Agent skill · software engineering · aiskillstore
modal
Run Python code in the cloud with serverless containers, GPUs, and autoscaling. Use when deploying ML models, running batch processing jobs, scheduling compute-intensive tasks, or serving APIs that require GPU acceleration or dynamic scaling.
Why this skill is useful
Adds executable scripts for deploying ML models and managing GPU resources that aren't commonly known or documented elsewhere.
What it needs
Requires uv installed locally. Requires modal account access. About 5k tokens when loaded. Last updated 2026-08-07. 405 stars on the source repository.
What this skill does
Modal Overview Modal is a serverless platform for running Python code in the cloud with minimal configuration. Execute functions on powerful GPUs, scale automatically to thousands of containers, and pay only for compute used. Modal is particularly suited for AI/ML workloads, high-performance batch processing, scheduled jobs, GPU inference, and serverless APIs. Sign up for free at https://modal.com and receive $30/month in credits. When to Use This Skill Use Modal for: Deploying and serving ML models (LLMs, image generation, embedding models) Running GPU-accelerated computation (training, inference, rendering) Batch processing large datasets in parallel Scheduling compute-intensive jobs (daily data processing, model training) Building serverless APIs that need automatic scaling Scientific computing requiring distributed compute or specialized hardware Authentication and Setup Modal requires authentication via API token. Initial Setup This creates a token stored in ~/.modal.toml. The token authenticates all Modal operations. Verify Setup Run with: modal run script.py Core Capabilities Modal provides serverless Python execution through Functions that run in containers. Define compute requirements, dependencies, and scaling behavior declaratively. 1. Define Container Images Specify dependencies and environment for functions using Modal Images. Common patterns: Install Python packages: .uvpipinstall("pandas", "scikit-learn") Install system packages: .aptinstall("ffmpeg", "git") Use existing Docker images: modal.Image.fromregistry("nvidia/cuda:12.1.0-base") Add local code: .addlocalpythonsource("mymodule") See references/images.md for comprehensive image building documentation. 2. Create Functions Define functions that run in the cloud with the @app.function() decorator. Call functions: Run with: modal run script.py See references/functions.md for function patterns, deployment, and parameter handling. 3. Request GPUs Attach GPUs to functions for accelerated computation. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills aiskillstore/modal--b62852