Remote Compute Ssh

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.
2. Put the real command in one run_in_context call. Include environment activation in the command so the Run is reproducible.
3. To watch the Run or wait for later work, call monitor_run 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. If the result has wait_interrupted: true, the remote process is still running: answer the user from the snapshot, then call monitor_run again with the same id. Do not resubmit. Use cancel_run only when the user asked to stop. For fire-and-forget work, report the Run id and end the turn instead.
4. 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.

{
  "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:

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

Pass that object to monitor_run. The call may remain suspended for hours; it does not consume model tokens while the Run Manager watches the job. If wait_interrupted is true, respond from the snapshot and call monitor_run again with the same id; do not resubmit.

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

Declare output_specs with workdir-relative globs for the final products. After the Run succeeds, Wisp collects the matches on the server, checksums them, pulls them back through a persisted transfer Run, places them under the project's configured results directory, and registers each as an ArtifactVersion. The Run records harvested_at once registration completes; harvest_run({"run_id":"..."}) retries a failed or interrupted harvest.

Selection is the database boundary: only spec-matched outputs are transferred and recorded. Point globs at final products (for example Trinity.fasta), never at intermediate trees. A non-bundle glob may match at most 500 files. For a many-file output that must be kept, set bundle: true so the matches (or a whole directory) arrive as one tar.gz archive registered as a single artifact:

{
  "output_specs": [
    { "glob": "results/*.tsv", "kind": "table", "residency": "auto" },
    { "glob": "assembly_out", "kind": "archive", "residency": "local", "bundle": true }
  ]
}

Files over the size caps (or residency: "remote") are moved out of the run workdir into the project's persistent remote data area, registered as ssh:// references with checksum and size, and ledgered so they stay visible in list_remote_files. Workspace cleanup never orphans them. Delete a ledgered persist file with remove_remote_files only after the user confirms they no longer need it — that marks the artifact's source discarded. Explicit ssh://… URIs in output_specs still register a remote reference without any download.

Cleanup: servers are disposable

Tasks and artifacts belong to the project; the server only computes. After the results are harvested (or knowingly abandoned), reclaim the workspace:

• cleanup_run_workspace({"run_id":"..."}) deletes the Run's ~/.wisp-science/runs/<run-id> directory (inputs, logs, intermediates). A succeeded Run with declared output_specs must be harvested first; the tool refuses otherwise so results are never lost. Registered artifacts stay in the project. Before deletion Wisp pulls a trailing slice of stdout/stderr (at most 4 MiB per stream) into runs/<id>/ — not the complete remote logs.
• list_remote_files({"context_id":"ssh:<alias>"}) shows every file this project placed on the server (staged inputs, uploads, and harvest-persisted outputs) classified as active, replaced, or orphan; remove_remote_files deletes retracted ones. Current successful uploads stay active — they are the user's dataset, not sweep fodder. Replaced rows are closed in the ledger only (they share a path with the current file). Harvest-persisted outputs stay active while a live External artifact still points at them. Uploads are ledgered when the transfer attempt starts, so a failed or cancelled partial is visible and can be removed.
• Removing the SSH host from Settings audits remaining references and ledgered files, then marks those External artifacts as source-discarded. Later download, preview, or transfer of those URIs is refused even if the same alias is re-registered.
• Project settings can enable retention windows that automatically clean succeeded+harvested and failed run workspaces after N days.

Intermediate files (for example Trinity's hundreds of thousands of read partitions) should never be enumerated, downloaded, or registered — leave them in the workdir and let cleanup reclaim them in one deletion.