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Docker Management

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Tommy-yw
docker-management

Manage Docker containers, images, volumes, networks, and Compose stacks — lifecycle ops, debugging, cleanup, and Dockerfile optimization.

Overview

PublisherTommy-yw
RepositoryRunbookHermes
Skill namedocker-management
Stars
546
Forks
41
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by Tommy-yw on GitHub. Read the source before you install it.

Installation

Install the Docker Management AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/Tommy-yw/RunbookHermes/tree/main/optional-skills/devops/docker-management
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/Tommy-yw/RunbookHermes.git /tmp/RunbookHermes
mkdir -p .claude/skills
cp -r /tmp/RunbookHermes/optional-skills/devops/docker-management .claude/skills/docker-management
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Docker Management in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Docker Management on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Docker Management is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Docker Management

Manage Docker containers, images, volumes, networks, and Compose stacks using standard Docker CLI commands. No additional dependencies beyond Docker itself.

When to Use

  • Run, stop, restart, remove, or inspect containers
  • Build, pull, push, tag, or clean up Docker images
  • Work with Docker Compose (multi-service stacks)
  • Manage volumes or networks
  • Debug a crashing container or analyze logs
  • Check Docker disk usage or free up space
  • Review or optimize a Dockerfile

Prerequisites

  • Docker Engine installed and running
  • User added to the docker group (or use sudo)
  • Docker Compose v2 (included with modern Docker installations)

Quick check:

bash
docker --version && docker compose version

Quick Reference

TaskCommand
Run container (background)docker run -d --name NAME IMAGE
Stop + removedocker stop NAME && docker rm NAME
View logs (follow)docker logs --tail 50 -f NAME
Shell into containerdocker exec -it NAME /bin/sh
List all containersdocker ps -a
Build imagedocker build -t TAG .
Compose updocker compose up -d
Compose downdocker compose down
Disk usagedocker system df
Cleanup danglingdocker image prune && docker container prune

Procedure

1. Identify the domain

Figure out which area the request falls into:

  • Container lifecycle → run, stop, start, restart, rm, pause/unpause
  • Container interaction → exec, cp, logs, inspect, stats
  • Image management → build, pull, push, tag, rmi, save/load
  • Docker Compose → up, down, ps, logs, exec, build, config
  • Volumes & networks → create, inspect, rm, prune, connect
  • Troubleshooting → log analysis, exit codes, resource issues

2. Container operations

Run a new container:

bash
# Detached service with port mapping
docker run -d --name web -p 8080:80 nginx

# With environment variables
docker run -d -e POSTGRES_PASSWORD=secret -e POSTGRES_DB=mydb --name db postgres:16

# With persistent data (named volume)
docker run -d -v pgdata:/var/lib/postgresql/data --name db postgres:16

# For development (bind mount source code)
docker run -d -v $(pwd)/src:/app/src -p 3000:3000 --name dev my-app

# Interactive debugging (auto-remove on exit)
docker run -it --rm ubuntu:22.04 /bin/bash

# With resource limits and restart policy
docker run -d --memory=512m --cpus=1.5 --restart=unless-stopped --name app my-app

Key flags: -d detached, -it interactive+tty, --rm auto-remove, -p port (host:container), -e env var, -v volume, --name name, --restart restart policy.

Manage running containers:

bash
docker ps                        # running containers
docker ps -a                     # all (including stopped)
docker stop NAME                 # graceful stop
docker start NAME                # start stopped container
docker restart NAME              # stop + start
docker rm NAME                   # remove stopped container
docker rm -f NAME                # force remove running container
docker container prune           # remove ALL stopped containers

Interact with containers:

bash
docker exec -it NAME /bin/sh          # shell access (use /bin/bash if available)
docker exec NAME env                   # view environment variables
docker exec -u root NAME apt update    # run as specific user
docker logs --tail 100 -f NAME         # follow last 100 lines
docker logs --since 2h NAME            # logs from last 2 hours
docker cp NAME:/path/file ./local      # copy file from container
docker cp ./file NAME:/path/           # copy file to container
docker inspect NAME                    # full container details (JSON)
docker stats --no-stream               # resource usage snapshot
docker top NAME                        # running processes

3. Image management

bash
# Build
docker build -t my-app:latest .
docker build -t my-app:prod -f Dockerfile.prod .
docker build --no-cache -t my-app .              # clean rebuild
DOCKER_BUILDKIT=1 docker build -t my-app .       # faster with BuildKit

# Pull and push
docker pull node:20-alpine
docker login ghcr.io
docker tag my-app:latest registry/my-app:v1.0
docker push registry/my-app:v1.0

# Inspect
docker images                          # list local images
docker history IMAGE                   # see layers
docker inspect IMAGE                   # full details

# Cleanup
docker image prune                     # remove dangling (untagged) images
docker image prune -a                  # remove ALL unused images (careful!)
docker image prune -a --filter "until=168h"   # unused images older than 7 days

4. Docker Compose

bash
# Start/stop
docker compose up -d                   # start all services detached
docker compose up -d --build           # rebuild images before starting
docker compose down                    # stop and remove containers
docker compose down -v                 # also remove volumes (DESTROYS DATA)

# Monitoring
docker compose ps                      # list services
docker compose logs -f api             # follow logs for specific service
docker compose logs --tail 50          # last 50 lines all services

# Interaction
docker compose exec api /bin/sh        # shell into running service
docker compose run --rm api npm test   # one-off command (new container)
docker compose restart api             # restart specific service

# Validation
docker compose config                  # validate and view resolved config

Minimal compose.yml example:

yaml
services:
  api:
    build: .
    ports:
      - "3000:3000"
    environment:
      - DATABASE_URL=postgres://user:pass@db:5432/mydb
    depends_on:
      db:
        condition: service_healthy

  db:
    image: postgres:16-alpine
    environment:
      POSTGRES_USER: user
      POSTGRES_PASSWORD: pass
      POSTGRES_DB: mydb
    volumes:
      - pgdata:/var/lib/postgresql/data
    healthcheck:
      test: ["CMD-SHELL", "pg_isready -U user"]
      interval: 10s
      timeout: 5s
      retries: 5

volumes:
  pgdata:

5. Volumes and networks

bash
# Volumes
docker volume ls                       # list volumes
docker volume create mydata            # create named volume
docker volume inspect mydata           # details (mount point, etc.)
docker volume rm mydata                # remove (fails if in use)
docker volume prune                    # remove unused volumes

# Networks
docker network ls                      # list networks
docker network create mynet            # create bridge network
docker network inspect mynet           # details (connected containers)
docker network connect mynet NAME      # attach container to network
docker network disconnect mynet NAME   # detach container
docker network rm mynet                # remove network
docker network prune                   # remove unused networks

6. Disk usage and cleanup

Always start with a diagnostic before cleaning:

bash
# Check what's using space
docker system df                       # summary
docker system df -v                    # detailed breakdown

# Targeted cleanup (safe)
docker container prune                 # stopped containers
docker image prune                     # dangling images
docker volume prune                    # unused volumes
docker network prune                   # unused networks

# Aggressive cleanup (confirm with user first!)
docker system prune                    # containers + images + networks
docker system prune -a                 # also unused images
docker system prune -a --volumes       # EVERYTHING — named volumes too

Warning: Never run docker system prune -a --volumes without confirming with the user. This removes named volumes with potentially important data.

Pitfalls

ProblemCauseFix
Container exits immediatelyMain process finished or crashedCheck docker logs NAME, try docker run -it --entrypoint /bin/sh IMAGE
"port is already allocated"Another process using that portdocker ps or lsof -i :PORT to find it
"no space left on device"Docker disk fulldocker system df then targeted prune
Can't connect to containerApp binds to 127.0.0.1 inside containerApp must bind to 0.0.0.0, check -p mapping
Permission denied on volumeUID/GID mismatch host vs containerUse --user $(id -u):$(id -g) or fix permissions
Compose services can't reach each otherWrong network or service nameServices use service name as hostname, check docker compose config
Build cache not workingLayer order wrong in DockerfilePut rarely-changing layers first (deps before source code)
Image too largeNo multi-stage build, no .dockerignoreUse multi-stage builds, add .dockerignore

Verification

After any Docker operation, verify the result:

  • Container started? → docker ps (check status is "Up")
  • Logs clean? → docker logs --tail 20 NAME (no errors)
  • Port accessible? → curl -s http://localhost:PORT or docker port NAME
  • Image built? → docker images | grep TAG
  • Compose stack healthy? → docker compose ps (all services "running" or "healthy")
  • Disk freed? → docker system df (compare before/after)

Dockerfile Optimization Tips

When reviewing or creating a Dockerfile, suggest these improvements:

  1. Multi-stage builds — separate build environment from runtime to reduce final image size
  2. Layer ordering — put dependencies before source code so changes don't invalidate cached layers
  3. Combine RUN commands — fewer layers, smaller image
  4. Use .dockerignore — exclude node_modules, .git, __pycache__, etc.
  5. Pin base image versions — node:20-alpine not node:latest
  6. Run as non-root — add USER instruction for security
  7. Use slim/alpine bases — python:3.12-slim not python:3.12

Frequently asked questions

What does the Docker Management AI skill do?

Manage Docker containers, images, volumes, networks, and Compose stacks — lifecycle ops, debugging, cleanup, and Dockerfile optimization.

Why use Docker Management on TypingMind?

Because you install it once and use it with any model. Docker Management is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Docker Management in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Tommy-yw/RunbookHermes/tree/main/optional-skills/devops/docker-management. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Docker Management?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Docker Management?

As many as you like. As long as a model supports skills, you can use Docker Management with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Docker Management AI skill free?

Yes. It is published on GitHub by Tommy-yw under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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