remote-compute-modal

remote-compute-modal is a skill for Claude Code, Codex from UnicomAI/wanwu. It costs 65 tokens per session (10,803 once invoked), scanned A, original, Apache-2.0.

A workflow for running GPU or CPU jobs in the user's Modal cloud account. Modal is a service that runs code in cloud containers on selected hardware.

In plain words
What is it for?
Use it to create and submit Modal jobs, choose compute resources, prepare images and volumes, and wait for job completion notifications.
Why use it?
It makes the job setup, environment preparation, submission, waiting, and user approval steps explicit, including that cloud usage is billed to the user's account.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: mentions subagents.

Good fit Use it to create and submit Modal jobs, choose compute resources, prepare images and volumes, and wait for job completion notifications.

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Install with agentmods
npx agentmods add skills/unicomai/wanwu/remote-compute-modal
About the project

Wanwu is an enterprise platform for building AI agents, workflows, retrieval-augmented applications, and managing models in multi-tenant environments. It is designed for developers and enterprise teams delivering AI applications and integrations. The catalogue entries provide skills and agents for using the platform.

UnicomAI/wanwu · 2,376 stars · on GitHub

Install

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.

Any agent
npx skills add UnicomAI/wanwu --skill remote-compute-modal
Clone the repo
git clone --depth 1 https://github.com/UnicomAI/wanwu

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for remote-compute-modal

README.md
[![agentmods](https://agentmods.dev/badge/skills/unicomai/wanwu/remote-compute-modal/github.svg)](https://agentmods.dev/skills/unicomai/wanwu/remote-compute-modal)
Your own site
<a href="https://agentmods.dev/skills/unicomai/wanwu/remote-compute-modal"><img src="https://agentmods.dev/badge/skills/unicomai/wanwu/remote-compute-modal/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.

agentmods 80×15 button for remote-compute-modal

Your own site · 80×15
<a href="https://agentmods.dev/skills/unicomai/wanwu/remote-compute-modal"><img src="https://agentmods.dev/badge/skills/unicomai/wanwu/remote-compute-modal.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 65 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 10,803 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 1 finding. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector warn 7 Sept 2026
SkillSpector: 2 findings, up to medium

These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →

  • medium Rogue Agent · line 387
    Skill establishes unauthorized persistence across sessions via cron jobs, startup scripts, or state files. Session persistence allows an attacker to maintain access beyond the current interaction.
    Fix: Remove any persistence mechanisms (cron jobs, startup scripts, state files). Skills should not maintain state across sessions without explicit user consent.
  • medium Excessive Agency · line 389
    Skill allows unbounded resource consumption (API calls, storage, compute). Without rate limits or quotas, a compromised or misbehaving agent can cause denial-of-service or cost overruns.
    Fix: Set explicit rate limits, timeouts, and resource quotas for API calls, file operations, and compute. Implement circuit breakers for runaway loops.
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00065 $0.10803
Opus 5.5 $0.00026 $0.04321
Sonnet 5.5 $0.00013 $0.02161
Haiku 4.5 $0.00006 $0.01080

Measured 1mo ago against content hash 52e7603e2f30, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

remote-compute-modal 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 1mo ago.

The scan reads SKILL.md. This mod also ships 10 executable files (envs/chemistry_gpu.py, envs/esmfold2_gpu.py, envs/genomics_evo2_gpu.py, …), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

job ("let me curl a few sites to test the network") only reproduces the
configs/microservice/bff-service/configs/agent-skills/claude-science/remote-compute-modal/SKILL.md · 790 lines

How it starts

The opening of the file, as written. The whole thing — 790 lines — stays where its author put it; the contents beside it link to each section on GitHub.

You're dispatching to the user's Modal account: containers spin up in Modal's cloud, on hardware you name in plain terms (gpu/cpu/memory/timeout), under their workspace, on their bill. That's the reason there are two approval cards rather than one, and the reason the env-setup surface is a separate kernel rather than something you can call inline from the control-plane kernel: each card is the user consenting to a specific, bounded use of their credential, and the architecture keeps those grants legible by keeping the surfaces apart.

If compute.create('byoc:modal', …) returns unknown provider 'byoc:modal', Modal isn't enabled in this install — ask the user to enable it under Settings → Compute → Modal (or surface the prompt; you can't enable it for them).

For first-time environment setup, see env-setup.md in this skill directory — it walks through driving the compute_provider kernel to build and record images.

Two timeouts, one timeline

Modal has two deadline timers. Both live INSIDE the sandbox, and neither can cost you the outputs of a job that ran. The timeline every job runs on:

  1. Container life starts at sandbox creation — staging the inputs counts against it, so a big upload spends container time before the job runs.
  2. The job runs. If it exceeds its own budget (the job timeout below), it is TERMed there.
  3. Near end of container life — one harvest margin before the provider destroys the sandbox — the in-sandbox harvest watchdog TERMs whatever is still running. It runs on the sandbox's own clock, so a wrong desktop clock or a slow upload can't make it fire late.
  4. After any TERM there is a grace window for checkpoint-on-TERM handlers to flush, then the process group is stopped.
  5. Whatever is under ./out/ (plus the logs) is tarred and staged UNCONDITIONALLY — on success, timeout, failure, and crash alike — and harvested back into the workspace. Staging is unconditional; delivery isn't. If the stream is refused or gives up (result_rejected, harvest_failed), the staged copy waits on the sandbox, and a third, post-job timer — the idle watchdog — terminates it after ~30 min of inactivity. See "When the job fails".

The container timeout (provider_params.modal.timeout) is how long the sandbox lives; omitted, it fills from the Settings default for this provider (Settings → Compute → Modal; ceiling: Modal's 24 h platform lifetime, minus the staging margins — 85,500 s). The job timeout (timeout_seconds on submit_job) is an optional runaway guard for one job; omitted, it defaults to the container's remaining life minus the harvest margin. Name one when you know the job's budget — a hung job then costs that budget, not the whole container, and the warm container survives for the next submit.

A deadline-ended job lands as status: 'timed_out' — not a generic failure — with its partial outputs already harvested, plus a note saying the deadline (not the workload) ended the run and suggesting the remedy: a larger timeout_seconds, or resuming from a harvested checkpoint. A deadline ends the run, never the results.

Read the full file on GitHub · 790 lines

Changes

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.

  1. 1mo ago First seen · 790 lines · 65 tokens per session scan A 52e7603e2f30

Subscribe to this mod's changes

remote-compute-modal is a skill published in the GitHub repository UnicomAI/wanwu (2,376 stars, last pushed 6d ago), licensed Apache-2.0. It adds 65 tokens to every session and 10,803 once invoked, about $0.0003 per session on Opus 5.5. 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 2026-08-30.

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