Does it support web framework integration?

Absolutely, it provides code patterns for deploying FastAPI, Flask, and custom web servers using Modal's asgi_app and web_server decorators.

Can this skill help with GPU selection on Modal?

Yes, it includes detailed specifications, memory limits, and cost-per-hour comparisons for all Modal-supported GPUs, from T4s to H200s.

What is the modal-knowledge skill?

It is a specialized knowledge base for Claude that provides instant access to Modal.com platform documentation, best practices, and implementation patterns.

Can it assist with scaling and parallelism?

Yes, it provides implementation guidance on using modal.map, modal.starmap, and managing concurrency limits for massive parallel processing tasks.

Modal Platform Expert

Name: Modal Platform Expert
Author: JosiahSiegel

byJosiahSiegel

•

云基础设施

Provides comprehensive documentation and implementation guidance for the Modal.com serverless Python platform.

关于

This skill transforms Claude into a specialized consultant for Modal.com, offering deep expertise in serverless Python deployment. It covers everything from GPU hardware configuration (H100, A100, etc.) and optimized image building with uv to advanced architectural patterns like parallel processing and distributed storage. Whether you are building high-performance LLM inference endpoints, complex batch processing pipelines, or scheduled ETL jobs, this skill provides the specific syntax, performance best practices, and pricing data needed to leverage Modal's infrastructure effectively.

主要功能

Scaling strategies for high-concurrency parallel processing and LLM inference
Comprehensive GPU hardware specs and per-second pricing reference
Optimized Python image configuration using Debian slim and uv for faster builds
Guidance on distributed storage via Modal Volumes, Secrets, and Dicts
Implementation patterns for serverless functions, web endpoints, and scheduled jobs
7 GitHub stars

使用场景

Migrating local Python scripts to a managed cloud environment with zero-configuration infrastructure
Deploying high-performance LLM inference APIs with specific GPU acceleration requirements
Setting up scalable serverless batch processing pipelines for large-scale data engineering

关于

主要功能

Scaling strategies for high-concurrency parallel processing and LLM inference
Comprehensive GPU hardware specs and per-second pricing reference
Optimized Python image configuration using Debian slim and uv for faster builds
Guidance on distributed storage via Modal Volumes, Secrets, and Dicts
Implementation patterns for serverless functions, web endpoints, and scheduled jobs
7 GitHub stars

使用场景

Migrating local Python scripts to a managed cloud environment with zero-configuration infrastructure
Deploying high-performance LLM inference APIs with specific GPU acceleration requirements
Setting up scalable serverless batch processing pipelines for large-scale data engineering