What is the Modal skill for Claude Code?

The Modal skill enables Claude to help you write, deploy, and manage Python functions on Modal's serverless cloud infrastructure directly from your workflow.

How does Modal handle containerization?

Modal builds container images declaratively in Python. You can specify system packages, pip requirements, and custom files without writing a separate Dockerfile.

Can I use Modal for persistent data storage?

Yes, Modal Volumes allow you to store datasets, model weights, and logs that persist across different function invocations.

Does this skill support GPU acceleration?

Yes, it provides full support for requesting specialized GPUs such as NVIDIA A100, H100, and L40S for intensive AI and ML tasks.

Is it possible to schedule recurring tasks?

Absolutely. You can use the skill to set up scheduled jobs using standard cron syntax or periodic intervals for automated data processing or backups.

Modal Serverless Python

Name: Modal Serverless Python
Author: K-Dense-AI

byK-Dense-AI

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2,188

Data Science & ML

Runs Python code in the cloud with serverless containers, autoscaling GPUs, and minimal infrastructure management.

About

The Modal skill for Claude Code enables seamless execution of Python functions in a serverless cloud environment. It allows developers to define compute requirements, custom container images, and GPU acceleration directly within their Python code, eliminating the need for complex Kubernetes or Docker configuration. This skill is ideal for intensive AI/ML workloads, distributed data processing, and deploying scalable web endpoints where performance and dynamic scaling are critical.

Key Features

Instant GPU provisioning including H100, A100, and B200 instances
Serverless web endpoints and scheduled cron job management
Declarative container image building and dependency management
Automatic scaling from zero to thousands of containers based on demand
Persistent data storage using high-performance Volumes
2,188 GitHub stars

Use Cases

Parallel batch processing of massive scientific datasets
Training and fine-tuning machine learning models on high-performance GPUs
Deploying and serving large language models (LLMs) or image generation APIs

About

Key Features

Instant GPU provisioning including H100, A100, and B200 instances
Serverless web endpoints and scheduled cron job management
Declarative container image building and dependency management
Automatic scaling from zero to thousands of containers based on demand
Persistent data storage using high-performance Volumes
2,188 GitHub stars

Use Cases

Parallel batch processing of massive scientific datasets
Training and fine-tuning machine learning models on high-performance GPUs
Deploying and serving large language models (LLMs) or image generation APIs