Containerization logo

Containerization

Organization
RedHatProductSecurity
containerization

Enforce container security best practices for MCP servers, including OpenShift integration. Use when containerizing MCP servers or reviewing container security configurations for MCP deployments.

Overview

PublisherRedHatProductSecurity
Repositoryprodsec-skills
Skill namecontainerization
Stars
56
Forks
9
Bundled files
Instructions only
LicenseApache-2.0
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 RedHatProductSecurity on GitHub. Read the source before you install it.

Installation

Install the Containerization 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/RedHatProductSecurity/prodsec-skills/tree/main/module/skills/containerization
  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/RedHatProductSecurity/prodsec-skills.git /tmp/prodsec-skills
mkdir -p .claude/skills
cp -r /tmp/prodsec-skills/module/skills/containerization .claude/skills/containerization
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Containerization 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 Containerization 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 Containerization 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.

Containerization and OpenShift Integration for MCP Servers

Security Recommendation

MCP servers SHOULD run in containers, leveraging container orchestration platform security features (e.g., OpenShift SCCs) for a hardened-by-default posture.

Container Security Requirements

Run as Non-Root

The container MUST run as a non-root user. OpenShift's default Security Context Constraints (SCCs) enforce this automatically.

yaml
securityContext:
  runAsNonRoot: true
  runAsUser: 1000

Drop All Capabilities

Remove all Linux capabilities and add back only what is strictly needed:

yaml
securityContext:
  capabilities:
    drop: ["ALL"]

Read-Only Root Filesystem

Use a read-only root filesystem. Mount a tmpfs only for directories that require temporary writes:

yaml
securityContext:
  readOnlyRootFilesystem: true
volumeMounts:
  - name: tmp
    mountPath: /tmp
volumes:
  - name: tmp
    emptyDir:
      medium: Memory

Kernel Hardening

MechanismDetails
SELinuxEnabled by default in OpenShift; confines container processes
seccompApply seccomp profiles to restrict available system calls
yaml
securityContext:
  seccompProfile:
    type: RuntimeDefault
  seLinuxOptions:
    type: container_t

Network Policies

Use Kubernetes NetworkPolicies as a pod-level firewall to control all ingress and egress traffic:

yaml
apiVersion: networking.k8s.io/v1
kind: NetworkPolicy
metadata:
  name: mcp-server-policy
spec:
  podSelector:
    matchLabels:
      app: mcp-server
  policyTypes: ["Ingress", "Egress"]
  ingress:
    - from:
        - podSelector:
            matchLabels:
              app: mcp-client
      ports:
        - port: 8080
  egress:
    - to:
        - podSelector:
            matchLabels:
              app: auth-server
      ports:
        - port: 443

Base Image Selection

  • Use a Universal Base Image (UBI) from the official Red Hat Container Registry as the foundation for MCP server containers
  • Prefer ubi-minimal to reduce attack surface and installed package count
  • Use the most up-to-date image available
  • For Red Hat Catalog images, omit floating tags to ensure the latest image is pulled (exception: Konflux uses digest-based pinning with automated updates)
  • For non-Red Hat images, pin the version or digest to prevent pulling a tampered image
  • Remove non-essential packages and clean caches:
dockerfile
RUN microdnf upgrade -y && microdnf install -y <required> && microdnf remove -y <unnecessary> && microdnf clean all

Containerfile Linting

Use Hadolint to lint Containerfiles for best-practice violations. Run it in CI but verify findings manually -- do not trust it blindly.

Implementation Checklist

  • Base image is a Red Hat UBI (preferably ubi-minimal)
  • Non-Red Hat base images are pinned by version or digest
  • Non-essential packages are removed and caches cleaned
  • Run container as non-root user (runAsNonRoot: true)
  • Drop all Linux capabilities (drop: ["ALL"])
  • Use read-only root filesystem (readOnlyRootFilesystem: true)
  • allowPrivilegeEscalation: false is set (no-new-privileges)
  • Mount tmpfs only for directories needing temporary writes
  • Apply seccomp profile (RuntimeDefault or custom)
  • Ensure SELinux is enabled and container type is appropriate
  • Deploy Kubernetes NetworkPolicies controlling ingress and egress
  • Use OpenShift SCCs or equivalent to enforce security context defaults
  • Hadolint runs in CI on all Containerfiles
  • Scan container images for vulnerabilities before deployment

Frequently asked questions

What does the Containerization AI skill do?

Enforce container security best practices for MCP servers, including OpenShift integration. Use when containerizing MCP servers or reviewing container security configurations for MCP deployments.

Why use Containerization on TypingMind?

Because you install it once and use it with any model. Containerization 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 Containerization in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/RedHatProductSecurity/prodsec-skills/tree/main/module/skills/containerization. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Containerization?

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 Containerization?

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

Is the Containerization AI skill free?

Yes. It is published on GitHub by RedHatProductSecurity under the Apache-2.0 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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