devcontainer-setup

Generate DevContainer configurations with Dockerfile and devcontainer.json for Python 3.13 environments.

Updated Jan 23, 2026
One-click install
npx skills add https://github.com/datorresb/vibecoding-starter --skill devcontainer-setup
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: devcontainer-setup
Source: https://github.com/datorresb/vibecoding-starter/tree/main/.claude/skills/devops/devcontainer-setup
Command: npx skills add https://github.com/datorresb/vibecoding-starter --skill devcontainer-setup

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill provides a standardized DevContainer setup to ensure consistent Python 3.13 development environments across teams, enabling reproducible builds, smoother onboarding, and fewer “works on my machine” issues.

Core Features & Use Cases

  • Environment as Code: Define development environments in code and version control, guaranteeing consistency across machines.
  • Step-by-step, configurable setup: Supports Default (all options on) or Advanced (customizable) workflows to generate Dockerfile, devcontainer.json, optional features (GitHub CLI, Docker-in-Docker, Node) and post-create automation.
  • Use Case: Use when initializing a new project, adding DevContainer support, or standardizing tool versions for a team, ensuring repeatable setup for every contributor.

Quick Start

Choose Default or Advanced, then run Steps 1-7 exactly as described in this skill to produce a ready-to-run DevContainer scaffold in your project root.

Frequently Asked Questions about devcontainer-setup

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I standardize Python DevContainers across my development team?

Standardize Python DevContainers by generating environment-as-code configurations, including Dockerfile and devcontainer.json, ensuring reproducible builds and consistent setups across all machines.

What is the process for setting up a DevContainer for a new Python project?

Setting up a DevContainer involves following a strict step-by-step process to scaffold a Dockerfile, devcontainer.json, optional features, and post-create automation directly in your project root.

Can I customize optional features like Docker-in-Docker when creating a devcontainer.json?

Yes, you can customize optional features like Docker-in-Docker, GitHub CLI, and Node by selecting the Advanced workflow, which allows configurable setup instead of the Default all-options-on mode.

Does this DevContainer setup handle secrets and post-create automation?

Yes, the DevContainer setup handles secrets and includes post-create automation, enforcing a strict configuration process that records changes without introducing unlisted modifications to your environment.

What is the best way to fix "works on my machine" issues for Python developers?

Fix works on my machine issues by using DevContainer configurations to define development environments in code and version control, guaranteeing Python 3.13 environment consistency across contributors.

When should I not use an automated DevContainer setup for my project?

Avoid automated DevContainer setup when your project requires changes outside the enforced step-by-step process, as this approach records configurations strictly without introducing unlisted modifications.