demand Linux VMs, run commands, read and write files, and stop or snapshot sessions using the @vercel/sandbox JavaScript SDK, Python SDK, or CLI. - Egress Firewall & Credential Brokering: Apply networkPolicy rules (deny-all or domain allow-lists) and inject auth headers at the firewall so secrets never enter the VM. - Persistence, Snapshots & Drives: Resume persistent sandboxes by name, boot from filesystem snapshots to skip reinstalls, and mount shared drives across sandboxes. - Use Case: An AI coding agent needs to execute generated code and call an LLM API. Create a sandbox with an allow-list permitting only ai-gateway.vercel.sh, broker the OIDC token at the firewall, and run the agent without exposing any credential inside the VM. ## Quick Start Ask the assistant to create a Vercel Sandbox, run a Python command inside it, print the output, and stop the sandbox in a finally block.