Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add AlterLab-IEU/AlterLab-Academic-Skills --skill alterlab-remote-computegit clone --depth 1 https://github.com/AlterLab-IEU/AlterLab-Academic-SkillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/alterlab-ieu/alterlab-academic-skills/alterlab-remote-compute)<a href="https://agentmods.dev/skills/alterlab-ieu/alterlab-academic-skills/alterlab-remote-compute"><img src="https://agentmods.dev/badge/skills/alterlab-ieu/alterlab-academic-skills/alterlab-remote-compute/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/alterlab-ieu/alterlab-academic-skills/alterlab-remote-compute"><img src="https://agentmods.dev/badge/skills/alterlab-ieu/alterlab-academic-skills/alterlab-remote-compute.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00156 | $0.01875 |
| Opus 5 | $0.00078 | $0.00937 |
| Sonnet 5 | $0.00031 | $0.00375 |
| Haiku 4.5 | $0.00016 | $0.00187 |
Grade A, and why
alterlab-remote-compute scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 5d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
It shells out to `sbatch`/`sacct` for SLURM and uses `urllib` for the REST backend — no How it starts
The opening of the file, as written. The whole thing — 148 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Remote Compute
Overview
Foundation-model workloads (protein folding, backbone diffusion, single-cell models) need a GPU and can run for minutes to hours — too long to sit in a synchronous call. This skill is the provider-agnostic dispatch layer the GPU skills build on: a single submit → poll → harvest contract that works the same whether the backend is a SLURM cluster, Modal, RunPod, or GCP. You describe the job once; the dispatcher submits it, returns a handle, polls status to a terminal state, and harvests the output artifacts.
It does not wrap any single model — each model skill (alterlab-alphafold,
alterlab-boltz, alterlab-proteinmpnn, …) describes what to run; this skill describes
where and how to run it.
When to Use This Skill
Use this skill when the user wants to:
- Submit a batch job to a SLURM/HPC cluster and track it to completion (
sbatch→sacct). - Run a GPU job on a managed provider (Modal, RunPod, GCP Batch / Vertex AI) and retrieve results.
- Write a portable job wrapper that runs the same payload across more than one backend.
- Poll a long-running remote job's status and harvest its output files/artifacts.
Does NOT Trigger
| Scenario | Use instead |
|---|---|
| Deploy a serverless container / autoscaling API specifically on Modal | alterlab-modal |
| Actually fold a structure, design a sequence, or run a specific model | the model's own skill (alterlab-alphafold, alterlab-boltz, alterlab-proteinmpnn, …) |
| Local single-machine data analysis with no remote dispatch | the relevant analysis skill (alterlab-scanpy, alterlab-rdkit, …) |
| Query a database over HTTP | the database connector skill (alterlab-pdb, alterlab-uniprot, …) |
The submit → poll → harvest contract
Every backend implements three verbs. Keeping the payload backend-independent is what makes a model skill portable across an HPC allocation and a cloud GPU:
- submit(spec) → handle — enqueue the job; return an opaque handle (SLURM job id, Modal call id, RunPod job id, GCP operation name).
- poll(handle) → status — map the backend's states to a common vocabulary:
PENDING | RUNNING | SUCCEEDED | FAILED | CANCELLED | UNKNOWN. Poll on a backoff; never busy-loop. - harvest(handle) → artifacts — copy the declared output files back to a local
out/directory (scp/rsync from HPC scratch; object-store download for cloud).
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 5d ago First seen · 148 lines · 156 tokens per session scan A 48b6bc95e01c
alterlab-remote-compute is a skill published in the GitHub repository AlterLab-IEU/AlterLab-Academic-Skills (66 stars, last pushed 6d ago), licensed MIT. It adds 156 tokens to every session and 1,875 once invoked, about $0.0008 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-05.
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