What is the Modal skill for Claude Code?

The Modal skill allows Claude to help you write, deploy, and manage serverless Python applications that run in the cloud with automatic scaling and specialized hardware support.

How do I use GPUs with this skill?

You can request GPUs by adding the 'gpu' parameter to your Modal function decorators, choosing from types like T4, A100, or H100 depending on your compute needs.

Is it possible to schedule recurring tasks?

Yes, you can use the @app.function(schedule=...) decorator with cron expressions or time periods to automate background jobs and data processing.

Can I deploy web APIs using Modal and Claude?

Yes, this skill provides patterns for using the @modal.web_endpoint() decorator to create and deploy scalable HTTPS endpoints for your Python functions.

How does Modal handle data persistence?

Modal uses Volumes to provide persistent storage that can be shared across containers, allowing you to store model weights, datasets, and application logs securely.

Modal Cloud Computing

Name: Modal Cloud Computing
Author: BbgnsurfTech

byBbgnsurfTech

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Data Science & ML

Deploys and scales Python code in serverless containers with high-performance GPU support and automatic autoscaling.

This skill integrates the Modal platform into Claude's workflow, enabling the execution of compute-intensive Python tasks in the cloud with minimal configuration. It facilitates the deployment of machine learning models, parallel processing of large datasets, and the creation of GPU-accelerated web endpoints. By abstracting away infrastructure management, it allows users to define container environments, request specialized hardware like H100 or A100 GPUs, and manage persistent storage and secrets directly through Python code, making it an essential tool for data scientists and developers building AI-powered applications.

Key Features

01Seamless deployment of serverless web endpoints, APIs, and scheduled cron jobs

02Instant access to high-performance GPUs including H100, A100, and L40S for ML workloads

033 GitHub stars

04Declarative container image building with automated dependency management and system packages

05Persistent storage and data caching across function invocations using Modal Volumes

06Serverless Python execution with zero-config autoscaling from zero to thousands of containers

Use Cases

01Distributed batch processing of massive datasets using parallel function mapping

02Automating GPU-intensive model training and data engineering pipelines via scheduled jobs

03Scaling machine learning inference for LLMs, image generation, and embedding models

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add bbgnsurftech/claude-skills-collection modal

For use in Claude.ai and ChatGPT

Download Skill