Expert DevOps engineer for CI/CD, IaC, Kubernetes, and deployment automation. Activate on: CI/CD, GitHub Actions, Terraform, Docker, Kubernetes, Helm, ArgoCD, GitOps, deployment pipeline,
npx skills add https://github.com/curiositech/some_claude_skills --skill devops-automator
Expert DevOps engineer specializing in CI/CD pipelines, infrastructure as code, container orchestration, and deployment automation.
Activate on: "CI/CD", "GitHub Actions", "deployment pipeline", "Terraform", "infrastructure as code", "IaC", "Docker", "Kubernetes", "K8s", "Helm", "container orchestration", "GitOps", "ArgoCD", "deployment automation", "secrets management", "monitoring setup"
NOT for: Application development → language skills | Database design → data-pipeline-engineer | API design → api-architect
| Domain | Tools & Technologies |
|--------|---------------------|
| CI/CD | GitHub Actions, GitLab CI, Jenkins |
| IaC | Terraform, AWS CDK, Pulumi |
| Containers | Docker, Kubernetes, Helm |
| GitOps | ArgoCD, Flux, Kustomize |
| Monitoring | Prometheus, Grafana, ELK/EFK |
Code Commit → Build → Test → Security Scan → Package
↓
Monitor ← Release Staging ← Smoke Tests ← Deploy Dev
↓
Manual Approval
↓
Deploy Production
App Repo ──CI──▶ Config Repo ──ArgoCD──▶ K8s Cluster
▲ │
└────Continuous Sync─────┘
Full working examples are in ./references/:
| File | Description | Lines |
|------|-------------|-------|
| github-actions-patterns.yaml | Complete CI/CD pipeline | 217 |
| terraform-eks-module.tf | Production EKS cluster | 282 |
| kubernetes-deployment.yaml | Deployment + HPA + ArgoCD | 200 |
| dockerfile-multistage.dockerfile | Optimized multi-stage build | 51 |
Symptom: Nearly identical workflow files duplicated across repositories
Fix: Reusable workflows, Helm charts, Kustomize bases, Terraform modules
Symptom: API keys, passwords committed to git
Fix: Secret managers (Vault, AWS SM), sealed secrets, env vars from secure sources
Symptom: No plan for deployment failure, manual intervention required
Fix: Blue/green, canary with automated rollback, ArgoCD auto-revert
Symptom: Single 45-minute pipeline rebuilding everything on every commit
Fix: Parallel jobs, caching, incremental builds, path-based triggers
Symptom: K8s pods without CPU/memory limits consuming all host resources
Fix: Always set requests/limits, use LimitRanges and ResourceQuotas
Symptom: Dockerfile without USER instruction, pods running privileged
Fix: Add USER instruction, set securityContext.runAsNonRoot: true
Symptom: FROM node:latest or image: app:latest in production
Fix: Pin specific versions, use immutable tags with SHA digests
Symptom: Missing HEALTHCHECK in Dockerfile, no liveness/readiness probes
Fix: Add health endpoints, configure probes with appropriate timeouts
Symptom: replicas: 1, no pod anti-affinity, single availability zone
Fix: Multiple replicas, pod anti-affinity, topology spread constraints
Symptom: terraform.tfstate committed to git or stored locally
Fix: Remote backend (S3+DynamoDB, Terraform Cloud, GCS)
Symptom: Multiple CI runs for same branch, deployment race conditions
Fix: Use concurrency groups, implement deployment locks
Symptom: No vulnerability scanning, no secret detection in CI
Fix: Trivy, Snyk, or Grype for vulnerabilities; TruffleHog for secrets
Symptom: Manual changes to infrastructure, config diverges from code
Fix: ArgoCD diff detection, terraform plan in CI, regular audits
Symptom: IAM roles with * actions, service accounts with cluster-admin
Fix: Principle of least privilege, IRSA for pods, audit permissions
Symptom: No metrics, logs only on stdout, no alerting
Fix: Export metrics, structured logging, define SLOs, configure alerts
Run ./scripts/validate-devops-skill.sh to check:
[ ] All secrets in secret management (not in code)
[ ] Resource limits defined for all containers
[ ] Health checks configured (liveness, readiness)
[ ] Horizontal pod autoscaling enabled
[ ] Security contexts set (non-root, read-only)
[ ] Monitoring and alerting configured
[ ] Rollback strategy documented
[ ] Multi-environment support (dev, staging, prod)
[ ] Concurrency controls in CI pipelines
[ ] Remote state backend for Terraform
[ ] Vulnerability scanning in pipeline
[ ] Version pinning for all dependencies
Read, Write, Edit - File operations for configs and manifestsBash(docker:*) - Build and manage containersBash(kubectl:*) - Kubernetes operationsBash(terraform:*) - Infrastructure provisioningBash(helm:*) - Helm chart managementBash(gh:*) - GitHub CLI operationsAssess Kubernetes workloads and cluster configuration for AKS Automatic compatibility. Identifies incompatibilities, generates fixes, and guides migration from AKS Standard to AKS Automatic. WHEN: migrate to AKS Automatic, check AKS Automatic readiness, validate manifests for Automatic, assess cluster for Automatic compatibility, fix deployment for Automatic compatibility, identify AKS Automatic migration blockers, is my cluster ready for AKS Automatic.
Discovers available Azure OpenAI model capacity across regions and projects. Analyzes quota limits, compares availability, and recommends optimal deployment locations based on capacity requirements. USE FOR: find capacity, check quota, where can I deploy, capacity discovery, best region for capacity, multi-project capacity search, quota analysis, model availability, region comparison, check TPM availability. DO NOT USE FOR: actual deployment (hand off to preset or customize after discovery), quota increase requests (direct user to Azure Portal), listing existing deployments.
Interactive guided deployment flow for Azure OpenAI models with full customization control. Step-by-step selection of model version, SKU (GlobalStandard/Standard/ProvisionedManaged), capacity, RAI policy (content filter), and advanced options (dynamic quota, priority processing, spillover). USE FOR: custom deployment, customize model deployment, choose version, select SKU, set capacity, configure content filter, RAI policy, deployment options, detailed deployment, advanced deployment, PTU deployment, provisioned throughput. DO NOT USE FOR: quick deployment to optimal region (use preset).
Unified Azure OpenAI model deployment skill with intelligent intent-based routing. Handles quick preset deployments, fully customized deployments (version/SKU/capacity/RAI policy), and capacity discovery across regions and projects. USE FOR: deploy model, deploy gpt, create deployment, model deployment, deploy openai model, set up model, provision model, find capacity, check model availability, where can I deploy, best region for model, capacity analysis. DO NOT USE FOR: listing existing deployments (use foundry_models_deployments_list MCP tool), deleting deployments, agent creation (use agent/create), project creation (use project/create).
Intelligently deploys Azure OpenAI models to optimal regions by analyzing capacity across all available regions. Automatically checks current region first and shows alternatives if needed. USE FOR: quick deployment, optimal region, best region, automatic region selection, fast setup, multi-region capacity check, high availability deployment, deploy to best location. DO NOT USE FOR: custom SKU selection (use customize), specific version selection (use customize), custom capacity configuration (use customize), PTU deployments (use customize).
This skill should be used when working with LaminDB, an open-source data framework for biology that makes data queryable, traceable, reproducible, and FAIR. Use when managing biological datasets (scRNA-seq, spatial, flow cytometry, etc.), tracking computational workflows, curating and validating data with biological ontologies, building data lakehouses, or ensuring data lineage and reproducibility in biological research. Covers data management, annotation, ontologies (genes, cell types, diseases, tissues), schema validation, integrations with workflow managers (Nextflow, Snakemake) and MLOps platforms (W&B, MLflow), and deployment strategies.
Latch platform for bioinformatics workflows. Build pipelines with Latch SDK, @workflow/@task decorators, deploy serverless workflows, LatchFile/LatchDir, Nextflow/Snakemake integration.
Run Python code in the cloud with serverless containers, GPUs, and autoscaling. Use when deploying ML models, running batch processing jobs, scheduling compute-intensive tasks, or serving APIs that require GPU acceleration or dynamic scaling.
Take curiositech/devops-automator from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.