Guide · Agent skill

How to use the Modal Serverless GPU agent skill

Updated 2026-10-29 · written with AI from the project's own documentation and checked against our catalogue data

The modal-serverless-gpu skill teaches your AI agent how to write code for Modal, a cloud platform that runs Python code on GPUs on demand, so you don't have to set up or manage servers. This guide is for people who may never have used an agent skill: it explains what the skill does, how to install it, how to ask for it, and what to check before running anything. By the end you will be able to install the skill, ask the agent for a GPU job or API, and know what you need in place to run it.

Modal Serverless GPUOrchestra-Research/AI-Research-SKILLs · MIT Download the skill

Quick start

1Check requirements. Confirm you have Python, a Modal account and a paid plan for GPU usage.
2Install modal. Run pip install modal in your terminal to install the package, version 0.64.0 or newer.
3Authenticate. Run modal setup and complete the sign-in in the browser window it opens.
4Download and unzip the skill. Download the ZIP from this page and unzip it into ~/.claude/skills/ or a project's .claude/skills/ folder.
5Ask for a GPU job. Describe the GPU workload, model or API you want in plain language so the agent loads the skill.
6Review and run. Read the generated code, check the GPU type, then run it with modal run or deploy it with modal deploy.

What the skill makes the agent do

An agent skill is a folder of instructions and reference files that an AI agent loads when your request matches the skill's description. With this skill loaded, the agent knows how to write Modal code that picks a GPU type, packages dependencies into a container image, stores models in persistent volumes, deploys functions as web APIs, and schedules recurring jobs.

The skill's description says it is for on-demand GPU access without infrastructure management, deploying ML models as APIs, or batch jobs with automatic scaling. Modal's pricing model is pay-per-second GPU use, and the platform scales to zero when idle.

Choose a GPU such as T4, L4, A10G, L40S, A100, H100, H200 or B200
Define the environment in Python code, with no YAML
Deploy a function as a REST API with a web endpoint decorator
Run jobs on a cron or periodic schedule

Install the skill from the ZIP

Download the ZIP for the skill from this page and unzip it. For a personal setup, place the unzipped skill folder in ~/.claude/skills/. For a single project, place it in that project's .claude/skills/ folder instead.

If you use the Claude apps, you can instead upload the skill under Settings → Capabilities → Skills.

Personal install: ~/.claude/skills/
Project install: .claude/skills/ inside your project
Claude apps: Settings → Capabilities → Skills

The skill requires Python with the modal package, version 0.64.0 or newer, installed with pip. You also need a Modal account and must authenticate through a browser. Modal is a paid platform for GPU usage, so a paid account is needed to run GPU workloads.

Run the following in your terminal. The setup command opens a browser window for authentication.

Python with the modal package (>=0.64.0)
A Modal account and a paid plan for GPU usage
Authentication through the browser with modal setup

pip install modal
modal setup  # Opens browser for authentication

Trigger the skill with natural requests

You don't need special syntax. Describe the outcome you want in plain language and mention GPUs, model APIs, or scheduled batch jobs. The skill's description lists these triggers, so requests in those areas are the ones it is built for.

Be specific about the model, the GPU size and whether you want a one-off run or a deployed endpoint. The skill's guide maps these to commands: modal run for a single execution, modal serve for development with live reload, and modal deploy for a persistent cloud deployment.

"Write a Modal function that runs nvidia-smi on an A10G and show me the output."
"Deploy a FastAPI endpoint on Modal that serves my text classifier, with a GPU."
"Make a Modal job that runs daily at midnight and processes yesterday's files on a GPU."
"Run a batch of 1000 inputs in parallel on Modal and collect the results."

Review generated code and secrets before running

The skill shows how to store credentials as Modal secrets, for example a Hugging Face token for gated models. The agent will run code against your paid cloud account, so read generated code before running it, especially code that handles tokens, data or GPU-heavy jobs that could be costly.

The skill's scripts and network access are part of how it works: the skill runs scripts and uses the network. Keep tokens out of the code itself and use Modal secrets instead.

Check the GPU type and timeout before a long job starts
Store tokens with modal secret create and read them as environment variables
Do not run code you have not reviewed if it touches sensitive data

modal secret create huggingface HF_TOKEN=hf_xxx

Troubleshoot common problems

The skill includes a troubleshooting reference. For authentication problems, the source suggests re-authenticating with modal token new and checking the current token with modal config show. For image build errors, pinning package versions is the suggested fix.

For a GPU out-of-memory error, the skill's common issues table suggests using a larger GPU such as A100-80GB.

Authentication fails: run modal token new, then modal config show
pip install modal fails: upgrade pip, or try python3.11 -m pip install modal
Image build fails: pin package versions in pip_install

modal token new
modal config show

What people use it for

Run a one-off GPU check or training script without owning a GPU
Deploy a prototype ML model as an auto-scaling HTTP API
Run a daily or hourly Python job on a schedule
Fan out a large batch of inputs across parallel containers

Frequently asked questions

How do I install the modal-serverless-gpu skill?

Download the ZIP from this page and unzip it into ~/.claude/skills/ for personal use, or into a project's .claude/skills/ folder. If you use the Claude apps, you can upload it under Settings → Capabilities → Skills.

What does the modal-serverless-gpu skill require?

It requires Python with the modal package, version 0.64.0 or newer, installed via pip. You also need a Modal account, authentication through modal setup in a browser, and a paid Modal account for GPU usage.

Is Modal free to use?

The source says Modal is a paid platform for GPU usage and bills per second of GPU use. It does not list prices, so check Modal's own pricing before running GPU jobs.

Do I need to know Python to use this skill?

Yes. The skill writes and runs Python code for Modal, and setup requires running terminal commands and authenticating an account. Users with no Python experience will find it hard to review the generated code.

What are the alternatives to Modal?

The skill itself points to RunPod for longer-running pods with persistent state, Lambda Labs for reserved GPU instances, SkyPilot for multi-cloud orchestration and cost optimization, and Kubernetes for complex multi-service architectures.

Why does my Modal install or authentication fail?

For authentication, the troubleshooting reference suggests running modal token new and checking the setup with modal config show. For pip install problems, it suggests upgrading pip or installing with a specific Python version such as python3.11 -m pip install modal.

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Our verdict

Use this skill if you are a developer who needs occasional GPU compute for inference, training or batch jobs, or who wants to turn a model into an auto-scaling API quickly, and you are comfortable reviewing Python code. Pick something else if you need a long-running pod with persistent state, which the skill itself suggests RunPod for, or if you have no Python experience or no paid Modal account.

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