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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.
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.
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.
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 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 | docker |
| description | 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. |
| allowed-tools | Bash(docker:*) |
Run research code inside Docker containers while Feynman stays on the host. The container gets the project files, runs the commands, and results sync back.
/replicate or /autoresearchFor Python research code (most common):
docker run --rm -v "$(pwd)":/workspace -w /workspace python:3.11 bash -c "
pip install -r requirements.txt &&
python train.py
"
For projects with a Dockerfile:
docker build -t feynman-experiment .
docker run --rm -v "$(pwd)/results":/workspace/results feynman-experiment
For GPU workloads:
docker run --rm --gpus all -v "$(pwd)":/workspace -w /workspace pytorch/pytorch:latest bash -c "
pip install -r requirements.txt &&
python train.py
"
| Research type | Base image |
|---|---|
| Python ML/DL | pytorch/pytorch:latest or tensorflow/tensorflow:latest-gpu |
| Python general | python:3.11 |
| Node.js | node:20 |
| R / statistics | rocker/r-ver:4 |
| Julia | julia:1.10 |
| Multi-language | ubuntu:24.04 with manual installs |
For iterative experiments (like /autoresearch), create a named container instead of --rm. Choose a descriptive name based on the experiment:
docker create --name <name> -v "$(pwd)":/workspace -w /workspace python:3.11 tail -f /dev/null
docker start <name>
docker exec <name> bash -c "pip install -r requirements.txt"
docker exec <name> bash -c "python train.py"
This preserves installed packages across iterations. Clean up with:
docker stop <name> && docker rm <name>
--network none for full isolation