Feynman docker

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.

install

source · Clone the upstream repo

git clone https://github.com/getcompanion-ai/feynman

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/getcompanion-ai/feynman "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/docker" ~/.claude/skills/getcompanion-ai-feynman-docker && rm -rf "$T"

manifest: skills/docker/SKILL.md

source content

Docker Sandbox

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.

When to use

User selects "Docker Sandbox" as the execution environment in
```
/replicate
```
or
```
/autoresearch
```
Running untrusted code from a paper's repository
Experiments that install packages or modify system state
Any time the user asks to run something "safely" or "isolated"

How it works

Build or pull an appropriate base image for the research code
Mount the project directory into the container
Run experiment commands inside the container
Results write back to the mounted directory

Running commands in a container

For 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
"

Choosing the base image

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

Persistent containers

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>

Notes

The mounted workspace syncs results back to the host automatically
Containers are network-enabled by default — add
```
--network none
```
for full isolation
For GPU access, Docker must be configured with the NVIDIA Container Toolkit