learn-docker-k8s-game
Interactive AI-driven game for learning Docker, Linux, networking, and Kubernetes through story-driven challenges in your AI editor.
Best use case
learn-docker-k8s-game is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Interactive AI-driven game for learning Docker, Linux, networking, and Kubernetes through story-driven challenges in your AI editor.
Teams using learn-docker-k8s-game should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/learn-docker-k8s-game/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How learn-docker-k8s-game Compares
| Feature / Agent | learn-docker-k8s-game | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Interactive AI-driven game for learning Docker, Linux, networking, and Kubernetes through story-driven challenges in your AI editor.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Learn Docker & K8s Game
> Skill by [ara.so](https://ara.so) — Daily 2026 Skills collection.
An open-source, AI-driven interactive learning game that teaches Docker, Linux, networking, and Kubernetes through a story-driven simulation. No web app, no video courses — just you, your AI editor, a terminal, and the chaotic coffee startup NoCappuccino Inc.
---
## How It Works
The game runs entirely inside your AI editor. Markdown prompt files in this repo act as the game engine. When a user says "let's play," the AI reads `AGENTS.md` (or the editor-specific entry point), becomes "Sarah" (the mentor character), and walks the learner through real Docker/K8s scenarios using their actual terminal.
```
User says "let's play"
→ AI reads AGENTS.md + engine/rules.md + engine/narrator.md
→ AI becomes Sarah, the senior DevOps mentor
→ Story begins: Dave broke staging, fix it with containers
→ Lessons → Challenges → verify.sh → next chapter
```
---
## Installation
```bash
git clone https://github.com/ericboy0224/learn-docker-and-k8s.git
cd learn-docker-and-k8s
```
Open the directory in your AI editor, then type: **"let's play"**
### Prerequisites
| Requirement | Chapters | Notes |
|---|---|---|
| Docker | Ch 1–7 | Required |
| Docker Compose v2 | Ch 1–7 | Required |
| kubectl | Ch 6–7 | Optional |
| kind | Ch 6–7 | Optional |
| AI Editor | All | Claude Code, Cursor, Windsurf, Copilot, Cline, Codex, Gemini CLI |
### Environment Check
```bash
bash engine/environment-check.sh
```
This verifies Docker, Docker Compose, and optional Kubernetes tools are installed and running.
---
## Editor Entry Points
Each AI editor reads a different config file automatically:
| Editor | Entry File |
|---|---|
| Claude Code | `CLAUDE.md` |
| Cursor | `.cursorrules` |
| Windsurf | `.windsurfrules` |
| Cline / Roo Code | `.clinerules` |
| GitHub Copilot | `.github/copilot-instructions.md` |
| Gemini CLI | `GEMINI.md` |
| All others | `AGENTS.md` |
The AI editor will read the appropriate file on startup and load game context from `engine/` and `curriculum/`.
---
## Game Commands
Type these naturally in your AI editor chat:
```
/play — start or resume the game
/env-check — verify Docker/K8s setup before starting
/progress — view save file (.player/progress.yaml)
/hint — progressive hint (3 levels: nudge → direction → near-answer)
/verify — check your challenge solution
/next — advance to next lesson or challenge
/skip-to <N> — jump to chapter N (triggers a quiz gate)
/cleanup — remove all learn-* Docker resources safely
```
You can also speak naturally:
- `"I'm stuck on the port mapping"`
- `"check my work"`
- `"what does -p do again?"`
- `"skip to chapter 4"`
---
## Curriculum Overview
```
Ch1 → Ch2 → Ch3 → Ch4 → Ch5 → Ch6 → Ch7
```
| Chapter | Title | Core Skills |
|---|---|---|
| 1 | 📦 It Works on My Machine | containers, images, port mapping |
| 2 | 🏋️ The 2GB Espresso | multi-stage builds, layer caching, .dockerignore |
| 3 | 💾 The Vanishing Beans | volumes, bind mounts, persistence |
| 4 | 🔌 The Silent Grinder | DNS, bridge networks, isolation |
| 5 | 🎼 The Symphony of Steam | Docker Compose, health checks, secrets |
| 6 | ⎈ The Giant Roaster | Pods, Deployments, Services, self-healing |
| 7 | 🔥 The Great Latte Leak | rolling updates, Secrets, HPA, chaos triage |
Linux fundamentals (namespaces, cgroups, mounts) and networking (DNS, NAT, subnets, iptables) are taught contextually throughout — no dedicated lecture needed.
---
## Project Structure
```
learn-docker-and-k8s/
├── AGENTS.md # Universal AI entry point
├── CLAUDE.md # Claude Code entry + skill definitions
├── GEMINI.md # Gemini CLI entry point
├── .cursorrules # Cursor entry point
├── .clinerules # Cline/Roo Code entry point
├── .windsurfrules # Windsurf entry point
├── .github/
│ └── copilot-instructions.md # GitHub Copilot entry point
│
├── engine/
│ ├── rules.md # Teaching vs challenge mode rules
│ ├── narrator.md # Story, characters (Sarah/Dave/Marcus), tone
│ ├── validation.md # How AI should verify challenge solutions
│ ├── environment-check.sh # Pre-flight Docker/K8s check
│ └── cleanup.sh # Remove all learn-* resources
│
├── curriculum/
│ ├── ch01-containers/
│ │ ├── README.md # Chapter story + learning objectives
│ │ ├── lessons/ # 3 teaching lessons per chapter
│ │ ├── challenges/ # Hands-on tasks + verify.sh scripts
│ │ └── quiz.md # Skip-level assessment questions
│ ├── ch02-image-optimization/
│ ├── ch03-persistence/
│ ├── ch04-networking/
│ ├── ch05-compose/
│ ├── ch06-k8s-intro/
│ └── ch07-k8s-production/
│
└── .player/
└── progress.yaml # Save file — AI reads/writes this
```
---
## The Game Engine: Key Files
### `engine/rules.md`
Defines the two game modes:
- **Teaching mode** — AI explains concepts, answers questions freely
- **Challenge mode** — AI gives only progressive hints; never reveals the answer directly
### `engine/narrator.md`
Defines character voices and story tone:
- **Sarah** — friendly senior DevOps mentor, uses coffee metaphors
- **Dave** — CTO who breaks things and says "just restart it"
- **Marcus** — PM who sets impossible deadlines ("demo is at 3")
### `engine/validation.md`
Tells the AI how to run and interpret `verify.sh` scripts — it will execute them in the terminal and parse output to determine pass/fail.
### `.player/progress.yaml`
The save file. The AI manages this automatically:
```yaml
# .player/progress.yaml (example structure)
player:
name: ""
started_at: ""
current:
chapter: 1
lesson: 2
challenge: 1
completed:
chapters: []
challenges: []
hints_used: 0
```
---
## Challenge Verification Scripts
Each challenge has a `verify.sh` that the AI runs to check the learner's work:
```bash
# curriculum/ch01-containers/challenges/verify.sh (example pattern)
#!/bin/bash
set -e
echo "🔍 Checking Chapter 1 Challenge..."
# Check container is running
if docker ps --filter "name=learn-api" --filter "status=running" | grep -q "learn-api"; then
echo "✅ Container 'learn-api' is running"
else
echo "❌ Container 'learn-api' not found or not running"
exit 1
fi
# Check port mapping
if docker inspect learn-api | grep -q '"HostPort": "8080"'; then
echo "✅ Port 8080 is mapped correctly"
else
echo "❌ Port mapping incorrect — expected 8080:8080"
exit 1
fi
# Check endpoint responds
if curl -sf http://localhost:8080/health > /dev/null; then
echo "✅ API is responding on port 8080"
else
echo "❌ API not responding — check the container logs"
exit 1
fi
echo ""
echo "🎉 Challenge complete! Sarah is proud of you."
```
---
## Real Docker Commands Used in the Game
The game teaches these patterns through hands-on challenges:
```bash
# Ch1 — Run a container with port mapping
docker run -d --name learn-api -p 8080:8080 my-api-image
# Ch2 — Multi-stage build to reduce image size
docker build -t learn-api:optimized .
docker images learn-api # compare sizes
# Ch3 — Named volume for persistence
docker volume create learn-beans-data
docker run -d -v learn-beans-data:/app/data --name learn-db postgres:15
# Ch3 — Bind mount for development
docker run -d -v $(pwd)/src:/app/src --name learn-dev my-app
# Ch4 — Custom bridge network
docker network create learn-coffee-net
docker run -d --network learn-coffee-net --name learn-api my-api
docker run -d --network learn-coffee-net --name learn-db postgres:15
# Ch5 — Docker Compose
docker compose up -d
docker compose ps
docker compose logs -f api
docker compose down -v
# Ch6 — Kubernetes basics
kubectl apply -f curriculum/ch06-k8s-intro/manifests/
kubectl get pods -l app=learn-api
kubectl rollout status deployment/learn-api
# Ch7 — Horizontal Pod Autoscaler
kubectl apply -f curriculum/ch07-k8s-production/hpa.yaml
kubectl get hpa learn-api-hpa
```
---
## Example Docker Compose (Ch5 Pattern)
```yaml
# curriculum/ch05-compose/docker-compose.yml (game example)
version: "3.9"
services:
api:
build: ./api
container_name: learn-api
ports:
- "8080:8080"
environment:
- DATABASE_URL=postgresql://user:${DB_PASSWORD}@db:5432/nocappuccino
depends_on:
db:
condition: service_healthy
networks:
- learn-coffee-net
db:
image: postgres:15-alpine
container_name: learn-db
volumes:
- learn-beans-data:/var/lib/postgresql/data
environment:
- POSTGRES_PASSWORD=${DB_PASSWORD}
- POSTGRES_DB=nocappuccino
healthcheck:
test: ["CMD-SHELL", "pg_isready -U postgres"]
interval: 10s
timeout: 5s
retries: 5
networks:
- learn-coffee-net
volumes:
learn-beans-data:
networks:
learn-coffee-net:
driver: bridge
```
> Note: `DB_PASSWORD` is read from a `.env` file — never hardcoded.
---
## Example Kubernetes Manifests (Ch6–7 Pattern)
```yaml
# curriculum/ch06-k8s-intro/manifests/deployment.yaml
apiVersion: apps/v1
kind: Deployment
metadata:
name: learn-api
labels:
app: learn-api
spec:
replicas: 2
selector:
matchLabels:
app: learn-api
template:
metadata:
labels:
app: learn-api
spec:
containers:
- name: api
image: learn-api:latest
ports:
- containerPort: 8080
env:
- name: DB_PASSWORD
valueFrom:
secretKeyRef:
name: learn-db-secret
key: password
readinessProbe:
httpGet:
path: /health
port: 8080
initialDelaySeconds: 5
periodSeconds: 10
---
apiVersion: v1
kind: Service
metadata:
name: learn-api-svc
spec:
selector:
app: learn-api
ports:
- port: 80
targetPort: 8080
type: ClusterIP
```
---
## Cleanup
Remove all game-created Docker resources safely (uses `learn-` prefix convention):
```bash
bash engine/cleanup.sh
```
Or via AI editor:
```
/cleanup
```
This removes containers, images, volumes, and networks prefixed with `learn-`. It never touches your other Docker resources.
---
## Contributing
```bash
# Fork and clone
git clone https://github.com/YOUR_USERNAME/learn-docker-and-k8s.git
# Key contribution areas:
# 1. New challenges → curriculum/chXX-*/challenges/
# 2. New chapters → curriculum/ch08-helm/, ch09-argocd/, etc.
# 3. Fix verify.sh → make sure exit codes are correct (0=pass, 1=fail)
# 4. New AI editor → add entry point file + update AGENTS.md
# 5. Translations → keep technical terms (Docker, kubectl) in English
```
Each chapter follows this structure:
```
chXX-topic/
├── README.md # Story hook + learning objectives
├── lessons/
│ ├── 01-concept.md # Teaching content
│ ├── 02-concept.md
│ └── 03-concept.md
├── challenges/
│ ├── README.md # Challenge instructions
│ └── verify.sh # Auto-verification script (chmod +x)
└── quiz.md # 5 questions for /skip-to gate
```
---
## Troubleshooting
**"Docker daemon not running"**
```bash
# macOS
open -a Docker
# Linux
sudo systemctl start docker
sudo usermod -aG docker $USER # then log out and back in
```
**"verify.sh permission denied"**
```bash
chmod +x curriculum/ch01-containers/challenges/verify.sh
```
**"Port already in use"**
```bash
# Find what's using port 8080
lsof -i :8080
# or
docker ps # check if a previous learn-* container is still running
bash engine/cleanup.sh
```
**"AI isn't staying in character"**
Remind it: `"You are Sarah from NoCappuccino. Read engine/narrator.md and continue the game."`
**"Progress file is corrupted"**
```bash
rm .player/progress.yaml
# Then: /play to restart
```
**kubectl not found for Ch6–7**
```bash
# Install kind for local Kubernetes
brew install kind kubectl # macOS
# or
curl -Lo ./kind https://kind.sigs.k8s.io/dl/v0.22.0/kind-linux-amd64
chmod +x kind && sudo mv kind /usr/local/bin/kind
# Create a local cluster
kind create cluster --name learn-k8s
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