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docker

Execute research code inside isolated Docker containers for safe replication, experiments, and benchmarks.

Skill by Companion-Inc

IndexedMIT★ 10,016
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About

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.

Use cases: devops, research, containers-kubernetes

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What this skill can do and what it asks for, from its SKILL.md. Checked Oct 25, 2026.

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  • codeload.github.com · found in install command

Files it touches

  • ~/.claude/skills/docker

Tools it pre-approves 1

From allowed-tools in SKILL.md: the agent may use these without asking you each time.

  • Bash(docker:*)

What's inside

Skill 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.
License
MIT
Allowed tools
Bash(docker:*)
Instructions
80 lines in SKILL.md · view on GitHub

Before installing

  • Pre-approves some shell commands

Files (1)

  • SKILL.md

SKILL.md (first lines)

# 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:

```bash
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:

```bash
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

From Companion-Inc/feynman, MIT.

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Stars
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Forks
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Last commit (this folder)
Jun 23, 2026 (4 months ago)
Last release
Oct 24, 2026 (3 days ago)

From GitHub (fetched Oct 24, 2026).

Details

License
MIT
Website
feynman.is
First seen
Oct 24, 2026
Last checked
Oct 24, 2026

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Cite this page

docker skill. Agent Mart. https://agentmart.dev/skills/companion-inc-docker (last updated 2026-10-24)