From lepton-cli
Operates NVIDIA DGX Cloud Lepton resources via the `lep` CLI: workspaces, endpoints, dev pods, batch jobs, fine-tuning, Ray/Slurm clusters, storage, secrets, ingress, and auth.
How this skill is triggered — by the user, by Claude, or both
Slash command
/lepton-cli:lepton-cliThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
Use the globally installed `lep` command to operate NVIDIA DGX Cloud Lepton resources from the terminal.
Use the globally installed lep command to operate NVIDIA DGX Cloud Lepton resources from the terminal.
Run commands directly:
lep <command> [args...]
Before doing real work, verify the CLI is available:
lep --help
If lep is missing, tell the user the global Lepton CLI is not available in the current environment and ask them to install or expose it on PATH. Do not attempt to download or install it yourself.
lep --help and lep <group> --help before any unfamiliar command.lep <group> <subcommand> --help for exact flags and required arguments.lep help output as authoritative for command behavior — command availability and flags vary by version.Lepton authentication is scoped to a single workspace. A logged-in lep CLI may have a default workspace, but that default must not be assumed when the user has not named a workspace.
The CLI may read local configuration and these environment variables:
LEP_API_URL — workspace URL used by lep.LEP_TOKEN — workspace auth token.LEP_WORKSPACE — default workspace name/display name.LEP_ENV — optional environment label.Rules:
lep workspace auth-status, lep workspace status, lep workspace list, lep workspace url, or lep workspace id.LEP_* environment variables when available.LEP_* variables above when using environment credentials. Do not substitute LEPTON_WORKSPACE_* names unless the installed lep help or local configuration proves this CLI version reads them.lep workspace token, print config files, echo tokens, or include tokens in final answers unless the user explicitly asks.lep workspace login -i <id> -t <token> when the user explicitly provides credentials and accepts that the token may be persisted by the CLI.lep command fails with FailedToOpenSocket, DNS, or other connection errors that look like a sandboxed network, surface the error to the user and ask whether to retry with broader network permission rather than silently retrying.--help.Destructive or high-impact commands include remove, delete, stop, stop-all, remove-all, rm, rmdir, update, restart, create, uploads/downloads to user-sensitive paths, and interactive pod ssh.
Workloads are endpoints/deployments, dev pods, batch jobs, fine-tuning jobs, Ray clusters, Slurm clusters, and Dynamo endpoints (see references/workloads.md). Any command that modifies or deletes an existing workload requires explicit confirmation before it runs — even when auto mode is active or the user has previously approved similar actions in the session. Authorization does not carry over between resources or invocations.
Before running the mutation, read the workload's current state with a narrow read-only command, then show the user: the exact lep command to be run, the target workload name, the workspace, and the current state. State what will change in one sentence (e.g., "About to delete endpoint foo in workspace bar — this is irreversible") and ask the user to confirm. Only execute the mutating command after an explicit yes. A waiver applies only to the single named workload — do not generalize it to other workloads or to a later command.
--output or when the command naturally emits structured JSON.--path, download, or upload, verify the source and destination before running.deployment may be available as an alias for endpoint.lep <group> --help before concluding it is unsupported.npx claudepluginhub johnny-rice/leptonai --plugin lepton-cliLaunches and manages GPU/TPU/CPU workloads across 25+ clouds, Kubernetes, and Slurm. Handles training, fine-tuning, inference serving with autoscaling, and cost optimization.
Provisions and manages on-demand/reserved GPU clusters (H100, H200, B200) on Together AI with Kubernetes or Slurm orchestration, shared storage, credentials, and scaling for ML/HPC workloads.
Deploy, monitor, and debug long GPU jobs on rented/remote instances: teardown/billing safety, spot resilience, resumable checkpointing, OOM/NaN triage.