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
Teach agents to build synthetic monitoring as code with Checkly, including Playwright browser checks, API checks, alerting, and CI deploy workflows.
Expert-level CI/CD pipeline skill for test automation. Covers GitHub Actions, Jenkins, GitLab CI, Azure DevOps, parallel execution, matrix strategies, caching, artifact management, and deployment gates.
Optimize CI test pipelines through intelligent test splitting, parallelization, caching strategies, and selective test execution based on code changes.
CI/CD pipeline configuration skill for test automation, covering GitHub Actions, Jenkins, GitLab CI, test parallelization, reporting, and artifact management.
Integration testing with real dependencies in throwaway Docker containers using the Testcontainers Node.js API - GenericContainer, exposed ports, wait strategies, module containers, Docker Compose environments, and reliable cleanup.
Testing Docker containers and images including Dockerfile linting, container structure tests, security scanning, and multi-stage build validation.
Detecting and testing infrastructure drift between desired state and actual state using automated scanning, alerting, and remediation patterns.
Kubernetes deployment testing including manifest validation, Helm chart testing, pod health checks, network policy testing, and rollback verification.
AI-powered testing patterns with Mabl including auto-healing tests, visual regression, API testing, performance monitoring, CI/CD integration, and intelligent test maintenance using Mabl's machine learning capabilities.
Enforcing performance budgets in CI/CD including bundle size limits, load time thresholds, Lighthouse score gates, and resource count limits.
Testing CI/CD pipeline configurations including build validation, deployment verification, rollback testing, and pipeline-as-code quality checks.
Automated API testing using Postman collections with Newman CLI for CI/CD integration, environment management, and test reporting.
Comprehensive SDET interview preparation covering coding challenges, system design for testing, automation framework design, CI/CD pipeline questions, testing strategy discussions, and behavioral interview patterns for QA engineering roles.
Integration testing with Sentry for error tracking, performance monitoring, and release health verification in production environments.
Testing serverless functions including Lambda, Cloud Functions, and Edge Functions with local emulation, cold start testing, and timeout handling.
Synthetic monitoring test creation for uptime checking, user journey simulation, API health verification, and multi-region availability testing.
Infrastructure-as-Code testing with Terratest, terraform validate, plan verification, and drift detection for Terraform configurations.
Docker-based integration testing with Testcontainers for databases, message queues, cloud services, and custom containers with lifecycle management.
Enforces rigorous verification evidence before any claims of task completion, preventing false positives, untested deployments, and premature celebration with a structured gate function.
Guidance for creating, managing and executing workflow automations that chain multiple tools and agents together. Use when the user asks about building, listing, running or composing workflows and automated task pipelines.
Guides deployment of Azure API Management infrastructure using Infrastructure as Code (Bicep/Terraform), CI/CD pipelines (GitHub Actions/Azure DevOps), and APIOps workflows. Use when deploying APIM, creating pipelines, or implementing dev→test→prod promotion strategies.
Design Azure cloud architectures from requirements and generate High-Level Design (HLD) documentation with service selection, patterns, cost estimates, and WAF alignment. Use this when asked to design or architect Azure solutions.
Analyzes and explains Azure API Management architecture decisions for enterprise API marketplace implementations using VNet Internal mode, Front Door, hybrid authentication, and multi-environment strategies. Use when discussing APIM component selection, network topology, cost optimization, or comparing alternatives like workspaces vs instances, VNet Internal vs External mode, or Front Door vs Application Gateway.
Look up real-time Azure retail pricing for any service, SKU, or region using the Azure MCP pricing tool. Estimate deployment costs from Bicep, ARM, or Terraform templates, compare pricing across regions, price types (Consumption, Reservation, DevTest), and surface savings plan options. Use when asked about Azure costs, SKU prices, region comparisons, or template cost estimates.
Create and edit architecture diagrams using Draw.io MCP (`drawio/create_diagram`) with reliable Azure icon rendering guidance and troubleshooting. compatibility Requires Python 3 and internet access to refresh the icon catalog (periodic, not per-run).
Create and edit diagrams using the Draw.io MCP server — any shape, any vendor. USE FOR: draw me a diagram, create an architecture diagram, add Azure/AWS/GCP/Cisco/Kubernetes icons to a diagram, convert Mermaid to draw.io, fix overlapping arrows, edit a .drawio file, network topology diagrams, CI/CD pipeline diagrams, auth flow diagrams. Supports XML, Mermaid, and CSV. Uses drawio/search_shapes to find any of 10,000+ shapes across all vendor and icon libraries. DO NOT USE FOR: Excalidraw output (use excalidraw-mcp-diagramming skill).
Safe Terraform provider upgrades with automatic resource migration, breaking change detection, and state management using moved blocks. Use when upgrading provider versions, handling removed resources, migrating deprecated syntax, or performing major version upgrades.
Analyze Azure architectures for cost optimization opportunities, provide savings recommendations, and calculate ROI for improvements.
Design and create Terraform modules from real infrastructure requirements. Use when asked to create a Terraform module, build a module from a requirement, turn repeated Terraform into a module, design module inputs and outputs, review whether a Terraform pattern should become a module, refactor existing Terraform into a reusable module, or assess whether a module is over-abstracted. Covers Azure-focused modules with KISS/DRY principles, module boundary definition, interface design, and practical file structure generation. Do NOT use for general Terraform coding with no module boundary, provider version upgrades (use terraform-provider-upgrade skill), or non-Terraform IaC.
Assess Azure architectures against Well-Architected Framework (WAF) five pillars - Reliability, Security, Cost Optimization, Operational Excellence, and Performance Efficiency. Provide scores and recommendations.
> Use when adding monitoring, metrics, logging, or tracing to Spring AI or LLM integration code. Covers token tracking, latency measurement, cost estimation, and prompt/response logging. Use when user mentions AI monitoring, token costs, or LLM observability.
> Use when the project follows hexagonal (ports & adapters) architecture. Prevents domain code from depending on Spring or JPA. Use when you see packages like domain/, application/, infrastructure/, or adapters/ in the project structure.
> Use when adding monitoring, metrics, logging, or tracing to Spring AI or LLM integration code. Covers token tracking, latency measurement, cost estimation, and prompt/response logging. Use when user mentions AI monitoring, token costs, or LLM observability.
> Use when the project follows hexagonal (ports & adapters) architecture. Prevents domain code from depending on Spring or JPA. Use when you see packages like domain/, application/, infrastructure/, or adapters/ in the project structure.
Monitoring and observability strategy, implementation, and troubleshooting. Use this skill whenever the user mentions monitoring, observability, metrics, logs, traces, alerting, SLOs, Prometheus, Grafana, Datadog, Loki, or OpenTelemetry. Triggers include designing metrics strategy (Four Golden Signals, RED/USE), setting up Prometheus/Grafana/Loki, creating alerts or dashboards, calculating SLOs and error budgets, instrumenting with OpenTelemetry, analyzing performance issues, choosing between monitoring tools, optimizing Datadog costs, migrating to open-source stack, and setting up distributed tracing.
CI/CD pipeline design, optimization, DevSecOps security scanning, and troubleshooting. Use this skill whenever the user mentions CI/CD, GitHub Actions, GitLab CI, pipelines, workflows, builds, or DevSecOps. Triggers include creating new CI/CD workflows, debugging pipeline failures or flaky tests, implementing SAST/DAST/SCA security scanning, optimizing slow builds with caching or parallelization, setting up deployment workflows, securing pipelines with OIDC or secrets management, implementing matrix builds or test sharding, and troubleshooting Docker, permissions, or timeout issues.
Infrastructure as Code with Terraform and Terragrunt. Use this skill whenever the user mentions Terraform, Terragrunt, HCL, or infrastructure as code. Triggers include writing or reviewing .tf files, creating reusable modules, debugging terraform plan/apply errors, managing remote state and locks, fixing state drift, setting up CI/CD for Terraform, scaffolding new modules, validating module structure, and implementing Terragrunt DRY patterns across environments.
Systematic Kubernetes troubleshooting and incident response. Use this skill whenever the user mentions Kubernetes, K8s, kubectl, pods, containers, or clusters. Triggers include diagnosing CrashLoopBackOff, ImagePullBackOff, OOMKilled, or Pending pods, responding to production incidents, troubleshooting node NotReady or DiskPressure, debugging service connectivity or networking, investigating PVC or storage failures, analyzing performance degradation, checking cluster health, troubleshooting Helm releases, and conducting post-incident reviews.
AWS cost optimization and FinOps workflows. Use this skill whenever the user mentions AWS costs, cloud spending, FinOps, Reserved Instances, Savings Plans, or cost reduction. Triggers include finding unused resources, analyzing the AWS bill, rightsizing EC2 or RDS instances, evaluating Spot instances, detecting cost anomalies, migrating to Graviton or newer instance generations, implementing tagging for cost allocation, setting up AWS Budgets, conducting monthly cost reviews, comparing RI vs Savings Plans, and optimizing storage, network, or database costs.
GitOps deployment workflows with ArgoCD and Flux. Use this skill whenever the user mentions GitOps, ArgoCD, Flux, Flagger, Argo Rollouts, or continuous deployment to Kubernetes. Triggers include setting up ArgoCD or Flux from scratch, designing Git repository structures (monorepo vs polyrepo, app-of-apps), deploying to multiple clusters with ApplicationSets, managing secrets in Git (SOPS, Sealed Secrets, External Secrets Operator), implementing canary or blue-green deployments, troubleshooting sync or reconciliation issues, working with OCI artifacts, and comparing ArgoCD vs Flux.
Dataverse solution lifecycle — create, export, import, promote across environments, and validate deployments. Use when the user wants to package customizations, deploy to another environment, or move work between dev / test / prod.
Keeps ingestion repository and published GPT-RAG documentation aligned with shipped formats, configuration, indexing, deployment, audit, and operator behavior.
Conducts and records a verifiable ingestion architectural decision. Use when a choice alters pipeline boundaries, chunk/index contracts, identity, data, source integration, deployment, or operation with meaningful reversal cost.
Selects and runs evidence-based validation for Python ingestion, chunking, jobs, Azure AI Search contracts, deployment assets, and the operator frontend. Use whenever repository behavior or engineering assets change.
Router skill for LLMQuant credit workflows. Use when the user needs issuer credit review, spread regime analysis, high-yield stress monitoring, default risk, debt maturity, or covenant context.
Launch AutoMagicCalib microservice and web UI from NGC release images via Docker Compose. Use when user says 'deploy auto calibration', 'launch auto calibration', 'launch AMC', 'start MS+UI', or 'set up auto-magic-calib'. Requires NGC API key.
Profile a DeepStream pipeline with Nsight Systems and derive its configs from the measurement. Use when the user asks for an efficient, performant, or profiled pipeline — or to benchmark, tune, or measure FPS.
> Use this skill when building, deploying, evaluating, debugging, or measuring latency for the DeepStream SOP Inference Microservice — a GPU-accelerated FastAPI service that detects whether operators perform assembly-line steps in order via event boundary detection (GEBD) plus VLM classification. Trigger missing or out-of-order SOP steps, score factory/work-cell video for procedure compliance, run VLM-based SOP checking on industrial cameras, or call /v1/chat/completions with a file, RTSP, or Basler camera. Also trigger Triton CAPI, nvds_custom_postprocess, Cosmos Reason 1/2 vLLM, SSE streaming, Kafka NvProto/JSON output, Basler/Pylon camera + emulation, Docker compose, chunk-level latency. Do NOT trigger for generic DeepStream pipelines, object detection/tracking, NIM imports, or video summarization.