Remote compute over SSH

Use this skill after choosing an ssh:<alias> execution context. Wisp owns the job lifecycle locally: run_in_context creates the Run record, stages explicit inputs with persisted byte progress, and starts a detached supervisor on the server. The Runs panel and SQLite record remain authoritative if the conversation ends or Wisp restarts.

Dispatch workflow

1. Use short run_in_context calls for read-only discovery such as

nvidia-smi -L, which python3, or module avail. Free-form shell SSH is disabled. Use the interpreter path reported by the context probe; do not assume a python alias exists when the probe found python3.

1. Put the real command in one run_in_context call. Include environment

activation in the command so the Run is reproducible.

1. To watch the Run or wait for later work, call monitor_run exactly once with

the returned Run id. Wisp inserts a live card in the conversation, suspends the tool without additional model calls, and resumes the same agent turn with the terminal result. For fire-and-forget work, report the Run id and end the turn instead.

1. Use get_run only for one explicit status snapshot; never call it repeatedly

to wait. Use cancel_run when the user asks to stop.

Never monitor a Run with Start-Sleep, sleep, ssh ... ps, kill -0, a shell polling loop, nohup, background &, or hand-written PID files. Those duplicate the control plane and can strand the agent turn. A transient SSH error is stored as last_poll_error; do not resubmit, because Wisp retries the same idempotent remote handle.

json
{
  "context_id": "ssh:gpu-box",
  "title": "Motif enrichment across 2,000 backgrounds",
  "command": "source ~/miniforge3/etc/profile.d/conda.sh && conda activate genomics && python motif_enrichment_analysis.py",
  "timeout_secs": 14400,
  "input_paths": ["scripts/motif_enrichment_analysis.py"]
}

Then, when live monitoring is needed:

json
{ "run_id": "<id returned by run_in_context>" }

Pass that object to monitor_run once. The call may remain suspended for hours; it does not consume model tokens while the Run Manager watches the job.

input_paths are project-relative local files. Wisp validates them, copies them into an isolated inputs/ directory, and flattens them to their basenames. The command starts in that directory, so the example above can use the staged script by basename. Upload progress, throughput, and ETA appear in the Run card. For a large dataset already on the server, reference its absolute remote path in command; do not copy it back to the laptop just to send it out again.

A remote command or application exiting non-zero is normal exploration after a successful login. Read stderr, correct the command from the probed capabilities, and continue. Stop only when SSH rejects authentication or host trust; do not repeat a rejected login with guessed credentials or SSH options.

The control directory is ~/.wisp-science/runs/<run-id> and the command starts in its inputs/ subdirectory. stdout and stderr are tailed into the Run record. The SSH supervisor requires setsid, GNU-compatible timeout, bash, and /proc; a missing prerequisite fails the Run instead of running without a wall-time limit. Wisp maps the supervisor timeout marker to timed_out.

Results

SSH-direct v1 does not expand remote output globs or automatically download a remote directory. Do not promise that relative output_specs will be harvested. When the command writes a result to a known durable server path, it may register that exact path as a remote Artifact reference:

json
{
  "output_specs": [
    {
      "glob": "ssh://gpu-box/home/me/project/results/motif_enrichment_all.tsv",
      "kind": "table",
      "residency": "remote"
    }
  ]
}

For a small result that must become local, wait until the Run is terminal, then transfer it as a separate quick operation and register the local file. Large outputs should remain remote references.

Transfers between local and SSH contexts

Use transfer_between_contexts for one exact remote file or directory. The destination may be another selected SSH context or local. Never compose nested ssh, scp, or rsync -e ssh inside run_in_context.

For a local upload, set source_context_id to local, provide the exact existing absolute local file or directory, and select an SSH destination. Wisp rejects globs, symlinks, special files, and existing remote destinations. Call monitor_run once with the returned Run id.

For a local download, set destination_context_id to local and provide the exact new absolute local path. Ask the user when that path is unspecified. Wisp stages the item beside the destination, never overwrites an existing path, and removes partial staging data after failure or cancellation. Call monitor_run once with the returned Run id.

When the user approves persistent A→B trust, call configure_ssh_trust first. It creates a dedicated key on A, carries only the public key through Wisp, installs it on B, and verifies the directed edge. The transfer then prefers rsync when both servers provide it and falls back to scp. If the user does not want server SSH configuration changed, select the relay route; Wisp downloads to a private local temporary directory and uploads with B's separately stored credentials.

Cancellation and recovery

cancel_run({"run_id":"..."}) changes an SSH Run to cancelling. Wisp verifies the persisted token, PGID, and Linux process start time before sending TERM to the remote process group; it records cancelled only after remote confirmation. If the server is temporarily unreachable, the Run stays cancelling and retry continues after reconnection or app restart.

Active statuses are submitted, running, and cancelling. Terminal statuses are succeeded, failed, timed_out, cancelled, and lost. lost means the remote token/control directory/process identity was definitively missing, not merely that one SSH poll failed.

Current boundary

This implementation is SSH-direct and assumes a Linux-like server with sh, bash, nohup, setsid, and /proc. Do not daemonize or create a new session inside the job, because that escapes process-group cancellation.

Scheduler lifecycle is not implemented yet. Do not submit sbatch, qsub, or bsub through this direct runner: the Run would only track the short submit command, not the scheduler job. On a shared login node, ask the user for a dedicated compute host or explain that scheduler-aware submit/poll/cancel is a separate capability still needed.