Does this skill handle environment variables?

Yes, it provides best practices for using Modal Secrets to securely manage API keys and credentials without hardcoding them in your source code.

How does the skill help with scaling?

It provides code patterns for using .map() and other Modal primitives to automatically parallelize tasks across multiple containers based on workload demand.

Can I deploy machine learning models using this skill?

Yes, it includes implementation patterns for serving ML models via web endpoints, including specific configurations for different GPU types like H100 and A100.

What is the Modal skill for Claude Code?

The Modal skill provides specialized guidance for Claude to help you write, deploy, and manage serverless Python applications that utilize cloud GPUs and autoscaling infrastructure.

Modal Serverless Cloud

Name: Modal Serverless Cloud
Author: eyadsibai

byeyadsibai

Cloud Infrastructure

Deploys and scales machine learning models and GPU-accelerated workloads using the Modal serverless platform.

About

This skill empowers Claude to assist with the Modal serverless platform, enabling the execution of Python code with on-demand access to high-performance GPUs, autoscaling compute, and persistent storage. It provides domain-specific patterns for building container images, managing secrets, and deploying serverless functions as web endpoints or scheduled cron jobs. This is ideal for developers who need to move from local prototyping to cloud-scale infrastructure for AI/ML tasks without the overhead of manual cluster management.

Key Features

Simplified deployment of autoscaling web endpoints and REST APIs
Parallelized batch processing and scheduled job execution patterns
0 GitHub stars
Automated container image building with custom Python dependencies
Persistent data management via Modal Volumes and secure Secrets
Serverless GPU orchestration including H100, A100, and L40S configurations

Use Cases

Scaling data processing pipelines across hundreds of parallel containers
Serving large language models (LLMs) and image generation APIs
Scheduling compute-intensive periodic tasks like model retraining or daily backups

About

Key Features

Simplified deployment of autoscaling web endpoints and REST APIs
Parallelized batch processing and scheduled job execution patterns
0 GitHub stars
Automated container image building with custom Python dependencies
Persistent data management via Modal Volumes and secure Secrets
Serverless GPU orchestration including H100, A100, and L40S configurations

Use Cases

Scaling data processing pipelines across hundreds of parallel containers
Serving large language models (LLMs) and image generation APIs
Scheduling compute-intensive periodic tasks like model retraining or daily backups