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Docker Management

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AlexAI-MCP
docker-management

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

Overview

PublisherAlexAI-MCP
Repositoryhermes-CCC
Skill namedocker-management
Stars
134
Forks
22
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 AlexAI-MCP 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/AlexAI-MCP/hermes-CCC/tree/master/skills/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/AlexAI-MCP/hermes-CCC.git /tmp/hermes-CCC
mkdir -p .claude/skills
cp -r /tmp/hermes-CCC/skills/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

Purpose

  • Use this skill to run, inspect, debug, and clean up Docker workloads.
  • Prefer it when the task involves containers, images, networks, volumes, or Compose stacks.
  • This skill covers both day-to-day lifecycle operations and practical debugging patterns.

Core Mental Model

  • Images are immutable build artifacts.
  • Containers are running or stopped instances of images.
  • Volumes persist data outside the container filesystem lifecycle.
  • Networks define service-to-service connectivity.
  • Compose describes multi-container applications declaratively.

Container Lifecycle

  • Create and run a container.
  • Start a stopped container.
  • Stop a running container.
  • Restart a container after config or dependency issues.
  • Remove a container when it is no longer needed.

Common Lifecycle Commands

bash
docker run -d --name myapp -p 8080:80 -v ./data:/data myimage
docker start myapp
docker stop myapp
docker restart myapp
docker rm myapp
  • -d runs the container in detached mode.
  • --name assigns a stable container name.
  • -p 8080:80 maps host port 8080 to container port 80.
  • -v ./data:/data mounts a host path into the container.

Run Command Pattern

bash
docker run -d --name myapp -p 8080:80 -v ./data:/data myimage

Use this pattern when you need:

  • background execution
  • a predictable container name
  • host-to-container port mapping
  • local bind-mount persistence

Inspect Running Containers

bash
docker ps
docker ps -a
  • docker ps shows running containers only.
  • docker ps -a shows all containers including exited ones.
  • Exited containers often reveal crash-loop or entrypoint problems.

Exec Into a Container

bash
docker exec -it myapp bash
  • Use this when the container image includes bash.
  • Fall back to sh for slimmer images:
bash
docker exec -it myapp sh

Useful checks inside a container:

  • verify file paths
  • inspect environment variables
  • test network reachability
  • check permissions
  • run the application command manually

View Logs

bash
docker logs -f myapp
  • Use -f to follow logs live.
  • Add --tail 100 to reduce noise when the log is very large.
  • Container logs are the fastest first stop for boot failures.

List Images, Volumes, and Networks

bash
docker images
docker volume ls
docker network ls
  • docker images helps identify build tags and old layers.
  • docker volume ls helps track stateful storage.
  • docker network ls is useful when debugging Compose service communication.

Build Images

bash
docker build -t myapp:latest .
  • Tag images explicitly.
  • Rebuild after Dockerfile changes, dependency changes, or copy-step changes.
  • Keep the build context small with a .dockerignore.

Dockerfile Baseline

dockerfile
FROM python:3.12-slim

WORKDIR /app
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt

COPY . .
CMD ["python", "app.py"]
  • Copy dependency manifests before source code to improve layer caching.
  • Use a narrow base image unless native toolchains are required.

Multi-Stage Dockerfile Pattern

dockerfile
FROM node:22 AS build
WORKDIR /src
COPY package*.json ./
RUN npm ci
COPY . .
RUN npm run build

FROM nginx:alpine
COPY --from=build /src/dist /usr/share/nginx/html
HEALTHCHECK CMD wget -qO- http://localhost/ || exit 1
  • Build dependencies stay in the builder stage.
  • The final stage is smaller and carries less attack surface.
  • Multi-stage builds are standard for frontend artifacts, Go binaries, and compiled assets.

Health Checks

  • Define health checks in the Dockerfile when the service has a meaningful liveness probe.
  • Health checks help Compose and orchestration layers reason about readiness.

Example:

dockerfile
HEALTHCHECK --interval=30s --timeout=5s --retries=3 CMD curl -fsS http://localhost:8080/health || exit 1
  • Prefer an explicit application health endpoint.
  • Avoid health checks that pass before dependencies are ready unless that is intentional.

Docker Compose Basics

bash
docker compose up -d
docker compose logs
docker compose down
  • docker compose up -d starts the stack in the background.
  • docker compose logs shows service logs across the stack.
  • docker compose down stops and removes the stack's containers and network.

Minimal Compose Example

yaml
services:
  app:
    build: .
    ports:
      - "8080:8080"
    environment:
      APP_ENV: development
    depends_on:
      - redis

  redis:
    image: redis:7-alpine
  • Compose is the default choice for local multi-service development.
  • Keep service names stable because they also become DNS names on the Compose network.

Inspect Container Metadata

bash
docker inspect myapp | jq '.[0].NetworkSettings'
  • Use docker inspect to view full JSON metadata.
  • Pipe through jq to isolate fields that matter.
  • Useful fields include mounts, networks, exposed ports, health status, and entrypoint configuration.

Resource Usage

bash
docker stats
  • This gives a live view of CPU, memory, network I/O, and block I/O.
  • Use it to spot memory leaks, unexpected idle CPU, or runaway workers.

Copy Files In and Out

bash
docker cp file.txt myapp:/app/
docker cp myapp:/app/output.log ./output.log
  • docker cp is useful for hot inspection, artifact extraction, and ad hoc debugging.
  • Prefer rebuilds for durable fixes, but docker cp can unblock investigation quickly.

Cleanup

bash
docker system prune -af
docker volume prune
  • docker system prune -af removes unused containers, networks, dangling images, and build cache aggressively.
  • docker volume prune removes unused volumes.
  • Be careful: pruning can destroy useful cached layers and orphaned but still valuable data.

Common Debugging Patterns

Override the Entrypoint

bash
docker run --rm -it --entrypoint sh myimage
  • Override the entrypoint when the default startup command exits too quickly.
  • This lets you inspect the filesystem and run the target process manually.

Check Environment Variables

bash
docker exec -it myapp env
  • Verify that expected secrets, ports, and mode flags are actually present.
  • Many container bugs reduce to missing or misspelled env vars.

Check the Effective Command

bash
docker inspect myapp | jq '.[0].Config.Entrypoint, .[0].Config.Cmd'
  • This helps distinguish build-time assumptions from runtime reality.
  • It is especially useful when wrapper scripts are involved.

Verify Mounts

bash
docker inspect myapp | jq '.[0].Mounts'
  • Bind-mount mistakes are common in local development.
  • Check source path, destination path, and access mode.

Test Networking

bash
docker exec -it myapp sh -c "wget -qO- http://redis:6379 || true"
  • Test container-to-container DNS and reachability from inside the network namespace.
  • For HTTP services, use curl or wget.
  • For TCP-only services, use the relevant client or a port probe.

Image Hygiene

  • Pin important base images when reproducibility matters.
  • Use slim runtime images where possible.
  • Minimize package-manager caches and temporary files.
  • Copy only what is needed.
  • Keep secrets out of the image entirely.

Security and Reliability Notes

  • Do not bake credentials into Dockerfiles.
  • Avoid running as root when the workload does not require it.
  • Use read-only filesystems or dropped capabilities where appropriate.
  • Scan images regularly with vulnerability tools.

Operational Guidance

  • Prefer Compose for local stacks rather than long hand-written docker run chains.
  • Use docker logs -f first when a service does not start cleanly.
  • Use docker inspect when port mappings or mounts behave unexpectedly.
  • Use health checks for services that depend on internal readiness.
  • Prune deliberately, not reflexively.

Summary

  • Manage container lifecycle with run, start, stop, restart, and rm.
  • Build images with docker build -t myapp:latest ..
  • Debug with docker exec -it myapp bash, docker logs -f myapp, docker inspect, and docker stats.
  • Operate stacks with docker compose up -d, docker compose logs, and docker compose down.
  • Clean carefully with docker system prune -af and docker volume prune.
  • Use multi-stage Dockerfiles and health checks to improve runtime quality and image hygiene.

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/AlexAI-MCP/hermes-CCC/tree/master/skills/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 AlexAI-MCP 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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