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Devops Cloud

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travisjneuman
devops-cloud

DevOps, cloud infrastructure, and platform engineering. Use when working with AWS, GCP, Azure, Kubernetes, Terraform, CI/CD pipelines, or infrastructure as code.

Overview

Publishertravisjneuman
Repository.claude
Skill namedevops-cloud
Stars
98
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 travisjneuman on GitHub. Read the source before you install it.

Installation

Install the Devops Cloud 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.
  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/travisjneuman/.claude.git /tmp/.claude
mkdir -p .claude/skills
cp -r /tmp/.claude/skills/devops-cloud .claude/skills/devops-cloud
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Devops Cloud 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 Devops Cloud 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 Devops Cloud 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.

DevOps & Cloud Infrastructure

Comprehensive guide for cloud platforms, infrastructure as code, and DevOps practices.

Cloud Platforms

AWS (Amazon Web Services)

Compute:

yaml
# EC2 Instance Types
General Purpose: t3, m6i, m7g (ARM)
Compute Optimized: c6i, c7g
Memory Optimized: r6i, x2idn
Storage Optimized: i3, d3

# Auto Scaling
aws autoscaling create-auto-scaling-group \
  --auto-scaling-group-name my-asg \
  --launch-template LaunchTemplateId=lt-xxx \
  --min-size 1 --max-size 10 --desired-capacity 2 \
  --vpc-zone-identifier "subnet-xxx,subnet-yyy"

Serverless:

typescript
// Lambda with TypeScript
import { APIGatewayProxyHandler } from "aws-lambda";

export const handler: APIGatewayProxyHandler = async (event) => {
  return {
    statusCode: 200,
    body: JSON.stringify({ message: "Success" }),
  };
};

Storage:

ServiceUse CaseDurability
S3Object storage99.999999999%
EBSBlock storage (EC2)99.999%
EFSShared file system99.999999999%
FSxWindows/Lustre99.999999999%

GCP (Google Cloud Platform)

Key Services:

bash
# GKE cluster
gcloud container clusters create my-cluster \
  --zone us-central1-a \
  --num-nodes 3 \
  --machine-type e2-medium \
  --enable-autoscaling --min-nodes 1 --max-nodes 10

# Cloud Run (serverless containers)
gcloud run deploy my-service \
  --image gcr.io/project/image:tag \
  --platform managed \
  --region us-central1 \
  --allow-unauthenticated

Azure

Key Services:

bash
# AKS cluster
az aks create \
  --resource-group myRG \
  --name myAKS \
  --node-count 3 \
  --enable-addons monitoring \
  --generate-ssh-keys

# Azure Functions
func init MyFunctionApp --typescript
func new --name HttpTrigger --template "HTTP trigger"

Kubernetes

Core Concepts

yaml
# Deployment
apiVersion: apps/v1
kind: Deployment
metadata:
  name: my-app
  labels:
    app: my-app
spec:
  replicas: 3
  selector:
    matchLabels:
      app: my-app
  template:
    metadata:
      labels:
        app: my-app
    spec:
      containers:
        - name: my-app
          image: my-app:1.0.0
          ports:
            - containerPort: 8080
          resources:
            requests:
              memory: "128Mi"
              cpu: "250m"
            limits:
              memory: "256Mi"
              cpu: "500m"
          livenessProbe:
            httpGet:
              path: /health
              port: 8080
            initialDelaySeconds: 30
            periodSeconds: 10
          readinessProbe:
            httpGet:
              path: /ready
              port: 8080
            initialDelaySeconds: 5
            periodSeconds: 5

Service Types

yaml
# ClusterIP (internal)
apiVersion: v1
kind: Service
metadata:
  name: my-service
spec:
  type: ClusterIP
  selector:
    app: my-app
  ports:
    - port: 80
      targetPort: 8080

---
# LoadBalancer (external)
apiVersion: v1
kind: Service
metadata:
  name: my-service-lb
spec:
  type: LoadBalancer
  selector:
    app: my-app
  ports:
    - port: 80
      targetPort: 8080

Ingress with TLS

yaml
apiVersion: networking.k8s.io/v1
kind: Ingress
metadata:
  name: my-ingress
  annotations:
    kubernetes.io/ingress.class: nginx
    cert-manager.io/cluster-issuer: letsencrypt-prod
spec:
  tls:
    - hosts:
        - app.example.com
      secretName: app-tls
  rules:
    - host: app.example.com
      http:
        paths:
          - path: /
            pathType: Prefix
            backend:
              service:
                name: my-service
                port:
                  number: 80

Helm Charts

yaml
# values.yaml
replicaCount: 3
image:
  repository: my-app
  tag: "1.0.0"
  pullPolicy: IfNotPresent
service:
  type: ClusterIP
  port: 80
ingress:
  enabled: true
  hosts:
    - host: app.example.com
      paths: ["/"]
resources:
  limits:
    cpu: 500m
    memory: 256Mi
  requests:
    cpu: 250m
    memory: 128Mi

Terraform

AWS Infrastructure

hcl
# main.tf
terraform {
  required_providers {
    aws = {
      source  = "hashicorp/aws"
      version = "~> 5.0"
    }
  }

  backend "s3" {
    bucket = "my-terraform-state"
    key    = "prod/terraform.tfstate"
    region = "us-east-1"
  }
}

provider "aws" {
  region = var.aws_region
}

# VPC
module "vpc" {
  source  = "terraform-aws-modules/vpc/aws"
  version = "5.0.0"

  name = "my-vpc"
  cidr = "10.0.0.0/16"

  azs             = ["us-east-1a", "us-east-1b", "us-east-1c"]
  private_subnets = ["10.0.1.0/24", "10.0.2.0/24", "10.0.3.0/24"]
  public_subnets  = ["10.0.101.0/24", "10.0.102.0/24", "10.0.103.0/24"]

  enable_nat_gateway = true
  single_nat_gateway = false
}

# EKS Cluster
module "eks" {
  source  = "terraform-aws-modules/eks/aws"
  version = "19.0.0"

  cluster_name    = "my-cluster"
  cluster_version = "1.28"

  vpc_id     = module.vpc.vpc_id
  subnet_ids = module.vpc.private_subnets

  eks_managed_node_groups = {
    default = {
      min_size     = 1
      max_size     = 10
      desired_size = 3
      instance_types = ["t3.medium"]
    }
  }
}

Variables and Outputs

hcl
# variables.tf
variable "aws_region" {
  description = "AWS region"
  type        = string
  default     = "us-east-1"
}

variable "environment" {
  description = "Environment name"
  type        = string
  validation {
    condition     = contains(["dev", "staging", "prod"], var.environment)
    error_message = "Environment must be dev, staging, or prod."
  }
}

# outputs.tf
output "cluster_endpoint" {
  description = "EKS cluster endpoint"
  value       = module.eks.cluster_endpoint
}

output "cluster_name" {
  description = "EKS cluster name"
  value       = module.eks.cluster_name
}

CI/CD Pipelines

GitHub Actions

yaml
# .github/workflows/deploy.yml
name: Deploy

on:
  push:
    branches: [main]
  pull_request:
    branches: [main]

env:
  REGISTRY: ghcr.io
  IMAGE_NAME: ${{ github.repository }}

jobs:
  test:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - uses: actions/setup-node@v4
        with:
          node-version: "20"
          cache: "npm"
      - run: npm ci
      - run: npm test
      - run: npm run build

  build-and-push:
    needs: test
    runs-on: ubuntu-latest
    if: github.event_name == 'push'
    permissions:
      contents: read
      packages: write
    steps:
      - uses: actions/checkout@v4

      - name: Log in to registry
        uses: docker/login-action@v3
        with:
          registry: ${{ env.REGISTRY }}
          username: ${{ github.actor }}
          password: ${{ secrets.GITHUB_TOKEN }}

      - name: Build and push
        uses: docker/build-push-action@v5
        with:
          context: .
          push: true
          tags: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}:${{ github.sha }}

  deploy:
    needs: build-and-push
    runs-on: ubuntu-latest
    if: github.ref == 'refs/heads/main'
    steps:
      - uses: actions/checkout@v4

      - name: Deploy to Kubernetes
        uses: azure/k8s-deploy@v4
        with:
          manifests: k8s/
          images: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}:${{ github.sha }}

GitLab CI

yaml
# .gitlab-ci.yml
stages:
  - test
  - build
  - deploy

variables:
  DOCKER_IMAGE: $CI_REGISTRY_IMAGE:$CI_COMMIT_SHA

test:
  stage: test
  image: node:20
  script:
    - npm ci
    - npm test
    - npm run build
  cache:
    paths:
      - node_modules/

build:
  stage: build
  image: docker:24
  services:
    - docker:24-dind
  script:
    - docker login -u $CI_REGISTRY_USER -p $CI_REGISTRY_PASSWORD $CI_REGISTRY
    - docker build -t $DOCKER_IMAGE .
    - docker push $DOCKER_IMAGE
  only:
    - main

deploy:
  stage: deploy
  image: bitnami/kubectl:latest
  script:
    - kubectl set image deployment/my-app my-app=$DOCKER_IMAGE
  only:
    - main
  environment:
    name: production

Docker

Multi-Stage Builds

dockerfile
# Build stage
FROM node:20-alpine AS builder
WORKDIR /app
COPY package*.json ./
RUN npm ci
COPY . .
RUN npm run build

# Production stage
FROM node:20-alpine AS production
WORKDIR /app
RUN addgroup -g 1001 -S nodejs && \
    adduser -S nextjs -u 1001
COPY --from=builder --chown=nextjs:nodejs /app/dist ./dist
COPY --from=builder --chown=nextjs:nodejs /app/node_modules ./node_modules
USER nextjs
EXPOSE 3000
CMD ["node", "dist/main.js"]

Docker Compose

yaml
# docker-compose.yml
version: "3.8"

services:
  app:
    build: .
    ports:
      - "3000:3000"
    environment:
      - DATABASE_URL=postgres://user:pass@db:5432/mydb
      - REDIS_URL=redis://redis:6379
    depends_on:
      db:
        condition: service_healthy
      redis:
        condition: service_started
    healthcheck:
      test: ["CMD", "curl", "-f", "http://localhost:3000/health"]
      interval: 30s
      timeout: 10s
      retries: 3

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

  redis:
    image: redis:7-alpine
    volumes:
      - redis_data:/data

volumes:
  postgres_data:
  redis_data:

Monitoring & Observability

Prometheus + Grafana

yaml
# prometheus.yml
global:
  scrape_interval: 15s

scrape_configs:
  - job_name: "kubernetes-pods"
    kubernetes_sd_configs:
      - role: pod
    relabel_configs:
      - source_labels: [__meta_kubernetes_pod_annotation_prometheus_io_scrape]
        action: keep
        regex: true

Application Metrics

typescript
// metrics.ts
import { Counter, Histogram, Registry } from "prom-client";

export const register = new Registry();

export const httpRequestsTotal = new Counter({
  name: "http_requests_total",
  help: "Total HTTP requests",
  labelNames: ["method", "path", "status"],
  registers: [register],
});

export const httpRequestDuration = new Histogram({
  name: "http_request_duration_seconds",
  help: "HTTP request duration",
  labelNames: ["method", "path"],
  buckets: [0.1, 0.5, 1, 2, 5],
  registers: [register],
});

Security Best Practices

Secrets Management

bash
# AWS Secrets Manager
aws secretsmanager create-secret \
  --name prod/db-credentials \
  --secret-string '{"username":"admin","password":"xxx"}'

# Kubernetes Secrets (external-secrets)
apiVersion: external-secrets.io/v1beta1
kind: ExternalSecret
metadata:
  name: db-credentials
spec:
  secretStoreRef:
    kind: ClusterSecretStore
    name: aws-secrets
  target:
    name: db-credentials
  data:
    - secretKey: username
      remoteRef:
        key: prod/db-credentials
        property: username

Network Policies

yaml
apiVersion: networking.k8s.io/v1
kind: NetworkPolicy
metadata:
  name: api-network-policy
spec:
  podSelector:
    matchLabels:
      app: api
  policyTypes:
    - Ingress
    - Egress
  ingress:
    - from:
        - podSelector:
            matchLabels:
              app: frontend
      ports:
        - protocol: TCP
          port: 8080
  egress:
    - to:
        - podSelector:
            matchLabels:
              app: database
      ports:
        - protocol: TCP
          port: 5432

Checklist

Pre-Deployment

  • Infrastructure as Code reviewed
  • Secrets in secrets manager (not env files)
  • Resource limits set
  • Health checks configured
  • Logging and monitoring enabled
  • Network policies defined
  • Backup strategy in place

Production Readiness

  • Multi-AZ deployment
  • Auto-scaling configured
  • SSL/TLS enabled
  • WAF rules configured
  • Disaster recovery tested
  • Runbooks documented

Frequently asked questions

What does the Devops Cloud AI skill do?

DevOps, cloud infrastructure, and platform engineering. Use when working with AWS, GCP, Azure, Kubernetes, Terraform, CI/CD pipelines, or infrastructure as code.

Why use Devops Cloud on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/travisjneuman/.claude/tree/master/skills/devops-cloud. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Devops Cloud?

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 Devops Cloud?

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

Is the Devops Cloud AI skill free?

Yes. It is published on GitHub by travisjneuman 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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