Menu

Individual skill

modal

Run Python code in the cloud with serverless containers, GPUs, and autoscaling. Use when deploying ML models, running batch processing jobs, scheduling compute-intensive tasks, or serving APIs that require GPU acceleration or dynamic scaling.

skills/modal/SKILL.md

Read pinned source

Source commit 894598cd3a90891a077c254566276bd379038bad

What was checked

Matches the observed repository HEAD
Yes
Scanner rules are current
Yes
Repository scan complete
No
Skill scan complete
No
Observed behavior
Network or broad shell
Last checked
Nov 5, 2026

These are observations at the recorded commit, not a guarantee of safety. Review the source before running it.

Install this skill

Choose an agent in your Stack or copy a command below. Commands fetch the pinned commit and copy only this skill directory. Existing destinations are refused. Copying a command does not install anything.

An install command is unavailable: current rules, complete repository and skill scans, an eligible behavior level and a permitted license are required.

Repository context

Repository stars (not skill ratings)
251
Repository last updated
Oct 30, 2026
License
Apache-2.0

Observed agent integration: No specific integration evidence. Standard SKILL.md installation does not imply verified compatibility.

Files and excerpt

File metadata and permitted, redacted excerpts come from the recorded scan. Source links are pinned to that commit.

Browse files
Read excerpt

# Modal

## Overview

Modal is a serverless platform for running Python code in the cloud with minimal configuration. Execute functions on powerful GPUs, scale automatically to thousands of containers, and pay only for compute used.

Modal is particularly suited for AI/ML workloads, high-performance batch processing, scheduled jobs, GPU inference, and serverless APIs. Sign up for free at https://modal.com and receive $30/month in credits.

## When to Use This Skill

Use Modal for:
- Deploying and serving ML models (LLMs, image generation, embedding models)
- Running GPU-accelerated computation (training, inference, rendering)
- Batch processing large datasets in parallel