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 2 094 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 649 copies of these same skills sitting in other people's repositories — counted once here, not 649 times.
4 344 unique 392 authors 4 updated this month 579 from vendors
Expert Kubernetes specialist for production-grade container orchestration. Invoke for cluster management, workload deployment, security hardening, and performance optimization. Keywords: Kubernetes, K8s, kubectl, Helm, RBAC, NetworkPolicy.
Distributed systems architect specializing in microservices design patterns. Invoke for service boundaries, domain-driven design, saga patterns, event sourcing, service mesh, distributed tracing. Keywords: microservices, service mesh, distributed systems, Kubernetes, event-driven.
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.
Senior Terraform engineer for infrastructure as code, multi-cloud provisioning, and modular architecture. Invoke for Terraform modules, state management, provider configuration, and enterprise IaC patterns.
Create HTML system architecture diagrams — microservice maps, dependency graphs, deployment topologies, data ownership maps, integration diagrams. Useful during incidents, design reviews, onboarding, and capacity planning. Use whenever the user wants to visualize, document, or explain how a system fits together — across services, regions, queues, caches, databases, or organizational boundaries. Reach for this whenever the explanation would otherwise involve sentences like "service A talks to service B which writes to queue C".
Reviews opportunities in the sales pipeline to identify stuck deals, missing next steps, weak qualification, and unbalanced stages. Use when user asks "how is my pipeline looking", "analyze pipeline health", "which deals are stuck", "pipeline review", "find at-risk deals", "identify stalled opportunities", or "audit my open deals".
Prepares production launches. Use when preparing to deploy to production. Use when you need a pre-launch checklist, when setting up monitoring, when planning a staged rollout, or when you need a rollback strategy.
Read the Convex deployment's 72h insights (read limits, OCC contention), root-cause each event in code, report evidence-backed perf/cost findings with fixes.
Classify + announce the target Convex deployment before any deployment-affecting command; fresh explicit consent for prod actions; session read-only mode.
Set and wire Convex deployment env vars / secrets for the app.
Query a running Convex app's logs + health in natural language (official MCP): failures, slow/expensive functions, deploy causality — scoped, evidence-backed, with a dashboard deep link.
Migrate schema + backfill data on a deployed Convex app using @convex-dev/migrations.
Audit and optimize an existing Convex app: security, scale, upgrades, observability.
Rehearse a live-app schema change + backfill on a snapshot-seeded preview deployment, verify, then promote the proven change to prod with the snapshot as rollback.
Set up Sentinel production error capture in your own Convex deployment.
>- Facilitates a zero-based AI-first process redesign session that helps a team reimagine an existing work process as AI-first. Guides them through framing, idea expansion, current-state task map, a future-state swimlane blueprint tagging each step AI-owned / Hybrid / Human-led, an AI-Agents-&-Skills summary table, and a next-sprint capability backlog. Weighs the full range of AI building blocks — process change, knowledge, tools, reusable skills, agents, connected agents — instead of defaulting to an agent. Use when the user wants to redesign a process for AI, make a workflow AI-first, map which tasks AI should own, or find AI or agent opportunities in a process. It shows WHERE AI could help; it does NOT build or deploy the agents or skills themselves. Do NOT use to build a specific agent or skill, for a one-off automation with no process to rethink, or for employee performance evaluation.
Use this skill when a Power Automate flow triggers the agent with a payload describing an unknown source column from a lab results file that broke an Azure Data Factory ingestion pipeline. The skill semantically matches the source column against the canonical clinical schema indexed in Azure AI Search, scoped first by the sending lab and then by clinical domain, and writes a suggested destination-column mapping to the Dataverse review queue for a clinical informatics steward to approve. Also activates on manual invocations like "map this lab column", "the ADF pipeline failed on column X, what should it map to", or "suggest a canonical column for this lab feed field".
Use when the user asks to set up, run, or check a scheduled regulation, law, or regulatory-guidance monitor or tracker — triggers include "set up a regulation tracker for X", "monitor [regulation] for me", "run my regulation monitor", "what changed on [regulation] this week", or an unattended scheduler invoking a saved profile. On first invocation walk interactive setup (topics, jurisdictions, cadence, delivery), auto-discover the top authoritative sources per topic, and STOP for user confirmation before any monitoring. On subsequent runs visit only the locked source list plus user seeds, classify items, flag team-relevant ones via a WorkIQ-derived keyword match, and render a self-contained HTML dashboard with client-side sortable columns. Do NOT use for one-off legal research, reading a single document, computing compliance liability or filing position, or non-regulatory news monitoring.
RouterOS /container subsystem for running OCI containers on MikroTik devices. Use when: enabling containers on RouterOS, setting up VETH/bridge networking for containers, managing container lifecycle via CLI or REST API, building OCI images for RouterOS, configuring container environment variables, troubleshooting container issues, or when the user mentions RouterOS container, /container, VETH, device-mode container, or MikroTik Docker.
RouterOS /app YAML format for container applications (7.21+ builtin app, 7.22+ custom YAML creation). Use when: writing or validating RouterOS /app YAML files, working with MikroTik container apps, building docker-compose-like definitions for RouterOS, creating /app store schemas, debugging /app validation errors, or when the user mentions /app, tikapp, or RouterOS container YAML.
Create and review .gitlab-ci.yml files based on GitLab CI best practices and company standards. Use when creating CI/CD pipelines for new projects, reviewing existing .gitlab-ci.yml, or optimizing pipeline configuration.
Query and manage Grafana dashboards, alert rules, and data sources via HTTP API. Use when viewing dashboards, troubleshooting alerts, checking service metrics, finding data sources, or when Grafana, monitoring, alerts, dashboards, or observability is mentioned.
Query Prometheus monitoring metrics and alert rules. Use when the user needs to check CPU/memory/disk utilization, service health, audit alert rules, analyze capacity trends, or mentions Prometheus, PromQL, metrics monitoring, or targets.
Manage Kubernetes cluster resources via kubectl. Use when user needs to view, troubleshoot, or modify K8s workloads across multiple clusters.
Create and configure Sentry projects across staging/production environments via REST API. Use when user needs to onboard a new service to Sentry, create Sentry projects, or set up error monitoring for a new application.
Audit Terraform codebases for security, cost, code quality, and architecture issues. Use when reviewing Terraform projects, checking infrastructure-as-code compliance, or assessing AWS resource configurations for best practices.
Query Sentry errors, triage issues, and check release health via REST API. Use when debugging exceptions, investigating crash reports, triaging error issues, checking release stability, or any error monitoring and issue tracking task. Also triggers on mentions of Sentry, errors, exceptions, crashes, stack traces, or error rates.
Handles LLM-as-judge and code evaluator workflows on Arize including creating/updating evaluators, running evaluations on spans or experiments, managing tasks, trigger-run operations, column mapping, and continuous monitoring. Use when the user mentions create evaluator, LLM judge, code evaluator, hallucination, faithfulness, correctness, relevance, run eval, score spans, score experiment, trigger-run, column mapping, continuous monitoring, or improve evaluator prompt.
Creates, reads, updates, and deletes Arize AI integrations that store LLM provider credentials used by evaluators and other Arize features. Supports any LLM provider (e.g. OpenAI, Anthropic, Azure OpenAI, AWS Bedrock, Vertex AI, Gemini, NVIDIA NIM). Use when the user mentions AI integration, LLM provider credentials, create integration, list integrations, update credentials, delete integration, or connecting an LLM provider to Arize.
Adds Arize AX tracing to an LLM application for the first time. Detects the stack, routes to the single matching integration doc, wires auto-instrumentation after user confirmation, and verifies traces land. Use when the user wants to instrument their app, add tracing from scratch, set up LLM observability, integrate OpenTelemetry or openinference, or get started with Arize tracing.
Audit the infra and DevOps domain - CI/CD, deployment, IaC, secrets management, environments, logging, observability. Run as part of /audit Phase E.
Audit the cost and billing risk domain - runaway loops, unbounded LLM/API calls, missing rate limits on paid services, infrastructure cost amplifiers. Run as part of /audit Phase E.
Use after deployments to verify health endpoints, run API smoke tests, and confirm release integrity with CLI checks.
Audit the current project for stale or broken orchestration setup. Verifies monitoring.md, AGENTS.md/CLAUDE.md, graphify freshness, dead script references, placeholder values still in YAML frontmatter. Read-only - never mutates. Use after /monitoring-setup, after pulling someone else's setup, or when something feels off.
Generate a Confluent Cloud topic creation script with idempotency checks. Use when the user asks to create a topic, provision topics, or write a `create-topics.sh` for Confluent Cloud. Do NOT trigger for self-managed Apache Kafka, schema registration, Terraform generation, or Kafka Streams topology authoring.
Create Confluent-specific skills for external users. Use this skill when users want to create, build, or author a new skill related to Confluent Cloud, Confluent Platform, Apache Kafka, WarpStream, Flink, Connectors, Schema Registry, Tableflow, CDC pipelines, or any Confluent product. Skills can be use-case focused (like data enrichment, CDC to Tableflow, stream processing workflows) or component-specific (like a Flink skill, Schema Registry skill, or Connector skill). Do NOT use this skill when users want to directly use Confluent products (e.g., build a pipeline, write a producer, deploy Flink SQL) — use the appropriate product-specific skill instead. This skill is specifically for creating new skills, not for using existing ones.
Use when the user wants to integrate a Kafka client into an existing application or scaffold a Java Kafka client project (Maven or Gradle based) for Confluent Cloud, local Docker, or WarpStream. Covers the Apache Kafka Java clients (KafkaProducer/KafkaConsumer/KafkaShareConsumer) with Avro, JSON Schema, or Protobuf serializers. Also use when the user wants to optimize Java Kafka client configuration for WarpStream. Do NOT trigger for Kafka Streams apps (use kafka-streams-programming), Flink, connectors, or Kafka Python client (use developing-kafka-python-client).
Build and deploy Apache Flink user-defined functions (UDFs) in Java for stream processing over Kafka. Use this skill when users want to create scalar UDFs, user-defined table functions (UDTFs), or process table functions (PTFs) in Java, deploy them to Confluent Cloud or local Docker environments, and invoke them from Flink SQL or the Table API. Trigger on: Flink UDF, custom Flink function, process table function, PTF, UDTF, Flink user defined, extend Flink SQL, stateful stream processing with Flink. Do NOT trigger for: Kafka Streams UDFs (use kafka-streams-programming skill), general Flink job development without custom functions, CDC streaming data piplines that include Flink (prefer the confluent-cloud-cdc-tableflow skill), Flink connector setup, or Kafka producer/consumer code.
Scan a project to identify Kafka applications, extract schemas from data models, tag PII fields, generate Terraform for Confluent Schema Registry registration, and produce a migration report with rollout ordering. Use this skill when a user asks to analyze a folder or repo for Kafka usage, extract schemas, audit producer/consumer configurations, or generate Terraform for Schema Registry.
Use this skill to assess and plan a migration from AWS MSK (Managed Streaming for Apache Kafka) to Confluent Cloud. Triggers on user intent like "migrate MSK to Confluent Cloud", "move off MSK", "MSK to CC cutover", "Zero-Cut migration from MSK", "kcp scan my MSK", or any discussion of MSK-to-CC assessment, planning, cluster sizing, Cluster Linking setup, Gateway-based switchover, or post-cutover validation. Do NOT trigger for non-MSK Kafka sources (open-source Kafka, Aiven, Confluent Platform, Redpanda) — this skill is MSK-only. Do NOT trigger for greenfield Confluent Cloud projects with no existing Kafka source. Do NOT trigger for general Kafka programming questions (producer/consumer code, Kafka Streams) unrelated to migration.
Generate CI/CD installation scripts for AWS CDK projects. Use when setting up deployment pipelines, creating install.sh scripts, configuring GitLab CI, or setting up AWS CodeBuild for CDK deployments.
AWS Cloud Development Kit (CDK) expert for building cloud infrastructure with TypeScript/Python. Use when creating CDK stacks, defining CDK constructs, implementing infrastructure as code, or when the user mentions CDK, CloudFormation, IaC, cdk synth, cdk deploy, or wants to define AWS infrastructure programmatically. Covers CDK app structure, construct patterns, stack composition, and deployment workflows.
Estimate AWS costs for CDK projects using real-time pricing data. Use when calculating infrastructure costs, generating pricing reports, or analyzing CDK resource costs before deployment.
Comprehensive security and compliance scanning for AWS CDK projects. Use when running security audits, checking license compliance, scanning container vulnerabilities, or running aggregated SAST/IaC/secret analysis before deployment.
Run configurable BMAD pipeline for story delivery using subagent
Use when deploying a local project or codebase to Zeabur. Use when the user says "deploy this", "deploy to Zeabur", "deploy from GitHub", "search my repos", or "find my repository". Default to direct deploy unless the user explicitly asks for Git-based deployment.
Use when scaling a dedicated Kubernetes cluster (Linode LKE or AWS EKS) on Zeabur — listing node pools, changing node counts, or adding/removing node pools. Use when user says "scale my cluster", "add nodes", "remove nodes", "resize node pool", "加節點", "擴容", "縮容". Do NOT use for renting new servers (use zeabur-server-rent) or for single-VM dedicated servers (they have no node pools).
Use when viewing service runtime or build logs. Use when user says "show logs", "why did deploy fail", "check build output", or "debug runtime error".