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Run GPU workloads on Modal's serverless infrastructure. Use when the user needs remote GPU compute for training, inference, benchmarks, or batch processing and Modal CLI is available.
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 the `alpha` CLI (alphaXiv-backed). 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.
Autonomous experiment loop that tries ideas, measures results, keeps what works, and discards what doesn't. Use when the user asks to optimize a metric, run an experiment loop, improve performance iteratively, or automate benchmarking.
Contribute changes to the Feynman repository itself. Use when the task is to add features, fix bugs, update prompts or skills, change install or release behavior, improve docs, or prepare a focused PR against this repo.
Run a thorough, source-heavy investigation on any topic. Use when the user asks for deep research, a comprehensive analysis, an in-depth report, or a multi-source investigation. Produces a cited research brief with provenance tracking.
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.
| name | modal-compute |
| description | Run GPU workloads on Modal's serverless infrastructure. Use when the user needs remote GPU compute for training, inference, benchmarks, or batch processing and Modal CLI is available. |
Use the modal CLI for serverless GPU workloads. No pod lifecycle to manage — write a decorated Python script and run it.
pip install modal
modal setup
| Command | Description |
|---|---|
modal run script.py | Run a script on Modal (ephemeral) |
modal run --detach script.py | Run detached (background) |
modal deploy script.py | Deploy persistently |
modal serve script.py | Serve with hot-reload (dev) |
modal shell --gpu a100 | Interactive shell with GPU |
modal app list | List deployed apps |
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 modalThis skill inherits the SeaBridgeAI /goal default protocol. Frame the work with a persistent goal, Definition of Done, validation plan, risks, dependencies, expected artifacts, and completion evidence. Do not claim completion until the DoD is validated or a hard blocker is documented.
Canonical protocol: C:\Users\adelm\SeaBridgeAI\everything-claude-code\protocols\GOAL_PROTOCOL.md