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Docker Expert

Checked
ai-engineer-agent·vlatest·cloud

Helps with AI, Docker as part of deploying and managing cloud infrastructure workflows.

Repository

Install on your platform

Showing Claude Code. One command installs this skill into any of the agents below.

  1. 1

    Run this in your project

    terminal
    $ npx skills add agenticaiengineer/ai-engineer-skills --skill docker-expert -a claude-code

  2. Uses the open-source skills CLI and needs Node.js. Add -g to install it for your user instead of this project.

What we know about this skill

Publisher

ai-engineer-agent

Taken from the source listing. We haven't verified who controls this account.

Install target

Live repository

Not pinned. Installing fetches whatever the repository holds at the time you run the command.

No human has reviewed this code. The checks below are a static scan of the files at one commit — nobody has run this skill or judged whether it works. A skill runs with whatever access you give your agent, so read the source before you install it.

Automated safety checks

scanned

This skill passed all 6 checks. We read its files at one commit and looked for the things that make a skill dangerous to run — not for whether it works.

  • Declares what it does: passed

    SKILL.md parses and declares a name and a description.

    SKILL.md exists, its frontmatter parses, and it declares a name and a description.

  • No undeclared network calls: passed

    Makes no network calls.

    Every host the skill dials — from its scripts, its config endpoints or the commands it tells the agent to run — is either well-known package and API infrastructure or declared in its own frontmatter. Links in documentation are not counted.

  • No obfuscated payloads: passed

    No encoded or obfuscated payloads.

    No base64, hex or compressed blob is decoded and then executed, and no encoded shell commands.

  • No credential access: passed

    Reads no keys, tokens, keychains or credential files.

    Neither the code nor the SKILL.md instructions read SSH keys, cloud credentials, keychains, token stores, browser cookie databases or dotfiles that hold secrets.

  • No pipe-to-shell installers: passed

    No pipe-to-shell or download-and-run installers.

    No `curl … | sh` pattern, in the code or in the instructions, that runs code fetched at run time.

  • Pinned to a commit: passed

    Scanned ai-engineer-agent/ai-engineer-skills at ae0b890c03.

    The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.

Commit
ae0b890c03
Manifest
skills/docker-expert/SKILL.md
Scanned
1 file, 57 lines
This is a static scan, not a review. It reads the skill's files; it does not run them, and it cannot tell you whether the skill is any good or whether the code does what its description says. A skill runs with whatever access you give your agent.
What each check looks for →

About this skill

What it does

The ai-engineer-agent-docker-expert skill allows AI agents to create, manage, and troubleshoot Docker containers. It can generate Dockerfiles based on requirements, build images from those files, and run containers with specified configurations. This skill simplifies deployment and ensures consistent environments for applications.

When to use it

  • You need to quickly deploy an application in a containerized environment.
  • You want to create reproducible development environments for your team.
  • You're troubleshooting issues within a Docker container or image.
  • You require assistance generating Dockerfiles from existing applications.

Key capabilities

  • Dockerfile generation
  • Image building
  • Container execution with configuration
  • Troubleshooting of Docker containers and images

Example prompts

  • "Create a Dockerfile for a Python Flask application."
  • "Build an image from this Dockerfile: [Dockerfile content]"
  • "Run a container based on the 'my-app' image, mapping port 80 to port 8080."
  • “Debug why my docker container is failing to start.”

Tips & gotchas

This skill assumes basic familiarity with Docker concepts. Providing clear and concise instructions will yield better results; vague requests may require clarification or iterative refinement of the generated output.