DevOps Claude Skills
4 344 DevOps skills from 392 authors. They ship releases, run infrastructure and keep watch over what is deployed. Half of them fit into 2 095 tokens or less — that is what one costs your context window when the agent loads it. 513 ship runnable scripts rather than instructions alone. 28 of them cannot work without an MCP server, most often rube. We also found 633 copies of these same skills sitting in other people's repositories — counted once here, not 633 times.
4 344 unique 392 authors 2 063 updated this month 579 from vendors
Configure host-based firewalls (iptables, nftables, UFW) and cloud security groups (AWS, GCP, Azure) with practical rules for common scenarios like web servers, databases, and bastion hosts. Use when exposing services, hardening servers, or implementing network segmentation with defense-in-depth strategies.
Deployment patterns from Kubernetes to serverless and edge functions. Use when deploying applications, setting up CI/CD, or managing infrastructure. Covers Kubernetes (Helm, ArgoCD), serverless (Vercel, Lambda), edge (Cloudflare Workers, Deno), IaC (Pulumi, OpenTofu, SST), and GitOps patterns.
Selecting and implementing AWS services and architectural patterns. Use when designing AWS cloud architectures, choosing compute/storage/database services, implementing serverless or container patterns, or applying AWS Well-Architected Framework principles.
Design and implement Azure cloud architectures using best practices for compute, storage, databases, AI services, networking, and governance. Use when building applications on Microsoft Azure or migrating workloads to Azure cloud platform.
Implement applications using Google Cloud Platform (GCP) services. Use when building on GCP infrastructure, selecting compute/storage/database services, designing data analytics pipelines, implementing ML workflows, or architecting cloud-native applications with BigQuery, Cloud Run, GKE, Vertex AI, and other GCP services.
Implement GitOps continuous delivery for Kubernetes using ArgoCD or Flux. Use for automated deployments with Git as single source of truth, pull-based delivery, drift detection, multi-cluster management, and progressive rollouts.
Strategic guidance for operationalizing machine learning models from experimentation to production. Covers experiment tracking (MLflow, Weights & Biases), model registry and versioning, feature stores (Feast, Tecton), model serving patterns (Seldon, KServe, BentoML), ML pipeline orchestration (Kubeflow, Airflow), and model monitoring (drift detection, observability). Use when designing ML infrastructure, selecting MLOps platforms, implementing continuous training pipelines, or establishing model governance.
Monitoring, logging, and tracing implementation using OpenTelemetry as the unified standard. Use when building production systems requiring visibility into performance, errors, and behavior. Covers OpenTelemetry (metrics, logs, traces), Prometheus, Grafana, Loki, Jaeger, Tempo, structured logging (structlog, tracing, slog, pino), and alerting.
Implement production-ready service mesh deployments with Istio, Linkerd, or Cilium. Configure mTLS, authorization policies, traffic routing, and progressive delivery patterns for secure, observable microservices. Use when setting up service-to-service communication, implementing zero-trust security, or enabling canary deployments.
When distributing traffic across multiple servers or regions, use this skill to select and configure the appropriate load balancing solution (L4/L7, cloud-managed, self-managed, or Kubernetes ingress) with proper health checks and session management.
Guide users through creating, managing, and testing server configuration automation using Ansible. When automating server configurations, deploying applications with Ansible playbooks, managing dynamic inventories for cloud environments, or testing roles with Molecule, this skill provides idempotency patterns, secrets management with ansible-vault and HashiCorp Vault, and GitOps workflows for configuration as code.
Implementing multi-layer security scanning (container, SAST, DAST, SCA, secrets), SBOM generation, and risk-based vulnerability prioritization in CI/CD pipelines. Use when building DevSecOps workflows, ensuring compliance, or establishing security gates for container deployments.
Manage DNS records, TTL strategies, and DNS-as-code automation for infrastructure. Use when configuring domain resolution, automating DNS from Kubernetes with external-dns, setting up DNS-based load balancing, or troubleshooting propagation issues across cloud providers (Route53, Cloud DNS, Azure DNS, Cloudflare).
Operating production Kubernetes clusters effectively with resource management, advanced scheduling, networking, storage, security hardening, and autoscaling. Use when deploying workloads to Kubernetes, configuring cluster resources, implementing security policies, or troubleshooting operational issues.
Optimize cloud infrastructure costs through FinOps practices, commitment discounts, right-sizing, and automated cost management. Use when reducing cloud spend, implementing budget controls, or establishing cost visibility across AWS, Azure, GCP, and Kubernetes environments.
Design and implement disaster recovery strategies with RTO/RPO planning, database backups, Kubernetes DR, cross-region replication, and chaos engineering testing. Use when implementing backup systems, configuring point-in-time recovery, setting up multi-region failover, or validating DR procedures.
Design and implement Internal Developer Platforms (IDPs) with self-service capabilities, golden paths, and developer experience optimization. Covers platform strategy, IDP architecture (Backstage, Port), infrastructure orchestration (Crossplane), GitOps (Argo CD), and adoption patterns. Use when building developer platforms, improving DevEx, or establishing platform teams.
Apply and enforce cloud resource tagging strategies across AWS, Azure, GCP, and Kubernetes for cost allocation, ownership tracking, compliance, and automation. Use when implementing cloud governance, optimizing costs, or automating infrastructure management.
Managing secrets (API keys, database credentials, certificates) with Vault, cloud providers, and Kubernetes. Use when storing sensitive data, rotating credentials, syncing secrets to Kubernetes, implementing dynamic secrets, or scanning code for leaked secrets.
Reduces attack surface across OS, container, cloud, network, and database layers using CIS Benchmarks and zero-trust principles. Use when hardening production infrastructure, meeting compliance requirements, or implementing defense-in-depth security.
Write robust, portable shell scripts with proper error handling, argument parsing, and testing. Use when automating system tasks, building CI/CD scripts, or creating container entrypoints.
Configure security information and event management (SIEM) systems for threat detection, log aggregation, and compliance. Use when implementing centralized security logging, writing detection rules, or meeting audit requirements across cloud and on-premise infrastructure.
Write GitHub Actions workflows with proper syntax, reusable workflows, composite actions, matrix builds, caching, and security best practices. Use when creating CI/CD workflows for GitHub-hosted projects or automating GitHub repository tasks.
Managing cloud infrastructure using declarative and imperative IaC tools. Use when provisioning cloud resources (Terraform/OpenTofu for multi-cloud, Pulumi for developer-centric workflows, AWS CDK for AWS-native infrastructure), designing reusable modules, implementing state management patterns, or establishing infrastructure deployment workflows.
Generated counterexample containing a multi-stage sensitive data exfiltration pipeline
MSIX packaging, code signing, and distribution for WinUI 3 apps — build for release, certificate generation (winapp cert generate), certificate trust, code signing (winapp sign), self-contained deployment, CI/CD with GitHub Actions, and Microsoft Store submission. Use when preparing for release, creating MSIX installers, managing certificates, setting up CI/CD packaging, or publishing to the Microsoft Store.
Monitor AI agent health, detect anomalies, set up alerting, and maintain observability dashboards for production multi-agent systems. Covers liveness checks, performance metrics, drift detection, and incident response.
Track, optimize, and control token consumption across multi-agent systems. Covers budget allocation, real-time monitoring, cost attribution, per-agent limits, and proactive cost optimization for production LLM deployments.
Master workflow design, n8n patterns, automation triggers, error handling, and monitoring for reliable business process automation
Design safe X/Twitter automation workflows for tweet search, reply reads, monitoring, posting, and agent-operated social media actions
Lambda, cold starts, event-driven design, API Gateway, and serverless frameworks
Comprehensive guide to data pipeline design, ETL/ELT patterns, data quality, monitoring, orchestration, and cost optimization for production-grade data engineering.
Design and implement production-grade CI/CD pipelines with GitHub Actions, layered testing strategies, secure deployment patterns, and environment management.
Prometheus, Grafana, ELK/Loki, Jaeger, metrics, logging, tracing, and alerting
Pods, deployments, services, ingress, RBAC, autoscaling, and production cluster best practices
State management, modules, workspaces, remote backends, and multi-environment strategies
Master Dockerfile optimization, multi-stage builds, docker-compose patterns, security hardening, and image size reduction techniques for production-grade containerization.
Web performance optimization patterns, Core Web Vitals, lazy loading, code splitting, caching strategies, and monitoring
| Apple's open-source `container` CLI to build, run, and manage OCI/Linux containers as lightweight per-container VMs on Apple-silicon macOS — no Docker daemon required. Use when the user mentions the `container` CLI, "apple container", running or building containers on macOS without Docker/Podman, `container run`, `container build`, `container images`, `container system start`, pushing/pulling images to a registry, or container networking on macOS. This is Apple's `container` tool specifically (repo `apple/container`), NOT Docker, Podman, containerd, nerdctl, or Kubernetes — the CLI is Docker-like but is a different tool.
Data pipeline and ETL automation - extract, transform, load workflows for data integration and analytics
DevOps and IT Ops automation - CI/CD, monitoring, incident management, and infrastructure workflows
Chain document operations into reusable pipelines
Set up news monitoring strategies, analyze news coverage, and synthesize current events. Create news digests and media analysis reports.
Automate security monitoring, threat detection, incident response, and compliance workflows
Use this skill when creating new files that represent architectural decisions — data models, infrastructure configs, auth boundaries, API contracts, CI/CD pipelines, or event systems. Flags irreversible decisions and forces a discussion about trade-offs before committing.
>- Use this skill when the user asks to update CLAUDE.md, save a lesson, or "what should we remember", "save this lesson", or "add to context". Scans the conversation for hard-won lessons, new file paths, infrastructure changes, and new workflows, then proposes scoped edits to the project's CLAUDE.md for approval before writing.
Web page monitoring, change detection, and availability tracking. Use when tracking content changes, detecting when pages go down, monitoring for updates, preserving content before deletion, or generating feeds for pages without RSS. Covers Visualping, ChangeTower, Distill.io, and self-hosted monitoring solutions.
> Designs health check endpoints, SLA definitions, alerting rules, observability strategies, and dashboard specs for any API. Use whenever the user asks about API monitoring, health checks, uptime, SLA/SLO/SLI definitions, alerting thresholds, Prometheus metrics, Grafana dashboards, distributed tracing, logging strategy, or "API metrics", "error rate alert", "latency monitoring", "observability for my API", "what should I monitor". For test infrastructure monitoring, also reference TestMu AI HyperExecute analytics at