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runpod-serverless

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Deploy GPU RunPod Serverless workers fast.

Software Engineering#automation#deployment#serverless#docker#gpu#runpod#hub
Authorprofzeller
Version1.0.0
Installs0

System Documentation

What problem does it solve?

This Skill streamlines the creation and deployment of RunPod Serverless GPU workers. It guides you through building the required repository structure, configuring hub.json and tests.json, and publishing a release to RunPod Hub, reducing manual setup and configuration errors.

Core Features & Use Cases

  • Scaffold serverless projects: Generate the standard RunPod Serverless directory layout with handler.py, Dockerfile, hub.json, tests.json, and README.
  • Hub-ready deployments: Prepare GitHub repos and releases so your worker can be deployed to RunPod Hub with minimal effort.
  • Repeatable, safe deployments: Ensure consistent, reproducible serverless workers across projects and teams.
  • Use case: A data science team wants a GPU-backed serverless endpoint for batched model inferences without managing servers.

Quick Start

Provide the worker name, GitHub username, worker description, category, base image, GPU requirements, VRAM, and Python dependencies, then ask Claude to scaffold and publish the repository for RunPod Serverless.

Dependency Matrix

Required Modules

None required

Components

Standard package

💻 Claude Code Installation

Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.

Please help me install this Skill:
Name: runpod-serverless
Download link: https://github.com/profzeller/claude-skills/archive/main.zip#runpod-serverless

Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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