devops-iac-engineer
Implements infrastructure as code using Terraform, Kubernetes, and cloud platforms. Designs scalable architectures, CI/CD pipelines, and observability solutions. Provides security-first DevOps practices and site reliability engineering guidance.
Best use case
devops-iac-engineer is best used when you need a repeatable AI agent workflow instead of a one-off prompt. It is especially useful for teams working in multi. Implements infrastructure as code using Terraform, Kubernetes, and cloud platforms. Designs scalable architectures, CI/CD pipelines, and observability solutions. Provides security-first DevOps practices and site reliability engineering guidance.
Implements infrastructure as code using Terraform, Kubernetes, and cloud platforms. Designs scalable architectures, CI/CD pipelines, and observability solutions. Provides security-first DevOps practices and site reliability engineering guidance.
Users should expect a more consistent workflow output, faster repeated execution, and less time spent rewriting prompts from scratch.
Practical example
Example input
Use the "devops-iac-engineer" skill to help with this workflow task. Context: Implements infrastructure as code using Terraform, Kubernetes, and cloud platforms. Designs scalable architectures, CI/CD pipelines, and observability solutions. Provides security-first DevOps practices and site reliability engineering guidance.
Example output
A structured workflow result with clearer steps, more consistent formatting, and an output that is easier to reuse in the next run.
When to use this skill
- Use this skill when you want a reusable workflow rather than writing the same prompt again and again.
When not to use this skill
- Do not use this when you only need a one-off answer and do not need a reusable workflow.
- Do not use it if you cannot install or maintain the related files, repository context, or supporting tools.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/devops-iac-engineer/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How devops-iac-engineer Compares
| Feature / Agent | devops-iac-engineer | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Implements infrastructure as code using Terraform, Kubernetes, and cloud platforms. Designs scalable architectures, CI/CD pipelines, and observability solutions. Provides security-first DevOps practices and site reliability engineering guidance.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
Related Guides
AI Agents for Coding
Browse AI agent skills for coding, debugging, testing, refactoring, code review, and developer workflows across Claude, Cursor, and Codex.
Cursor vs Codex for AI Workflows
Compare Cursor and Codex for AI coding workflows, repository assistance, debugging, refactoring, and reusable developer skills.
SKILL.md Source
# DevOps IaC Engineer This Skill helps DevOps teams design, implement, and maintain cloud infrastructure using Infrastructure as Code principles. Use this when building cloud architectures, deploying containerized applications, setting up CI/CD pipelines, or implementing observability and security practices. ## Quick Navigation - **Terraform & IaC**: See [terraform.md](reference/terraform.md) for Terraform best practices and patterns - **Kubernetes & Containers**: See [kubernetes.md](reference/kubernetes.md) for container orchestration - **Cloud Platforms**: See [cloud_platforms.md](reference/cloud_platforms.md) for AWS, Azure, GCP guidance - **CI/CD Pipelines**: See [cicd.md](reference/cicd.md) for pipeline design and GitOps - **Observability**: See [observability.md](reference/observability.md) for monitoring and logging - **Security**: See [security.md](reference/security.md) for DevSecOps practices - **Templates & Tools**: See [templates.md](reference/templates.md) for ready-to-use templates ## Core Principles ### Key DevOps Terminology (Consistent Throughout) - **Infrastructure as Code (IaC)**: Managing infrastructure through declarative code files - **GitOps**: Using Git as the single source of truth for infrastructure and applications - **Immutable Infrastructure**: Infrastructure components that are replaced rather than modified - **Service Mesh**: Infrastructure layer for service-to-service communication - **Observability**: Ability to understand system state from external outputs (logs, metrics, traces) - **SLI/SLO/SLA**: Service Level Indicators/Objectives/Agreements for reliability - **RTO/RPO**: Recovery Time Objective/Recovery Point Objective for disaster recovery ### Workflow: Infrastructure Implementation When implementing infrastructure, follow this structured approach: 1. **Understand Requirements** - What is the business need? (new application, migration, scaling, compliance) - What are the scale requirements? (traffic, data, geographic distribution) - What are the constraints? (budget, timeline, regulatory) - What are the dependencies? (existing systems, data sources) 2. **Design Architecture** - Choose appropriate cloud platform(s) and services - Design for high availability and fault tolerance - Plan network topology and security boundaries - Identify data flows and storage requirements - Document architecture with diagrams 3. **Select IaC Tools** - Terraform for multi-cloud infrastructure provisioning - Kubernetes manifests/Helm for container orchestration - CI/CD tool selection based on team and requirements - Configuration management tools if needed 4. **Implement Infrastructure** - Create modular, reusable IaC code - Follow security best practices (see [security.md](reference/security.md)) - Implement proper state management and versioning - Use consistent naming and tagging conventions - Document code and create README files 5. **Set Up Observability** - Define SLIs and SLOs for critical services - Implement logging, metrics, and tracing - Create dashboards and alerts - Set up log aggregation and analysis - Plan on-call rotation and runbooks 6. **Implement CI/CD** - Design deployment pipeline stages - Implement automated testing (unit, integration, e2e) - Set up GitOps workflows - Configure deployment strategies (blue/green, canary) - Implement rollback procedures 7. **Test & Validate** - Run infrastructure tests (security, compliance, cost) - Perform disaster recovery drills - Load testing and performance validation - Security scanning and penetration testing - Document test results and improvements 8. **Deploy & Monitor** - Execute phased rollout - Monitor metrics and logs closely - Validate against SLOs - Document runbooks and troubleshooting guides - Conduct post-deployment review ### Decision Framework: Tool Selection **Multi-Cloud Requirements** → Terraform or Pulumi **AWS-Only** → Terraform, AWS CDK, or CloudFormation **Container Orchestration** → Kubernetes (EKS, GKE, AKS) **Simple Container Deployment** → ECS, Cloud Run, or App Service **Configuration Management** → Ansible or cloud-native solutions **GitOps Workflows** → ArgoCD or Flux **CI/CD Pipelines** → GitHub Actions, GitLab CI, or Jenkins ## Common Challenges & Solutions **Problem**: Infrastructure drift between code and reality **Solution**: Implement automated drift detection, use terraform plan in CI/CD, enable read-only production access, maintain state file integrity **Problem**: Secrets management and credential exposure **Solution**: Use cloud-native secret managers (AWS Secrets Manager, HashiCorp Vault), implement SOPS for encrypted secrets in Git, use IRSA/workload identity **Problem**: High cloud costs and unexpected bills **Solution**: Implement tagging strategy, use cost allocation tags, set up budget alerts, right-size resources, use spot instances, implement auto-scaling **Problem**: Complex Kubernetes configurations **Solution**: Use Helm charts for templating, implement Kustomize for environment-specific configs, follow GitOps patterns, use operators for complex workloads ## Collaboration Tips - **With Development Teams**: Provide self-service platforms, document APIs, share infrastructure as reusable modules - **With Security Teams**: Implement policy as code, automate compliance checks, provide audit trails - **With SRE Teams**: Define SLIs/SLOs together, share on-call responsibilities, collaborate on incident response - **With Finance Teams**: Provide cost visibility, forecast expenses, implement chargeback models --- ## Next Steps 1. Start with [terraform.md](reference/terraform.md) if you're implementing infrastructure as code 2. Use [kubernetes.md](reference/kubernetes.md) for container orchestration 3. Reference [templates.md](reference/templates.md) for ready-to-use configurations 4. Check [observability.md](reference/observability.md) to set up monitoring **Note**: Always verify current infrastructure state, security requirements, and compliance needs before implementing changes. This Skill provides frameworks and best practices but should be adapted to your organization's specific requirements.
Related Skills
vector-database-engineer
Expert in vector databases, embedding strategies, and semantic search implementation. Masters Pinecone, Weaviate, Qdrant, Milvus, and pgvector for RAG applications, recommendation systems, and similar
tutorial-engineer
Creates step-by-step tutorials and educational content from code. Transforms complex concepts into progressive learning experiences with hands-on examples. Use PROACTIVELY for onboarding guides, feature tutorials, or concept explanations.
reverse-engineer
Expert reverse engineer specializing in binary analysis, disassembly, decompilation, and software analysis. Masters IDA Pro, Ghidra, radare2, x64dbg, and modern RE toolchains. Handles executable analysis, library inspection, protocol extraction, and vulnerability research. Use PROACTIVELY for binary analysis, CTF challenges, security research, or understanding undocumented software.
research-engineer
An uncompromising Academic Research Engineer. Operates with absolute scientific rigor, objective criticism, and zero flair. Focuses on theoretical correctness, formal verification, and optimal implementation across any required technology.
rag-engineer
Expert in building Retrieval-Augmented Generation systems. Masters embedding models, vector databases, chunking strategies, and retrieval optimization for LLM applications. Use when: building RAG, vector search, embeddings, semantic search, document retrieval.
protocol-reverse-engineering
Master network protocol reverse engineering including packet analysis, protocol dissection, and custom protocol documentation. Use when analyzing network traffic, understanding proprietary protocols, or debugging network communication.
prompt-engineering-patterns
Master advanced prompt engineering techniques to maximize LLM performance, reliability, and controllability in production. Use when optimizing prompts, improving LLM outputs, or designing production prompt templates.
prompt-engineer
Transforms user prompts into optimized prompts using frameworks (RTF, RISEN, Chain of Thought, RODES, Chain of Density, RACE, RISE, STAR, SOAP, CLEAR, GROW)
performance-engineer
Expert performance engineer specializing in modern observability, application optimization, and scalable system performance. Masters OpenTelemetry, distributed tracing, load testing, multi-tier caching, Core Web Vitals, and performance monitoring. Handles end-to-end optimization, real user monitoring, and scalability patterns. Use PROACTIVELY for performance optimization, observability, or scalability challenges.
observability-engineer
Build production-ready monitoring, logging, and tracing systems. Implements comprehensive observability strategies, SLI/SLO management, and incident response workflows. Use PROACTIVELY for monitoring infrastructure, performance optimization, or production reliability.
network-engineer
Expert network engineer specializing in modern cloud networking, security architectures, and performance optimization. Masters multi-cloud connectivity, service mesh, zero-trust networking, SSL/TLS, global load balancing, and advanced troubleshooting. Handles CDN optimization, network automation, and compliance. Use PROACTIVELY for network design, connectivity issues, or performance optimization.
mlops-engineer
Build comprehensive ML pipelines, experiment tracking, and model registries with MLflow, Kubeflow, and modern MLOps tools. Implements automated training, deployment, and monitoring across cloud platforms. Use PROACTIVELY for ML infrastructure, experiment management, or pipeline automation.