Senior Devops

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

Overview

This skill provides comprehensive DevOps capabilities through three core Python automation tools and extensive reference documentation. Whether setting up CI/CD pipelines, automating infrastructure, deploying containerized applications, or implementing monitoring, this skill delivers production-ready DevOps solutions.

Senior DevOps engineers use this skill for continuous integration/deployment, infrastructure as code (Terraform), containerization (Docker, Kubernetes), cloud platforms (AWS, GCP, Azure), pipeline automation (GitHub Actions, CircleCI), and observability (Prometheus, Grafana). The skill covers deployment strategies, infrastructure automation, and reliability engineering.

Core Value: Accelerate deployment pipelines by 75%+ while improving reliability, consistency, and infrastructure automation through proven DevOps patterns and tools.

Quick Start

Main Capabilities

This skill provides four core capabilities through automated scripts:

Script 1: Pipeline Generator

python scripts/pipeline_generator.py [options]

Script 2: Terraform Scaffolder

python scripts/terraform_scaffolder.py [options]

Script 3: Deployment Manager

python scripts/deployment_manager.py [options]

Script 4: ServiceNow Change Manager - ITIL change request automation

python scripts/servicenow_change_manager.py --deployment-file deploy.json --change-type normal

Core Capabilities

• CI/CD Pipeline Setup - GitHub Actions, CircleCI, GitLab CI configuration with automated testing, building, and deployment

• Infrastructure as Code - Terraform scaffolding for AWS, GCP, Azure with modular architecture and state management

• Container Orchestration - Docker and Kubernetes configuration, Helm charts, service mesh setup

• Deployment Automation - Blue-green, canary, rolling deployments with automated rollback strategies

• Monitoring & Observability - Prometheus, Grafana dashboards, logging (ELK/EFK), alerting, and incident response

• Security & Compliance - Secret management, RBAC, network policies, security scanning in pipelines

Python Tools

1. Pipeline Generator

Generate production-ready CI/CD pipelines for multiple platforms.

Key Features:

• GitHub Actions workflow generation

• CircleCI config creation

• GitLab CI pipeline templates

• Multi-stage builds (test, build, deploy)

• Automated testing integration

• Docker image building and pushing

Common Usage:

Generate GitHub Actions pipeline

python scripts/pipeline_generator.py --platform github --language nodejs --output .github/workflows/

CircleCI pipeline

python scripts/pipeline_generator.py --platform circleci --language python --test pytest

Full stack pipeline

python scripts/pipeline_generator.py --platform github --stack fullstack --deploy kubernetes

Help

python scripts/pipeline_generator.py --help

Use Cases:

• Setting up CI/CD for new projects

• Standardizing pipelines across teams

• Adding deployment stages to existing pipelines

2. Terraform Scaffolder

Scaffold infrastructure as code with Terraform best practices.

Key Features:

• Modular Terraform structure generation

• AWS, GCP, Azure provider templates

• Remote state configuration (S3, GCS, Azure Blob)

• Module organization (networking, compute, database)

• Variable and output management

• Security best practices built-in

Common Usage:

Scaffold AWS infrastructure

python scripts/terraform_scaffolder.py --provider aws --modules vpc,eks,rds --output infrastructure/

GCP infrastructure

python scripts/terraform_scaffolder.py --provider gcp --modules gke,cloudsql --remote-state gcs

Multi-environment setup

python scripts/terraform_scaffolder.py --provider aws --environments dev,staging,prod

Help

python scripts/terraform_scaffolder.py --help

Use Cases:

• Starting new infrastructure projects

• Organizing existing Terraform code

• Creating reusable infrastructure modules

3. Deployment Manager

Automate application deployments with multiple strategies.

Key Features:

• Blue-green deployment automation

• Canary release management

• Rolling update strategies

• Automated rollback on failure

• Health check integration

• Multi-environment deployment

Common Usage:

Blue-green deployment

python scripts/deployment_manager.py --strategy blue-green --app myapp --version v2.0

Canary deployment

python scripts/deployment_manager.py --strategy canary --app myapp --version v2.0 --canary-percentage 10

Rollback

python scripts/deployment_manager.py --rollback --app myapp --to-version v1.9

Help

python scripts/deployment_manager.py --help

Use Cases:

• Zero-downtime deployments

• Testing new releases with minimal risk

• Automating deployment workflows

4. ServiceNow Change Manager

Generate ServiceNow change request payloads from deployment configurations for ITIL-compliant change management.

Key Features:

• Change request generation (Standard, Normal, Emergency)

• Automatic risk assessment based on deployment scope

• Backout plan documentation from deployment config

• Test plan generation with validation steps

• CMDB Configuration Item linking

• CAB approval workflow support

• curl command generation for API testing

Common Usage:

Generate normal change request from deployment

python scripts/servicenow_change_manager.py
--deployment-file deploy-config.json
--change-type normal
--ci-names "pandora-api-prod,pandora-db-prod"
--start-time "2025-01-15T10:00:00Z"
--end-time "2025-01-15T12:00:00Z"
--output json

Generate standard change (pre-approved)

python scripts/servicenow_change_manager.py
--deployment-file deploy-config.json
--change-type standard
--output curl

Generate emergency change for hotfix

python scripts/servicenow_change_manager.py
--deployment-file hotfix.json
--change-type emergency
--output curl

Help

python scripts/servicenow_change_manager.py --help

Use Cases:

• ITIL-compliant deployment change management

• Audit trail for production deployments

• CAB approval automation

• Compliance tracking (SOX, PCI-DSS)

• Linking deployments to CMDB Configuration Items

See servicenow_change_mgmt.md for change management best practices.

See cicd_pipeline_guide.md for comprehensive documentation.

Reference Documentation

Cicd Pipeline Guide

Comprehensive guide available in references/cicd_pipeline_guide.md :

• Detailed patterns and practices

• Code examples

• Best practices

• Anti-patterns to avoid

• Real-world scenarios

Infrastructure As Code

Complete workflow documentation in references/infrastructure_as_code.md :

• Step-by-step processes

• Optimization strategies

• Tool integrations

• Performance tuning

• Troubleshooting guide

Deployment Strategies

Technical reference guide in references/deployment_strategies.md :

• Technology stack details

• Configuration examples

• Integration patterns

• Security considerations

• Scalability guidelines

ServiceNow Change Management

ITIL change management integration guide in references/servicenow_change_mgmt.md :

• Change request types (Standard, Normal, Emergency)

• Deployment-to-change workflow automation

• Change Request API patterns

• CAB approval workflows

• CI/CD pipeline integration (GitHub Actions, Jenkins)

• Risk assessment and impact analysis

• Backout plan documentation

• Post-implementation review

Tech Stack

Languages: TypeScript, JavaScript, Python, Go, Swift, Kotlin Frontend: React, Next.js, React Native, Flutter Backend: Node.js, Express, GraphQL, REST APIs Database: PostgreSQL, Prisma, NeonDB, Supabase DevOps: Docker, Kubernetes, Terraform, GitHub Actions, CircleCI Cloud: AWS, GCP, Azure

Key Workflows

1. CI/CD Pipeline Setup

Time: 2-3 hours for complete pipeline

• Define Pipeline Stages - Test, build, scan, deploy stages with appropriate triggers

• Generate Pipeline Configuration - Use pipeline generator for platform-specific config

Generate GitHub Actions pipeline

python scripts/pipeline_generator.py --platform github --language nodejs --deploy kubernetes

• Configure Secrets - Setup repository secrets for cloud credentials, API keys

• Test Pipeline - Trigger test run, validate all stages execute correctly

• Enable Branch Protection - Require pipeline success before merging

See cicd_pipeline_guide.md for pipeline patterns.

2. Infrastructure Provisioning with Terraform

Time: 4-6 hours for initial infrastructure

• Design Infrastructure - Define required resources (networking, compute, database, storage)

• Scaffold Terraform - Generate modular Terraform structure

Scaffold AWS infrastructure

python scripts/terraform_scaffolder.py --provider aws --modules vpc,eks,rds --output infrastructure/

• Configure Remote State - Setup S3/GCS backend for state management

• Apply Infrastructure - Plan and apply Terraform configuration terraform init terraform plan -out=tfplan terraform apply tfplan

• Document Architecture - Create diagrams and runbooks

See infrastructure_as_code.md for IaC best practices.

3. Kubernetes Application Deployment

Time: 3-4 hours for initial deployment

• Containerize Application - Create optimized Dockerfile with multi-stage build

• Create Kubernetes Manifests - Deployment, Service, Ingress, ConfigMap, Secret

• Deploy Application - Use deployment manager for controlled rollout

Blue-green deployment

python scripts/deployment_manager.py --strategy blue-green --app myapp --version v1.0

• Configure Monitoring - Setup Prometheus metrics, Grafana dashboards

• Test & Validate - Health checks, load testing, rollback testing

4. Monitoring & Alerting Setup

Time: 2-3 hours for complete observability stack

• Deploy Monitoring Stack - Prometheus, Grafana, Alertmanager

• Configure Metrics Collection - Application metrics, infrastructure metrics, logs

• Create Dashboards - Service health, resource usage, error rates

• Setup Alerts - Error rate thresholds, latency SLOs, capacity warnings

• Test Incident Response - Validate alerting, runbooks, escalation

See deployment_strategies.md for deployment and monitoring patterns.

Development Workflow

1. Setup and Configuration

Install dependencies

npm install

or

pip install -r requirements.txt

Configure environment

cp .env.example .env

2. Run Quality Checks

Use the analyzer script

python scripts/terraform_scaffolder.py .

Review recommendations

Apply fixes

3. Implement Best Practices

Follow the patterns and practices documented in:

• references/cicd_pipeline_guide.md

• references/infrastructure_as_code.md

• references/deployment_strategies.md

Best Practices Summary

Code Quality

• Follow established patterns

• Write comprehensive tests

• Document decisions

• Review regularly

Performance

• Measure before optimizing

• Use appropriate caching

• Optimize critical paths

• Monitor in production

Security

• Validate all inputs

• Use parameterized queries

• Implement proper authentication

• Keep dependencies updated

Maintainability

• Write clear code

• Use consistent naming

• Add helpful comments

• Keep it simple

Common Commands

Development

npm run dev npm run build npm run test npm run lint

Analysis

python scripts/terraform_scaffolder.py . python scripts/deployment_manager.py --analyze

Deployment

docker build -t app:latest . docker-compose up -d kubectl apply -f k8s/

Troubleshooting

Common Issues

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

Getting Help

• Review reference documentation

• Check script output messages

• Consult tech stack documentation

• Review error logs

Resources

• Pattern Reference: references/cicd_pipeline_guide.md

• Workflow Guide: references/infrastructure_as_code.md

• Technical Guide: references/deployment_strategies.md

• ServiceNow Guide: references/servicenow_change_mgmt.md

• ServiceNow Template: assets/servicenow-change-template.json

• Tool Scripts: scripts/ directory