Agent Skills › Companion-Inc/feynman › modal-compute

modal-compute

GitHub

通过 Modal CLI 在无状态服务器端运行受控的研究基准测试或模型复现任务，利用按需 GPU 算力执行训练脚本并保存结果，适用于需要突发计算且无需持久化状态的场景。

skills/modal-compute/SKILL.md Companion-Inc/feynman

Trigger Scenarios

需要运行研究基准测试或实验复制需要突发 GPU 算力进行模型训练

Install

npx skills add Companion-Inc/feynman --skill modal-compute -g -y

More Options

Use without installing

npx skills use Companion-Inc/feynman@modal-compute

指定 Agent (Claude Code)

npx skills add Companion-Inc/feynman --skill modal-compute -a claude-code -g -y

安装 repo 全部 skill

npx skills add Companion-Inc/feynman --all -g -y

预览 repo 内 skill

npx skills add Companion-Inc/feynman --list

SKILL.md

Frontmatter

{
    "name": "modal-compute",
    "description": "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."
}

Modal Compute

Use the modal CLI for bounded research experiments that need burst GPU compute. No pod lifecycle to manage; write a decorated Python script, run it, and save raw outputs back into the research artifact folder. Do not use this skill to deploy services or unrelated batch jobs.

Setup

pip install modal
modal setup

Commands

Command	Description
`modal run script.py`	Run one research experiment script on Modal
`modal run --detach script.py`	Run a long research experiment and record the returned app/run identifier
`modal shell --gpu a100`	Open an interactive GPU shell for research environment debugging

GPU types

T4, L4, A10G, L40S, A100, A100-80GB, H100, H200, B200

Multi-GPU: "H100:4" for 4x H100s.

Script pattern

import modal

app = modal.App("experiment")
image = modal.Image.debian_slim(python_version="3.11").pip_install("torch==2.8.0")

@app.function(gpu="A100", image=image, timeout=600)
def train():
    import torch
    # training code here

@app.local_entrypoint()
def main():
    train.remote()