How does autoscaling work with the Modal skill?

Modal automatically scales your functions based on the number of inputs or requests, scaling up to thousands of containers and back to zero when idle to save costs.

How does Modal handle data persistence?

Modal uses 'Volumes' to provide persistent storage that can be shared across multiple function containers, making it easy to store model weights or large datasets.

Do I need a separate account to use this skill?

Yes, you must sign up at modal.com and authenticate via the Modal CLI to generate an API token for your environment.

What is the Modal skill for Claude Code?

The Modal skill allows Claude to write, configure, and deploy serverless Python code directly to the Modal platform for high-performance cloud computing.

Can I use this skill for machine learning tasks?

Absolutely. Modal is optimized for ML workloads, offering easy access to powerful GPUs and the ability to serve models as serverless endpoints.

Modal Serverless Cloud

Name: Modal Serverless Cloud
Author: sanand0

bysanand0

•

Cloud Infrastructure

Executes Python code in serverless containers with automated scaling and on-demand GPU acceleration.

About

The Modal skill empowers Claude to deploy, manage, and scale Python applications seamlessly in the cloud without the burden of complex infrastructure configuration. It provides a robust interface for defining custom container images, attaching high-performance NVIDIA GPUs, and managing persistent storage via Modal Volumes. This skill is ideal for developers needing to transition local Python scripts into production-ready cloud functions, whether they are serving large language models, running massive parallel batch processing jobs, or scheduling periodic data pipelines.

Key Features

Persistent data storage and caching via Modal Volumes
Secure environment variable and secret management
Integrated web endpoint deployment and cron-based job scheduling
Automatic scaling from zero to thousands of concurrent containers
3 GitHub stars
On-demand GPU access ranging from T4 to H100/B200

Use Cases

Building serverless APIs that require dynamic scaling and high compute
Deploying and serving LLMs or image generation models with GPU support
Large-scale parallel batch processing for data science and research

About

Key Features

Persistent data storage and caching via Modal Volumes
Secure environment variable and secret management
Integrated web endpoint deployment and cron-based job scheduling
Automatic scaling from zero to thousands of concurrent containers
3 GitHub stars
On-demand GPU access ranging from T4 to H100/B200

Use Cases

Building serverless APIs that require dynamic scaling and high compute
Deploying and serving LLMs or image generation models with GPU support
Large-scale parallel batch processing for data science and research