remote-compute-ssh

name	remote-compute-ssh
description	Evaluate and use SSH Remote Compute before choosing where to run GPU, high-memory, parallel, batch, model-inference, bioinformatics, or other long-running scientific work; supports short remote commands and asynchronous jobs with automatic harvest and analysis.
license	Apache-2.0

This skill covers remote compute over SSH: listing hosts, creating handles, running short remote commands (callCommand), reading/writing host knowledge docs, and the full async job lifecycle — submit → harvest → analysis turn → publish artifacts.

Where host.compute runs: host.compute lives ONLY on the control-plane REPL kernel — run every example below with the repl_execute tool (JavaScript), the same kernel that hosts host.mcp. The python/r data kernels have NO host.compute (SSH and approvals stay outside the sandbox workspace); calling it from a python/r cell will fail with host.compute is undefined.

Choose an execution location

Only Compute Hosts enabled for this Session are visible or callable. Discover them in one catalog; each entry has role selected or available. A non-empty selected pool is an execution instruction: run tool-backed task work on one or more selected hosts as the task requires. The pool has no priority and does not imply automatic multi-host scheduling. If no host is selected, choose from the available entries. Read details() only for candidates that need closer evaluation.

Never guess or reuse a provider id absent from the catalog. A user naming a disabled host does not make it callable; explain that it must first be enabled for this Session. If no eligible host is usable, explain the blocker and ask the user how to proceed.

const hosts = await host.compute.listHosts()
const selectedHosts = hosts.filter((host) => host.role === 'selected')
const candidates = selectedHosts.length > 0 ? selectedHosts : hosts

Each list item is a compact summary with provider_id, display_name, shape, status, and role (last_probe_ok, probe_failed, or not_probed). last_probe_ok means the most recent persisted Probe succeeded; it does not assert live connectivity. Knowledge documents and resource probe snapshots are deliberately excluded from discovery results.

API reference

// List this Session's enabled hosts as one role-bearing compact catalog
const hosts = await host.compute.listHosts()

// Compatibility discovery names remain available; both still hide disabled hosts.
const visibleHosts = await host.compute.listRegistered()
const selectedHosts = await host.compute.listPreferred()

// Create a handle to a specific host (no network call)
const c = host.compute.create('ssh:<alias>')

// Run a short remote command (throws on approval_denied / host_unreachable / timeout)
const result = await c.callCommand('<shell command>', '<one-line intent for the approval card>', {
  loginShell: true, // default: true — runs login profiles, then readable ~/.bashrc, before this command
  timeoutSeconds: 60 // optional — the host applies its own default (60s) when omitted
})
// result → { exit_code, stdout, stderr, truncated }

// Read the host knowledge doc and resource probe snapshot on demand.
// probe is explicitly null when this host has never been probed.
const info = await host.compute.details('ssh:<alias>', { mode: 'read' })

// Append a note to the host knowledge doc (agent writes; 32 KB cap enforced)
 host..(, {
  : ,
  : 
})


 host..(, {
  : ,
  : ,
  : info. 
})

With loginShell: true, the remote Bash login profiles run first and then Open Science attempts to source ~/.bashrc when it is readable. A .bashrc can deliberately return early for non-interactive shells, so variables declared after such a guard are not available. A missing .bashrc is a no-op. Set loginShell: false to run the command without either initialization step. Initialization failures are reported through the normal command result/error behavior.

API reference (async jobs)

Use submitJob for long-running computations (minutes to hours). It returns immediately with a job_id; the job runs on the remote host in the background. When the job finishes, the app automatically harvests the outputs and initiates a new analysis turn. Do not poll for completion; perform only the single bounded immediate-failure check below, then return control to the user.

// Reuse the `candidates` selected above from the Session catalog.

// Submit a non-blocking job — returns immediately after the user approves
const c = host.compute.create('ssh:<alias>')
const job = await c.submitJob(
  '<one-line intent for the approval card>', // shown in the approval card
  '<shell command>', // command to run remotely
  {
    timeoutSeconds: 3600, // optional; default 24 h, max 7 days
    inputs: [
      { src: 'in.dat', dstFilename: 'in.dat' }, // stage a workspace file
      { remotePath: 'ssh:<alias>/<abs_path>' } // link a remote file (no transfer)
    ],
    outputs: [
      '*.result', // featured (default visibility)
      { glob: '*.json', visibility: 'featured' }, // explicitly featured
      { glob: '*.log', visibility: 'hidden' }, // hidden (diagnostic, not shown in card)
      { glob: 'checkpoints/**', residency: 'remote' } // leave on remote — recorded in left_on_remote
    ],
    harvest: {
      exclude: ['work/**'], // never harvest these paths
      : , 
      :  
    }
  }
)


  ( (resolve, ))


 initial =  c.(job.).()
 initial

Immediate failure check after submission

Wait exactly once for 2 seconds (setTimeout(..., 2000)), then call .result() exactly once. The result read is non-blocking for submitted and running jobs and includes status, stdout, stderr, and error details already persisted by dispatch. This catches syntax errors, missing executables, and other scripts that fail as soon as they start without waiting for a long-running job or starting a second harvest. Do not wait again and do not turn this into a polling loop: after printing the snapshot, end the cell and let the app own the rest of the lifecycle.

End the cell after that one check. Do NOT write a polling loop. The app runs the poller and harvest in the background. When the job finishes, the app automatically starts a new analysis turn in this conversation — the conversation is NOT locked while the job runs, so the user can keep chatting.

Harvest safety boundaries

• Declared output files are selected before stdout and stderr; logs use the remaining per-job budget.
• The app rejects model-supplied limits above 100 MiB per file or 500 MiB per job.
• Harvest also preserves a fixed 2 GiB of free local disk space. Files that do not fit remain remote.

Behavior boundaries

• While the job runs: the conversation is open. The user can send messages; you can handle other tasks. No blocking wait.
• When the job finishes: the app initiates a new analysis turn automatically. You do not trigger this — it happens without any action on your part.
• Do NOT write a loop calling attachJob().status() to wait for completion. That is the app's job, not yours. Writing such a loop would block the conversation for the entire job duration.

Check job status (non-blocking read, for informational use)

// Non-blocking DB read — no SSH. Use if you need a status snapshot mid-conversation.
const handle = c.attachJob(job.job_id)
const s = await handle.status()
// s → { job_id, status, cancellation_status?, exit_code, stdout_tail, stderr_tail, remote_workdir }
// status: 'submitted' | 'running' | 'success' | 'failed' | 'timeout' | 'error'

To stop one active job, request durable cancellation through the same handle:

await c.attachJob(job.job_id).cancel()
// cancellation_status is 'cancelling' until owned remote termination is confirmed,
// then 'cancelled'. Repeating cancel() is safe.

submitJob status values

status	meaning
submitted	accepted; background dispatch in progress
running	remote process confirmed alive (pid recorded)
success	exit code 0
failed	non-zero exit (job_failed) or process vanished (process_vanished)
timeout	exceeded timeoutSeconds
error	never reached the remote host (host_unreachable / dispatch_failed)

Workflow: the analysis turn

When the app initiates the analysis turn, it provides the job_id, status, and featured_files (workspace-relative paths under hpc/<job_id>/featured/). In this turn:

1. Call attachJob(job_id).result() to get the full result dict.
2. Inspect the outputs, run any analysis needed.
3. Call write_artifact_file to publish outputs worth keeping as artifacts.

// In the analysis turn — read the full harvested result (non-blocking DB + directory scan)
const c = host.compute.create('ssh:<alias>')
const r = await c.attachJob(job_id).result()
// r → {
//   job_id, status, exit_code,
//   featured_files: ['hpc/<job_id>/featured/out.result', ...],   // workspace-relative
//   hidden_files:   ['hpc/<job_id>/hidden/run.log', ...],
//   output_files:   [...featured_files, ...hidden_files],         // featured first
//   left_on_remote: [{ uri: 'ssh:<alias>/<abs_path>', size_mb: 420, reason: 'residency:remote' }],
//   remote_workdir: '.openscience/jobs/<job_id>',
//   stdout_tail: '...last 64 KB...',
//   stderr_tail: '...last 64 KB...'
// }

Files land in the workspace at hpc/<job_id>/ and are readable directly:

# python cell — files are in the workspace; open() works with workspace-relative paths
import pandas as pd
df = pd.read_csv('hpc/<job_id>/featured/results.csv')

Publish artifacts

Harvest only lands files in the workspace — it does NOT publish artifacts automatically. Call write_artifact_file in the analysis turn to publish outputs worth keeping:

// In the analysis turn — publish featured outputs as artifacts (bound to this turn)
for (const path of r.featured_files) {
  await host.mcp('artifacts', 'write_artifact_file', { path })
}
// Artifacts appear in the artifact panel with provenance tied to this analysis turn.

When the job fails

Read r.exit_code and r.stderr_tail. An infrastructure failure (wrong partition, env not activated, missing module, OOM, walltime) is yours to fix — adjust command, record the fix, fresh c.submitJob(). A harvest failure (r.stderr_tail notes it, r.remote_workdir is preserved) means some files were not downloaded — the remote workdir is kept so you can c.callCommand('ls ...', intent='...') to inspect what's there.

Chaining jobs via left_on_remote

Large outputs declared with residency: 'remote' or files that exceed the size threshold stay on the remote host and appear in r.left_on_remote. Use their URIs directly as remotePath inputs to the next job — no local round-trip:

// In the analysis turn — chain a left_on_remote output into the next job
const big_output_uri = r.left_on_remote[0].uri // e.g. 'ssh:biowulf//scratch/jobs/<id>/big.h5'

const job2 = await c.submitJob(
  'process big.h5 output from job 1',
  'python process.py --input big.h5 --out summary.csv',
  {
    inputs: [
      { remotePath: big_output_uri } // symlinked in job workdir, no transfer
    ],
    outputs: ['summary.csv']
  }
)

Submitting several jobs

Submit a batch and let each job's analysis turn handle its results independently. The app triggers a separate analysis turn for each job as it finishes (or merges simultaneous completions into one turn with multiple job_ids):

// Submit multiple jobs — end the cell after all submits
const c = host.compute.create('ssh:gpu-cluster')
const jobs = []
for (const seed of [0, 1, 2, 3, 4]) {
  const job = await c.submitJob(
    `AlphaFold seed ${seed}`,
    `python fold.py --seed ${seed} --in input.fasta --out ranked.pdb`,
    {
      inputs: [{ src: 'input.fasta', dstFilename: 'input.fasta' }],
      outputs: [{ glob: '*.pdb', visibility: 'featured' }],
      timeoutSeconds: 3600
    }
  )
  jobs.push(job.job_id)
}
return jobs // end the cell — no waiting, no loop

The app triggers one analysis turn per job completion (or a merged turn for simultaneous completions). Do NOT write a loop collecting all results — each analysis turn handles its job independently.

Session concurrency control

Cap how many non-terminal jobs run at once across all providers in this conversation. Jobs that would exceed the cap enter a queued state and auto-dispatch when a slot frees up. These two methods live on the handle returned by create(), but they are session-scoped — they act on the whole conversation, not on the handle's bound provider.

const c = host.compute.create('ssh:<alias>')

// Set the conversation-wide limit (positive integer 1..500).
await c.setConcurrencyLimit(2)

// Read the session's concurrency status (non-blocking DB read, no SSH).
const s = await c.status()
// s → {
//   session_limit: number | null,            // the cap you set, or null if unset
//   active_count: number,                    // non-terminal jobs running now
//   queued_count: number,                    // jobs waiting for a slot
//   provider_ceilings: Record<string, number> // per-host hard limits (host config)
// }

callCommand error handling

try {
  const r = await c.callCommand('cmd', '<intent>')
} catch (e) {
  const code = e.error_code || ''
  if (code === 'host_unreachable') {
    // SSH connectivity issue — needs user action (VPN, key, etc.); e.retry_after_user_action is true
  } else if (code === 'approval_denied') {
    // User declined the approval card
  } else if (code === 'timeout') {
    // Command exceeded timeoutSeconds
  }
}

Typical first-contact workflow

1. await host.compute.details(provider_id, { mode: 'read' }) — a ## Resources skeleton means first contact; populated sections mean prior sessions did the legwork, trust them.
2. Bind once: const c = host.compute.create(provider_id).
3. Run one batched probe: await c.callCommand('id; module avail 2>&1 | head -40', '<intent>').
4. Append what you learned via await host.compute.details(..., { mode: 'append' }).

What to record in the knowledge doc

The knowledge doc is the only state that survives across sessions. Record:

• Scheduler type and any known partition/account combinations that worked.
• Environment activation commands (e.g. module load X/<ver>, conda activate <env>).
• Verified invocations tagged verified <date>; user-provided info tagged per user <date>.
• Gotchas specific to this host or provider.

Do NOT record per-job state, transient errors, or facts about your project — those belong elsewhere. When a session ends without new host-specific learnings, write nothing.