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devops-automator

Writes and debugs infrastructure-as-code (Terraform, CloudFormation, CDK), generates CI/CD pipeline configurations (GitHub Actions, GitLab CI, Jenkins), and produces Kubernetes manifests, Docker setups, and cloud resource definitions for AWS, GCP, and Azure. Covers secrets management, observability instrumentation, rollback strategies, cost controls, and security scanning embedded in delivery pipelines. Use when the user asks about deploying applications, writing pipeline YAML, configuring infrastructure as code, Docker containers, Kubernetes manifests, autoscaling, cloud resource provisioning, setting up monitoring and alerting, or automating operational workflows.

90

Quality

88%

Does it follow best practices?

Impact

Pending

No eval scenarios have been run

SecuritybySnyk

Passed

No known issues

SKILL.md
Quality
Evals
Security

DevOps Automator

Workflow

  1. Assess the application topology, environments, deployment pain points, and compliance constraints before writing any configuration.
  2. Standardize infrastructure definitions and delivery stages: agree on directory layout, naming conventions, and environment promotion order.
  3. Draft IaC, pipeline, and runtime configs with concrete examples (see patterns below). Run terraform plan / dry-run equivalents and review the diff before applying anything.
  4. Embed security scanning, health checks, rollout controls, and rollback paths directly in the pipeline.
  5. Verify: after deploy, confirm health check endpoints return 200, key metrics are flowing, and alerts fire correctly on a test signal. Rollback immediately if checks fail.
  6. Instrument logs, metrics, and traces.
  7. Review scaling behavior, operating cost, and recovery time objectives post-rollout.

Concrete Patterns

Terraform — minimal AWS module scaffold

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

variable "name"        { type = string }
variable "image"       { type = string }
variable "cpu"         { default = 256 }
variable "memory"      { default = 512 }
variable "desired"     { default = 2 }

resource "aws_ecs_task_definition" "this" {
  family                   = var.name
  cpu                      = var.cpu
  memory                   = var.memory
  network_mode             = "awsvpc"
  requires_compatibilities = ["FARGATE"]
  container_definitions = jsonencode([{
    name      = var.name
    image     = var.image
    essential = true
    portMappings = [{ containerPort = 8080 }]
    logConfiguration = {
      logDriver = "awslogs"
      options = {
        "awslogs-group"  = "/ecs/${var.name}"
        "awslogs-region" = "us-east-1"
        "awslogs-stream-prefix" = "ecs"
      }
    }
  }])
}

Validation step: terraform init && terraform plan -out=tfplan → review before terraform apply tfplan.

GitHub Actions — build, scan, deploy, rollback

# .github/workflows/deploy.yml
name: CI/CD Pipeline

on:
  push:
    branches: [main]

jobs:
  build-and-scan:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4

      - name: Build image
        run: docker build -t ${{ env.IMAGE }}:${{ github.sha }} .

      - name: Vulnerability scan (Trivy)
        uses: aquasecurity/trivy-action@master
        with:
          image-ref: ${{ env.IMAGE }}:${{ github.sha }}
          exit-code: "1"
          severity: "CRITICAL,HIGH"

      - name: Push to registry
        run: |
          docker tag ${{ env.IMAGE }}:${{ github.sha }} ${{ secrets.REGISTRY }}/${{ env.IMAGE }}:${{ github.sha }}
          docker push ${{ secrets.REGISTRY }}/${{ env.IMAGE }}:${{ github.sha }}

  deploy:
    needs: build-and-scan
    runs-on: ubuntu-latest
    steps:
      - name: Deploy (rolling update)
        run: |
          kubectl set image deployment/${{ env.APP }} \
            app=${{ secrets.REGISTRY }}/${{ env.IMAGE }}:${{ github.sha }}
          kubectl rollout status deployment/${{ env.APP }} --timeout=120s

      - name: Health check
        run: |
          STATUS=$(curl -s -o /dev/null -w "%{http_code}" https://${{ env.HOST }}/health)
          if [ "$STATUS" != "200" ]; then
            echo "Health check failed ($STATUS) — rolling back"
            kubectl rollout undo deployment/${{ env.APP }}
            exit 1
          fi

Kubernetes — deployment with health checks and rollback annotations

# k8s/deployment.yaml
apiVersion: apps/v1
kind: Deployment
metadata:
  name: my-service
  annotations:
    kubernetes.io/change-cause: "Release {{ .Values.image.tag }}"
spec:
  replicas: 3
  strategy:
    type: RollingUpdate
    rollingUpdate:
      maxSurge: 1
      maxUnavailable: 0          # zero-downtime rollout
  selector:
    matchLabels:
      app: my-service
  template:
    metadata:
      labels:
        app: my-service
    spec:
      containers:
        - name: app
          image: registry/my-service:{{ .Values.image.tag }}
          ports:
            - containerPort: 8080
          readinessProbe:
            httpGet:
              path: /health
              port: 8080
            initialDelaySeconds: 10
            periodSeconds: 5
            failureThreshold: 3
          livenessProbe:
            httpGet:
              path: /health
              port: 8080
            initialDelaySeconds: 30
            periodSeconds: 10
          resources:
            requests: { cpu: "100m", memory: "128Mi" }
            limits:   { cpu: "500m", memory: "512Mi" }
          env:
            - name: DB_PASSWORD
              valueFrom:
                secretKeyRef:
                  name: my-service-secrets
                  key: db-password

Rollback command: kubectl rollout undo deployment/my-service

Constraints

  • Do not accept manual production steps when they can be automated safely.
  • Treat secret handling, vulnerability scanning, and auditability as baseline requirements. No plaintext secrets in code; integrate with Vault, AWS Secrets Manager, or Kubernetes Secrets with RBAC.
  • Call out cloud cost risks (resource tagging, budget alerts, right-sizing), lock-in tradeoffs, and recovery gaps explicitly.
  • Always include a validation or rollback step alongside any destructive or state-changing operation.
  • Prefer boring, repeatable automation over clever one-off scripts.
  • On request, produce observability configs (log aggregation, Prometheus/Grafana/Datadog metrics, distributed traces, error-budget alerts).
Repository
OpenRoster-ai/awesome-agents
Commit

010799b

Last updated
Created

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