RunPod Compute
SkillDatabases & dataThis skill lets your AI rent and manage GPU machines on RunPod, so long research experiments can run on dedicated compute instead of your own computer. Once added, your AI can set up persistent GPU compute for things like Feynman replications, benchmarks, or dataset-heavy research runs, and reach those machines over SSH.
Available today. Use it from your connected AI after setup.
No other account needed.
After adding it, pick a research experiment that needs GPU compute, such as a Feynman replication or benchmark, and ask your AI to set up a RunPod machine for the run.
Then ask your AI: use the RunPod Compute skill
What your AI can do with it
- Rent GPU machines on RunPod for research experiments
- Keep long-running experiments going with persistent compute
- Create and manage rented GPU machines while a run is underway
- Connect to running experiments over SSH
- Support dataset-heavy runs, benchmarks, and Feynman replications
What this skill tells your AI
The instructions your AI receives, as published by companion-inc/feynman in skills/runpod-compute/SKILL.md and read by ahel’s review.
Use runpodctl CLI for persistent GPU pods with SSH access during a specific research run. Do not use this skill for provider administration outside that run; tie every pod to a replication, benchmark, or dataset-heavy research objective.
Setup
brew install runpod/runpodctl/runpodctl # macOS
runpodctl config --apiKey=YOUR_KEY
Commands
| Command | Description |
|---|---|
runpodctl create pod --gpuType "NVIDIA A100 80GB PCIe" --imageName "runpod/pytorch:2.4.0-py3.11-cuda12.4.1-devel-ubuntu22.04" --name experiment | Create a pod |
runpodctl get pod | List all pods |
runpodctl stop pod <id> | Stop (preserves volume) |
runpodctl start pod <id> | Resume a stopped pod |
runpodctl remove pod <id> | Terminate and delete |
runpodctl gpu list | List available GPU types and prices |
runpodctl send <file> | Transfer files to/from pods |
runpodctl receive <code> | Receive transferred files |
SSH access
ssh root@<IP> -p <PORT> -i ~/.ssh/id_ed25519
Get connection details from runpodctl get pod <id>. Pods must expose port 22/tcp.
GPU types
NVIDIA GeForce RTX 4090, NVIDIA RTX A6000, NVIDIA A40, NVIDIA A100 80GB PCIe, NVIDIA H100 80GB HBM3
When to use
- Long-running research experiments needing persistent state
- Large research datasets required by a replication or benchmark
- Multi-step research work with SSH access between iterations
- Always stop or remove pods after experiments
- Check availability:
command -v runpodctl
Signals
- GitHub stars
- 9k
- Forks
- 1k
- Last commit
- Sep 2026
Advanced
- Catalog kind
- skill
- Gateway key
runpod-compute- Source
- github.com/companion-inc/feynman