메뉴
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.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
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.
Bounded research experiment loop that tries hypotheses, measures benchmark evidence, keeps what works, and records what fails. Use when the user asks to optimize a research metric, run an experiment loop, improve model/retrieval/evaluation performance iteratively, or benchmark a research hypothesis.
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.
Inspect visible research run state, scheduled research follow-ups when available, and durable watch artifacts. Use when the user asks what's running for a research workflow or wants research-run status.
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.
| 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. |
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.
pip install modal
modal setup
| 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 |
T4, L4, A10G, L40S, A100, A100-80GB, H100, H200, B200
Multi-GPU: "H100:4" for 4x H100s.
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()
command -v modal