🚀

Senior DevOps Engineer

CI/CD pipeline design, Infrastructure as Code, containerization with Docker and Kubernetes, and deployment automation from a senior DevOps perspective.

by @alirezarezvani · MIT New

Built for: Developers Founders

What this skill does

Generate secure release workflows and manage cloud infrastructure without manually configuring complex tools. This ensures every update is tested and released safely while reducing setup time. Reach for this when launching a new product or streamlining how updates reach your customers.

@alirezarezvani · Development

view on github ↗

name: “senior-devops” description: 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.

Senior Devops

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.

Cicd Pipeline Guide

Overview

This reference guide provides comprehensive information for senior devops.

Patterns and Practices

Pattern 1: Best Practice Implementation

Description: Detailed explanation of the pattern.

When to Use:

Scenario 1
Scenario 2
Scenario 3

Implementation:

// Example code implementation
export class Example {
  // Implementation details
}

Benefits:

Benefit 1
Benefit 2
Benefit 3

Trade-offs:

Consider 1
Consider 2
Consider 3

Pattern 2: Advanced Technique

Description: Another important pattern for senior devops.

Implementation:

// Advanced example
async function advancedExample() {
  // Code here
}

Guidelines

Code Organization

Clear structure
Logical separation
Consistent naming
Proper documentation

Performance Considerations

Optimization strategies
Bottleneck identification
Monitoring approaches
Scaling techniques

Security Best Practices

Input validation
Authentication
Authorization
Data protection

Common Patterns

Pattern A

Implementation details and examples.

Pattern B

Implementation details and examples.

Pattern C

Implementation details and examples.

Anti-Patterns to Avoid

Anti-Pattern 1

What not to do and why.

Anti-Pattern 2

What not to do and why.

Tools and Resources

Recommended Tools

Tool 1: Purpose
Tool 2: Purpose
Tool 3: Purpose

Conclusion

Key takeaways for using this reference guide effectively.

Deployment Strategies

Overview

This reference guide provides comprehensive information for senior devops.

Patterns and Practices

Pattern 1: Best Practice Implementation

Description: Detailed explanation of the pattern.

When to Use:

Scenario 1
Scenario 2
Scenario 3

Implementation:

// Example code implementation
export class Example {
  // Implementation details
}

Benefits:

Benefit 1
Benefit 2
Benefit 3

Trade-offs:

Consider 1
Consider 2
Consider 3

Pattern 2: Advanced Technique

Description: Another important pattern for senior devops.

Implementation:

// Advanced example
async function advancedExample() {
  // Code here
}

Guidelines

Code Organization

Clear structure
Logical separation
Consistent naming
Proper documentation

Performance Considerations

Optimization strategies
Bottleneck identification
Monitoring approaches
Scaling techniques

Security Best Practices

Input validation
Authentication
Authorization
Data protection

Common Patterns

Pattern A

Implementation details and examples.

Pattern B

Implementation details and examples.

Pattern C

Implementation details and examples.

Anti-Patterns to Avoid

Anti-Pattern 1

What not to do and why.

Anti-Pattern 2

What not to do and why.

Tools and Resources

Recommended Tools

Tool 1: Purpose
Tool 2: Purpose
Tool 3: Purpose

Conclusion

Key takeaways for using this reference guide effectively.

Infrastructure As Code

Overview

This reference guide provides comprehensive information for senior devops.

Patterns and Practices

Pattern 1: Best Practice Implementation

Description: Detailed explanation of the pattern.

When to Use:

Scenario 1
Scenario 2
Scenario 3

Implementation:

// Example code implementation
export class Example {
  // Implementation details
}

Benefits:

Benefit 1
Benefit 2
Benefit 3

Trade-offs:

Consider 1
Consider 2
Consider 3

Pattern 2: Advanced Technique

Description: Another important pattern for senior devops.

Implementation:

// Advanced example
async function advancedExample() {
  // Code here
}

Guidelines

Code Organization

Clear structure
Logical separation
Consistent naming
Proper documentation

Performance Considerations

Optimization strategies
Bottleneck identification
Monitoring approaches
Scaling techniques

Security Best Practices

Input validation
Authentication
Authorization
Data protection

Common Patterns

Pattern A

Implementation details and examples.

Pattern B

Implementation details and examples.

Pattern C

Implementation details and examples.

Anti-Patterns to Avoid

Anti-Pattern 1

What not to do and why.

Anti-Pattern 2

What not to do and why.

Tools and Resources

Recommended Tools

Tool 1: Purpose
Tool 2: Purpose
Tool 3: Purpose

Conclusion

Key takeaways for using this reference guide effectively.

#!/usr/bin/env python3
"""
Deployment Manager
Automated tool for senior devops tasks
"""

import os
import sys
import json
import argparse
from pathlib import Path
from typing import Dict, List, Optional

class DeploymentManager:
    """Main class for deployment manager functionality"""
    
    def __init__(self, target_path: str, verbose: bool = False):
        self.target_path = Path(target_path)
        self.verbose = verbose
        self.results = {}
    
    def run(self) -> Dict:
        """Execute the main functionality"""
        print(f"🚀 Running {self.__class__.__name__}...")
        print(f"📁 Target: {self.target_path}")
        
        try:
            self.validate_target()
            self.analyze()
            self.generate_report()
            
            print("✅ Completed successfully!")
            return self.results
            
        except Exception as e:
            print(f"❌ Error: {e}")
            sys.exit(1)
    
    def validate_target(self):
        """Validate the target path exists and is accessible"""
        if not self.target_path.exists():
            raise ValueError(f"Target path does not exist: {self.target_path}")
        
        if self.verbose:
            print(f"✓ Target validated: {self.target_path}")
    
    def analyze(self):
        """Perform the main analysis or operation"""
        if self.verbose:
            print("📊 Analyzing...")
        
        # Main logic here
        self.results['status'] = 'success'
        self.results['target'] = str(self.target_path)
        self.results['findings'] = []
        
        # Add analysis results
        if self.verbose:
            print(f"✓ Analysis complete: {len(self.results.get('findings', []))} findings")
    
    def generate_report(self):
        """Generate and display the report"""
        print("\n" + "="*50)
        print("REPORT")
        print("="*50)
        print(f"Target: {self.results.get('target')}")
        print(f"Status: {self.results.get('status')}")
        print(f"Findings: {len(self.results.get('findings', []))}")
        print("="*50 + "\n")

def main():
    """Main entry point"""
    parser = argparse.ArgumentParser(
        description="Deployment Manager"
    )
    parser.add_argument(
        'target',
        help='Target path to analyze or process'
    )
    parser.add_argument(
        '--verbose', '-v',
        action='store_true',
        help='Enable verbose output'
    )
    parser.add_argument(
        '--json',
        action='store_true',
        help='Output results as JSON'
    )
    parser.add_argument(
        '--output', '-o',
        help='Output file path'
    )
    
    args = parser.parse_args()
    
    tool = DeploymentManager(
        args.target,
        verbose=args.verbose
    )
    
    results = tool.run()
    
    if args.json:
        output = json.dumps(results, indent=2)
        if args.output:
            with open(args.output, 'w') as f:
                f.write(output)
            print(f"Results written to {args.output}")
        else:
            print(output)

if __name__ == '__main__':
    main()

#!/usr/bin/env python3
"""
Pipeline Generator
Automated tool for senior devops tasks
"""

import os
import sys
import json
import argparse
from pathlib import Path
from typing import Dict, List, Optional

class PipelineGenerator:
    """Main class for pipeline generator functionality"""
    
    def __init__(self, target_path: str, verbose: bool = False):
        self.target_path = Path(target_path)
        self.verbose = verbose
        self.results = {}
    
    def run(self) -> Dict:
        """Execute the main functionality"""
        print(f"🚀 Running {self.__class__.__name__}...")
        print(f"📁 Target: {self.target_path}")
        
        try:
            self.validate_target()
            self.analyze()
            self.generate_report()
            
            print("✅ Completed successfully!")
            return self.results
            
        except Exception as e:
            print(f"❌ Error: {e}")
            sys.exit(1)
    
    def validate_target(self):
        """Validate the target path exists and is accessible"""
        if not self.target_path.exists():
            raise ValueError(f"Target path does not exist: {self.target_path}")
        
        if self.verbose:
            print(f"✓ Target validated: {self.target_path}")
    
    def analyze(self):
        """Perform the main analysis or operation"""
        if self.verbose:
            print("📊 Analyzing...")
        
        # Main logic here
        self.results['status'] = 'success'
        self.results['target'] = str(self.target_path)
        self.results['findings'] = []
        
        # Add analysis results
        if self.verbose:
            print(f"✓ Analysis complete: {len(self.results.get('findings', []))} findings")
    
    def generate_report(self):
        """Generate and display the report"""
        print("\n" + "="*50)
        print("REPORT")
        print("="*50)
        print(f"Target: {self.results.get('target')}")
        print(f"Status: {self.results.get('status')}")
        print(f"Findings: {len(self.results.get('findings', []))}")
        print("="*50 + "\n")

def main():
    """Main entry point"""
    parser = argparse.ArgumentParser(
        description="Pipeline Generator"
    )
    parser.add_argument(
        'target',
        help='Target path to analyze or process'
    )
    parser.add_argument(
        '--verbose', '-v',
        action='store_true',
        help='Enable verbose output'
    )
    parser.add_argument(
        '--json',
        action='store_true',
        help='Output results as JSON'
    )
    parser.add_argument(
        '--output', '-o',
        help='Output file path'
    )
    
    args = parser.parse_args()
    
    tool = PipelineGenerator(
        args.target,
        verbose=args.verbose
    )
    
    results = tool.run()
    
    if args.json:
        output = json.dumps(results, indent=2)
        if args.output:
            with open(args.output, 'w') as f:
                f.write(output)
            print(f"Results written to {args.output}")
        else:
            print(output)

if __name__ == '__main__':
    main()

#!/usr/bin/env python3
"""
Terraform Scaffolder
Automated tool for senior devops tasks
"""

import os
import sys
import json
import argparse
from pathlib import Path
from typing import Dict, List, Optional

class TerraformScaffolder:
    """Main class for terraform scaffolder functionality"""
    
    def __init__(self, target_path: str, verbose: bool = False):
        self.target_path = Path(target_path)
        self.verbose = verbose
        self.results = {}
    
    def run(self) -> Dict:
        """Execute the main functionality"""
        print(f"🚀 Running {self.__class__.__name__}...")
        print(f"📁 Target: {self.target_path}")
        
        try:
            self.validate_target()
            self.analyze()
            self.generate_report()
            
            print("✅ Completed successfully!")
            return self.results
            
        except Exception as e:
            print(f"❌ Error: {e}")
            sys.exit(1)
    
    def validate_target(self):
        """Validate the target path exists and is accessible"""
        if not self.target_path.exists():
            raise ValueError(f"Target path does not exist: {self.target_path}")
        
        if self.verbose:
            print(f"✓ Target validated: {self.target_path}")
    
    def analyze(self):
        """Perform the main analysis or operation"""
        if self.verbose:
            print("📊 Analyzing...")
        
        # Main logic here
        self.results['status'] = 'success'
        self.results['target'] = str(self.target_path)
        self.results['findings'] = []
        
        # Add analysis results
        if self.verbose:
            print(f"✓ Analysis complete: {len(self.results.get('findings', []))} findings")
    
    def generate_report(self):
        """Generate and display the report"""
        print("\n" + "="*50)
        print("REPORT")
        print("="*50)
        print(f"Target: {self.results.get('target')}")
        print(f"Status: {self.results.get('status')}")
        print(f"Findings: {len(self.results.get('findings', []))}")
        print("="*50 + "\n")

def main():
    """Main entry point"""
    parser = argparse.ArgumentParser(
        description="Terraform Scaffolder"
    )
    parser.add_argument(
        'target',
        help='Target path to analyze or process'
    )
    parser.add_argument(
        '--verbose', '-v',
        action='store_true',
        help='Enable verbose output'
    )
    parser.add_argument(
        '--json',
        action='store_true',
        help='Output results as JSON'
    )
    parser.add_argument(
        '--output', '-o',
        help='Output file path'
    )
    
    args = parser.parse_args()
    
    tool = TerraformScaffolder(
        args.target,
        verbose=args.verbose
    )
    
    results = tool.run()
    
    if args.json:
        output = json.dumps(results, indent=2)
        if args.output:
            with open(args.output, 'w') as f:
                f.write(output)
            print(f"Results written to {args.output}")
        else:
            print(output)

if __name__ == '__main__':
    main()

Install this Skill

Skills give your AI agent a consistent, structured approach to this task — better output than a one-off prompt.

npx skills add alirezarezvani/claude-skills --skill engineering-team/senior-devops

Download ZIP

Community skill by @alirezarezvani. Need a walkthrough? See the install guide →

Works with

Claude Code OpenAI Codex CLI Gemini CLI

Prefer no terminal? Download the ZIP and place it manually.

Details

Category: Development
License: MIT
Author: @alirezarezvani
Source: GitHub →
Source file: show path
engineering-team/senior-devops/SKILL.md

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Senior Devops

Quick Start

Main Capabilities

Core Capabilities

1. Pipeline Generator

2. Terraform Scaffolder

3. Deployment Manager

Resources

Development Workflow

1. Infrastructure Changes (Terraform)

2. Application Deployment

3. Rollback Procedure

Troubleshooting

Cicd Pipeline Guide

Overview

Patterns and Practices

Pattern 1: Best Practice Implementation

Pattern 2: Advanced Technique

Guidelines

Code Organization

Performance Considerations

Security Best Practices

Common Patterns

Pattern A

Pattern B

Pattern C

Anti-Patterns to Avoid

Anti-Pattern 1

Anti-Pattern 2

Tools and Resources

Recommended Tools

Further Reading

Conclusion

Deployment Strategies

Overview

Patterns and Practices

Pattern 1: Best Practice Implementation

Pattern 2: Advanced Technique

Guidelines

Code Organization

Performance Considerations

Security Best Practices

Common Patterns

Pattern A

Pattern B

Pattern C

Anti-Patterns to Avoid

Anti-Pattern 1

Anti-Pattern 2

Tools and Resources

Recommended Tools

Further Reading

Conclusion

Infrastructure As Code

Overview

Patterns and Practices

Pattern 1: Best Practice Implementation

Pattern 2: Advanced Technique

Guidelines

Code Organization

Performance Considerations

Security Best Practices

Common Patterns

Pattern A

Pattern B

Pattern C

Anti-Patterns to Avoid

Anti-Pattern 1

Anti-Pattern 2

Tools and Resources

Recommended Tools

Further Reading

Conclusion

Install this Skill

Works with

Details

People who install this also use