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Provision and manage GPU pods on RunPod for explicitly chosen long-running research experiments. Use when a Feynman replication, benchmark, or dataset-heavy research run needs persistent GPU compute with SSH access.
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
SOC 職業分類に基づく
Search, read, and query research papers via Feynman's alphaXiv-backed alpha tools. Use when the user asks about academic papers, wants to find research on a topic, needs to read a specific paper, ask questions about a paper, inspect a paper's code repository, or manage paper annotations.
Execute research code inside isolated Docker containers for safe replication, experiments, and benchmarks. Use when the user selects Docker as the execution environment or asks to run code safely, in isolation, or in a sandbox.
Explain research, papers, or technical ideas in plain English with minimal jargon, concrete analogies, and clear takeaways. Use when the user says "ELI5 this", asks for a simple explanation of a paper or research result, wants jargon removed, or asks what something technically dense actually means.
Run explicitly chosen research benchmark or replication jobs on Modal's serverless infrastructure. Use when a Feynman research workflow needs burst remote GPU compute and the Modal CLI is available.
Preview Markdown, LaTeX, PDF, or code artifacts when preview commands are visible, or fall back to shell/browser tools. Use when the user wants to review a written artifact, export a report, or view a rendered document.
Use this for complex debugging, design review, deep refactors, or research-heavy coding work. The agent must inspect the repository first, plan before editing, make one coherent patch, and validate changes.
| name | runpod-compute |
| description | Provision and manage GPU pods on RunPod for explicitly chosen long-running research experiments. Use when a Feynman replication, benchmark, or dataset-heavy research run needs persistent GPU compute with SSH access. |
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.
brew install runpod/runpodctl/runpodctl # macOS
runpodctl config --apiKey=YOUR_KEY
| 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 root@<IP> -p <PORT> -i ~/.ssh/id_ed25519
Get connection details from runpodctl get pod <id>. Pods must expose port 22/tcp.
NVIDIA GeForce RTX 4090, NVIDIA RTX A6000, NVIDIA A40, NVIDIA A100 80GB PCIe, NVIDIA H100 80GB HBM3
command -v runpodctl