Senior Devops

Comprehensive DevOps skill for CI/CD, infrastructure automation, containerization, and cloud platforms (AWS, GCP, Azure). Includes pipeline setup, infrastruc...

Security Scan

The skill's description promises full cloud CI/CD and infra automation, but the included scripts are lightweight placeholders and the instructions/examples reference external cloud operations without requesting or implementing the credentials and tooling needed — this mismatch is concerning.

Purpose & Capability

The name/description claim comprehensive CI/CD, Terraform, and cloud deployments (AWS/GCP/Azure). However, the three included Python scripts are simple scaffolding/placeholders that only validate a path, produce an empty 'findings' list, and print a report — they do not implement Terraform, AWS/GCP/Azure SDK/CLI calls, container orchestration, or actual pipeline generation. The declared requirements list no cloud credentials or binaries (terraform, aws, kubectl, docker), which is inconsistent with the described capabilities.

Instruction Scope

SKILL.md instructs running the included scripts and provides examples that show terraform validate/plan, aws ecs update-service, Docker build/push, and Kubernetes blue/green deployment YAML. Those examples will require external CLIs/credentials in practice, but the runtime instructions do not request those env vars or explain prerequisites. The README examples could mislead users into thinking the scripts perform those actions when the current script implementations do not.

Install Mechanism

No install spec is present and the skill is instruction-only with bundled scripts and docs. Nothing will be downloaded from external URLs during install, so there is low install-time risk. The included Python files are local and human-readable.

Credentials

The skill declares no required environment variables or primary credential despite providing examples that would normally require AWS/GCP/Azure credentials and CLI tools. This omission is an inconsistency: a legitimate DevOps skill that performs cloud operations would normally require and document credentials and local tooling prerequisites.

Persistence & Privilege

The skill is not force-included (always:false) and does not request persistent system-wide privileges. It does not modify other skills' configs or request elevated presence. Autonomous invocation remains enabled by default (normal), but there are no additional privilege requests.

What to consider before installing

This skill appears to overpromise: the README and examples describe full cloud CI/CD and infra actions, but the shipped scripts are simple placeholders that only validate a target path and print reports. Before installing or using it in production: 1) Review the three Python files yourself — they are short and local; verify whether they implement the cloud/terraform logic you need. 2) Do not run these scripts against production environments until you confirm what they do; run in a sandbox. 3) If you expect real AWS/GCP/Azure or terraform operations, require the author to document prerequisites (AWS creds, terraform, kubectl, docker) and to add explicit env var handling and safety checks. 4) Prefer skills from a known, trusted source and ask the publisher for a changelog or proof of implementation. If you want, I can (a) list the exact code locations to inspect for hidden network calls or credential usage, or (b) produce a checklist of minimal tests to validate the skill safely.

Complete toolkit for senior devops with modern tools and best practices.

Quick Start

Main Capabilities

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 — orchestrates container deployments with rollback support
python scripts/deployment_manager.py deploy --env=production --image=app:1.2.3 --strategy=blue-green

Core Capabilities

1. Pipeline Generator

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

2. Terraform Scaffolder

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]

3. Deployment Manager

Orchestrates deployments with blue/green or rolling strategies, health-check gates, and automatic rollback on failure.

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

Resources

• Pattern Reference: references/cicd_pipeline_guide.md — detailed CI/CD patterns, best practices, anti-patterns
• Workflow Guide: references/infrastructure_as_code.md — IaC step-by-step processes, optimization, troubleshooting
• Technical Guide: references/deployment_strategies.md — deployment strategy configs, security considerations, scalability
• Tool Scripts: scripts/ directory

Development Workflow

1. Infrastructure Changes (Terraform)

# 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}'

2. Application Deployment

# 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"}}}'

3. Rollback Procedure

# 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"

Troubleshooting

Check the comprehensive troubleshooting section in references/deployment_strategies.md.