# Docker

Execute research code inside isolated Docker containers for safe replication...

- Repository: https://github.com/Companion-Inc/feynman (10055 stars)
- Category: DevOps
- License: MIT
- Last updated: 2026-10-31
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add Companion-Inc/feynman --skill docker -g -a claude-code -y`
Codex: `npx skills add Companion-Inc/feynman --skill docker -g -a codex -y`
Cursor: `npx skills add Companion-Inc/feynman --skill docker -g -a cursor -y`
Gemini CLI: `npx skills add Companion-Inc/feynman --skill docker -g -a gemini-cli -y`

## 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.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# 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 research code safely or isolated for a Feynman workflow

## How it works

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

## Running commands in a container

For Python research code (most common):

```bash
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:

```bash
docker build -t feynman-experiment .
docker run --rm -v "$(pwd)/results":/workspace/results feynman-experiment
```

For GPU workloads:

```bash
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:

The rest: https://github.com/Companion-Inc/feynman/blob/HEAD/skills/docker/SKILL.md