engineering-team/skills/senior-devops/SKILL.md
Comprehensive DevOps skill for CI/CD, infrastructure automation, containerization, and cloud platforms (AWS, GCP, Azure). Includes pipeline setup, infrastructure as code, deployment automation, and monitoring. Use when setting up pipelines, deploying applications, managing infrastructure, implementing monitoring, or optimizing deployment processes.
npx skillsauth add alirezarezvani/claude-skills senior-devopsInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Complete toolkit for senior devops with modern tools and best practices.
This skill provides three core capabilities through automated scripts:
# Script 1: Pipeline Generator — scaffolds CI/CD pipelines for GitHub Actions or CircleCI
python scripts/pipeline_generator.py ./app --platform=github --stages=build,test,deploy
# Script 2: Terraform Scaffolder — generates and validates IaC modules for AWS/GCP/Azure
python scripts/terraform_scaffolder.py ./infra --provider=aws --module=ecs-service --verbose
# Script 3: Deployment Manager — generates deployment manifests + runbooks with rollback support
python3 scripts/deployment_manager.py deploy --env=staging --image=app:1.2.3 --strategy=blue-green --verbose --json
Scaffolds CI/CD pipeline configurations for GitHub Actions or CircleCI, with stages for build, test, security scan, and deploy.
Example — GitHub Actions workflow:
# .github/workflows/ci.yml
name: CI/CD Pipeline
on:
push:
branches: [main, develop]
pull_request:
branches: [main]
jobs:
build-and-test:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
- name: Set up Node.js
uses: actions/setup-node@v4
with:
node-version: '20'
cache: 'npm'
- run: npm ci
- run: npm run lint
- run: npm test -- --coverage
- name: Upload coverage
uses: codecov/codecov-action@v4
build-docker:
needs: build-and-test
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
- name: Build and push image
uses: docker/build-push-action@v5
with:
push: ${{ github.ref == 'refs/heads/main' }}
tags: ghcr.io/${{ github.repository }}:${{ github.sha }}
deploy:
needs: build-docker
if: github.ref == 'refs/heads/main'
runs-on: ubuntu-latest
steps:
- name: Deploy to ECS
run: |
aws ecs update-service \
--cluster production \
--service app-service \
--force-new-deployment
Usage:
python scripts/pipeline_generator.py <project-path> --platform=github|circleci --stages=build,test,deploy
Generates, validates, and plans Terraform modules. Enforces consistent module structure and runs terraform validate + terraform plan before any apply.
Example — AWS ECS service module:
# modules/ecs-service/main.tf
resource "aws_ecs_task_definition" "app" {
family = var.service_name
requires_compatibilities = ["FARGATE"]
network_mode = "awsvpc"
cpu = var.cpu
memory = var.memory
container_definitions = jsonencode([{
name = var.service_name
image = var.container_image
essential = true
portMappings = [{
containerPort = var.container_port
protocol = "tcp"
}]
environment = [for k, v in var.env_vars : { name = k, value = v }]
logConfiguration = {
logDriver = "awslogs"
options = {
awslogs-group = "/ecs/${var.service_name}"
awslogs-region = var.aws_region
awslogs-stream-prefix = "ecs"
}
}
}])
}
resource "aws_ecs_service" "app" {
name = var.service_name
cluster = var.cluster_id
task_definition = aws_ecs_task_definition.app.arn
desired_count = var.desired_count
launch_type = "FARGATE"
network_configuration {
subnets = var.private_subnet_ids
security_groups = [aws_security_group.app.id]
assign_public_ip = false
}
load_balancer {
target_group_arn = aws_lb_target_group.app.arn
container_name = var.service_name
container_port = var.container_port
}
}
Usage:
python scripts/terraform_scaffolder.py <target-path> --provider=aws|gcp|azure --module=ecs-service|gke-deployment|aks-service [--verbose]
Generates Kubernetes deployment manifests and ordered kubectl runbooks for blue/green or rolling strategies, with health-check gates before traffic switches and rollback runbooks. The tool writes manifests and prints the commands — it never applies them to a cluster itself, so every change gets a human review.
Example — Kubernetes blue/green deployment (blue-slot specific elements):
# k8s/deployment-blue.yaml
apiVersion: apps/v1
kind: Deployment
metadata:
name: app-blue
labels:
app: myapp
slot: blue # slot label distinguishes blue from green
spec:
replicas: 3
selector:
matchLabels:
app: myapp
slot: blue
template:
metadata:
labels:
app: myapp
slot: blue
spec:
containers:
- name: app
image: ghcr.io/org/app:1.2.3
readinessProbe: # gate: pod must pass before traffic switches
httpGet:
path: /healthz
port: 8080
initialDelaySeconds: 10
periodSeconds: 5
resources:
requests:
cpu: "250m"
memory: "256Mi"
limits:
cpu: "500m"
memory: "512Mi"
Usage:
python scripts/deployment_manager.py deploy \
--env=staging|production \
--image=app:1.2.3 \
--strategy=blue-green|rolling \
--health-check-url=https://app.example.com/healthz
python scripts/deployment_manager.py rollback --env=production --to-version=1.2.2
python scripts/deployment_manager.py --analyze --env=production # audit current state
references/cicd_pipeline_guide.md — detailed CI/CD patterns, best practices, anti-patternsreferences/infrastructure_as_code.md — IaC step-by-step processes, optimization, troubleshootingreferences/deployment_strategies.md — deployment strategy configs, security considerations, scalabilityscripts/ directory# Scaffold or update module
python scripts/terraform_scaffolder.py ./infra --provider=aws --module=ecs-service --verbose
# Validate and plan — review diff before applying
terraform -chdir=infra init
terraform -chdir=infra validate
terraform -chdir=infra plan -out=tfplan
# Apply only after plan review
terraform -chdir=infra apply tfplan
# Verify resources are healthy
aws ecs describe-services --cluster production --services app-service \
--query 'services[0].{Status:status,Running:runningCount,Desired:desiredCount}'
# Generate or update pipeline config
python scripts/pipeline_generator.py . --platform=github --stages=build,test,security,deploy
# Build and tag image
docker build -t ghcr.io/org/app:$(git rev-parse --short HEAD) .
docker push ghcr.io/org/app:$(git rev-parse --short HEAD)
# Deploy with health-check gate
python scripts/deployment_manager.py deploy \
--env=production \
--image=app:$(git rev-parse --short HEAD) \
--strategy=blue-green \
--health-check-url=https://app.example.com/healthz
# Verify pods are running
kubectl get pods -n production -l app=myapp
kubectl rollout status deployment/app-blue -n production
# Switch traffic after verification
kubectl patch service app-svc -n production \
-p '{"spec":{"selector":{"slot":"blue"}}}'
# Immediate rollback via deployment manager
python scripts/deployment_manager.py rollback --env=production --to-version=1.2.2
# Or via kubectl
kubectl rollout undo deployment/app -n production
kubectl rollout status deployment/app -n production
# Verify rollback succeeded
kubectl get pods -n production -l app=myapp
curl -sf https://app.example.com/healthz || echo "ROLLBACK FAILED — escalate"
Use these companion skills for cloud-specific deep dives:
| Skill | Cloud | Use When | |-------|-------|----------| | aws-solution-architect | AWS | ECS/EKS, Lambda, VPC design, cost optimization | | azure-cloud-architect | Azure | AKS, App Service, Virtual Networks, Azure DevOps | | gcp-cloud-architect | GCP | GKE, Cloud Run, VPC, Cloud Build (coming soon) |
Multi-cloud vs single-cloud decision:
Start single-cloud. Add a second cloud only when there is a concrete business or compliance driver — not for theoretical redundancy.
Terraform (or its open-source fork OpenTofu) is the recommended IaC tool for most teams:
Choose Pulumi when the team strongly prefers TypeScript, Python, Go, or C# over HCL:
| Tool | Use When | |------|----------| | CloudFormation | AWS-only shop; need native AWS support (StackSets, Service Catalog) | | Bicep | Azure-only shop; simpler syntax than ARM templates | | Cloud Deployment Manager | GCP-only; rare — most GCP teams prefer Terraform |
Rule of thumb: Use Terraform/OpenTofu unless you are 100% committed to a single cloud AND the cloud-native tool offers a feature Terraform cannot replicate (e.g., AWS Service Catalog integration).
Check the comprehensive troubleshooting section in references/deployment_strategies.md.
development
Use when someone wants to run a weekly review, close open loops, audit stalled projects and commitments, get their system back to trusted, restart a lapsed review habit, or says "/cs:weekly-review". Walks David Allen's three-phase loop — GET CLEAR, GET CURRENT, GET CREATIVE — with deterministic scripts that inventory open loops, gate the checklist with named gaps, and score commitment health 0-100.
development
Use when someone wants to decide whether a meeting is worth calling, price a meeting in dollars, build a timeboxed agenda with desired outcomes, or turn messy meeting notes into owned action items — or says "should this be a meeting", "/cs:meeting-prep", or "/cs:meeting-actions". Runs a cost gate (ASYNC / NOT-READY / MEET), builds a decision-first agenda, and extracts an owner + due-date checklist that flags every orphan.
development
Convert a rambling description of a desired outcome into one polished, autonomous /goal prompt ready to paste into a fresh session. Use when the user says "/fable-goal", "turn this into a goal prompt", "write me a fable prompt", "write the prompt that builds X", or rambles about something they want made and asks for the prompt that makes it happen. The output is a single copy-paste prompt, never the build itself. Do NOT use when the user wants the thing built right now in this session — only when they want the PROMPT that will make it happen in a fresh session.
development
Use when someone wants to plan a deep work day, time-block their calendar or task list, budget or cut shallow work, protect focus hours, track deep-work sessions and streaks, run an end-of-day shutdown ritual, or says "/deep-work" or "/time-block". Classifies tasks deep vs shallow, builds an energy-first time-blocked schedule that refuses deep demand past the 4-hour ceiling, batches shallow work into at most two windows, and logs focus sessions against a weekly target.