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Podman

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BagelHole
podman

Manage containers using Podman, the daemonless container engine. Run rootless containers, create pods, manage images, and use Docker-compatible commands. Use when working with Podman or requiring rootless container operations.

Overview

PublisherBagelHole
RepositoryDevOps-Security-Agent-Skills
Skill namepodman
Stars
1.1K
Forks
161
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 BagelHole on GitHub. Read the source before you install it.

Installation

Install the Podman 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/BagelHole/DevOps-Security-Agent-Skills/tree/main/devops/containers/podman
  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/BagelHole/DevOps-Security-Agent-Skills.git /tmp/DevOps-Security-Agent-Skills
mkdir -p .claude/skills
cp -r /tmp/DevOps-Security-Agent-Skills/devops/containers/podman .claude/skills/podman
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Podman 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 Podman 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 Podman 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.

Podman

Run and manage containers without a daemon using Podman's rootless container engine.

When to Use This Skill

Use this skill when:

  • Running containers without root privileges
  • Managing containers on systems without Docker
  • Creating pod-based container groups
  • Using systemd for container management
  • Working in security-conscious environments

Prerequisites

  • Podman installed (4.x+)
  • For rootless: user namespaces enabled
  • Basic container concepts understanding

Key Differences from Docker

FeatureDockerPodman
ArchitectureClient-daemonDaemonless
Root requiredDefaultOptional (rootless)
Pod supportNoYes (Kubernetes-style)
Systemd integrationLimitedNative
Socketdocker.sockpodman.sock (optional)

Basic Commands

Container Operations

bash
# Run container (identical to Docker)
podman run -d --name webserver -p 8080:80 nginx

# List containers
podman ps -a

# Stop and remove
podman stop webserver
podman rm webserver

# Execute command
podman exec -it webserver /bin/sh

# View logs
podman logs -f webserver

Image Management

bash
# Pull image
podman pull docker.io/library/nginx:latest

# List images
podman images

# Build image
podman build -t myapp:latest .

# Push to registry
podman push myapp:latest registry.example.com/myapp:latest

# Remove image
podman rmi nginx:latest

Rootless Containers

Setup

bash
# Check user namespace support
cat /proc/sys/user/max_user_namespaces

# Enable if needed (as root)
echo "user.max_user_namespaces=28633" | sudo tee /etc/sysctl.d/userns.conf
sudo sysctl -p /etc/sysctl.d/userns.conf

# Configure subuid/subgid for user
sudo usermod --add-subuids 100000-165535 --add-subgids 100000-165535 $USER

# Verify
podman unshare cat /proc/self/uid_map

Running Rootless

bash
# Run as regular user (no sudo)
podman run -d --name myapp -p 8080:80 nginx

# Check user namespace mapping
podman unshare id

# Verify non-root
podman top myapp user

Port Considerations

bash
# Rootless cannot bind to ports < 1024 by default
# Use ports >= 1024
podman run -d -p 8080:80 nginx

# Or enable unprivileged ports (as root)
echo "net.ipv4.ip_unprivileged_port_start=80" | sudo tee /etc/sysctl.d/ports.conf
sudo sysctl -p /etc/sysctl.d/ports.conf

Pods

Creating Pods

bash
# Create pod
podman pod create --name mypod -p 8080:80 -p 5432:5432

# Add containers to pod
podman run -d --pod mypod --name web nginx
podman run -d --pod mypod --name db postgres:15

# List pods
podman pod ps

# Containers share network namespace
podman exec web curl localhost:5432

Pod Management

bash
# Start/stop pod (affects all containers)
podman pod start mypod
podman pod stop mypod

# Remove pod and containers
podman pod rm -f mypod

# View pod details
podman pod inspect mypod

# Generate Kubernetes YAML from pod
podman generate kube mypod > mypod.yaml

Systemd Integration

Generate Systemd Unit

bash
# Generate unit file for container
podman generate systemd --new --name myapp > ~/.config/systemd/user/container-myapp.service

# For pod
podman generate systemd --new --name mypod --files

# Reload systemd
systemctl --user daemon-reload

# Enable and start
systemctl --user enable --now container-myapp.service

Quadlet (Podman 4.4+)

ini
# ~/.config/containers/systemd/webapp.container
[Container]
Image=docker.io/library/nginx:latest
PublishPort=8080:80
Volume=webapp-data:/usr/share/nginx/html

[Service]
Restart=always

[Install]
WantedBy=default.target
bash
# Reload to generate service
systemctl --user daemon-reload

# Start the service
systemctl --user start webapp

Compose Compatibility

Using Podman Compose

bash
# Install podman-compose
pip install podman-compose

# Run compose file
podman-compose up -d

# Or use Docker Compose with Podman socket
systemctl --user enable --now podman.socket
export DOCKER_HOST=unix:///run/user/$UID/podman/podman.sock
docker-compose up -d

Native Podman Kube

bash
# Play Kubernetes YAML
podman kube play deployment.yaml

# Stop and remove
podman kube down deployment.yaml

Networking

Network Management

bash
# Create network
podman network create mynetwork

# Run on network
podman run -d --network mynetwork --name app myapp

# Connect container to network
podman network connect mynetwork existing-container

# List networks
podman network ls

# Inspect network
podman network inspect mynetwork

DNS Resolution

bash
# Containers on same network can resolve by name
podman run -d --network mynetwork --name db postgres:15
podman run -d --network mynetwork --name app \
  -e DATABASE_HOST=db myapp

Storage

Volume Management

bash
# Create volume
podman volume create mydata

# Use volume
podman run -d -v mydata:/data myapp

# List volumes
podman volume ls

# Inspect volume
podman volume inspect mydata

# Rootless volumes location
ls ~/.local/share/containers/storage/volumes/

Bind Mounts

bash
# Bind mount with SELinux label
podman run -v ./data:/app/data:Z myapp

# Z = private label (single container)
# z = shared label (multiple containers)

Registry Configuration

Configure Registries

bash
# Edit registries.conf
# ~/.config/containers/registries.conf
toml
unqualified-search-registries = ["docker.io", "quay.io"]

[[registry]]
prefix = "docker.io"
location = "docker.io"

[[registry.mirror]]
location = "mirror.gcr.io"

Authentication

bash
# Login to registry
podman login docker.io

# Login to private registry
podman login registry.example.com

# Credentials stored in
# ~/.config/containers/auth.json

Building Images

Buildah Integration

bash
# Podman uses Buildah for builds
podman build -t myapp:latest .

# Build with specific format
podman build --format docker -t myapp .

# Multi-stage build
podman build --target production -t myapp:prod .

Buildah Commands

bash
# Create container from scratch
buildah from scratch
buildah copy working-container ./app /app
buildah config --entrypoint '["/app/main"]' working-container
buildah commit working-container myapp:minimal

Common Issues

Issue: Permission Denied

Problem: Cannot access files in mounted volumes Solution: Use :Z or :z suffix for SELinux, or check ownership

Issue: Cannot Connect to Container

Problem: Port not accessible in rootless mode Solution: Use ports >= 1024 or configure unprivileged port start

Issue: Slow Image Pulls

Problem: Images download slowly Solution: Configure registry mirrors in registries.conf

Issue: Systemd Service Fails

Problem: Container doesn't start via systemd Solution: Enable lingering: loginctl enable-linger $USER

Best Practices

  • Use rootless mode for enhanced security
  • Leverage pods for related containers
  • Generate systemd units for production
  • Use Quadlet for declarative container services
  • Configure SELinux labels for bind mounts
  • Enable user lingering for persistent services
  • Use podman auto-update for automatic updates
  • Alias docker to podman for compatibility

Related Skills

Frequently asked questions

What does the Podman AI skill do?

Manage containers using Podman, the daemonless container engine. Run rootless containers, create pods, manage images, and use Docker-compatible commands. Use when working with Podman or requiring rootless container operations.

Why use Podman on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/BagelHole/DevOps-Security-Agent-Skills/tree/main/devops/containers/podman. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Podman?

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 Podman?

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

Is the Podman AI skill free?

Yes. It is published on GitHub by BagelHole 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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