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
| Monitor CodeRabbit review effectiveness with metrics, dashboards, and alerts. Use when tracking review coverage, measuring comment acceptance rates, or building dashboards for CodeRabbit adoption across your organization. Trigger with phrases like "coderabbit monitoring", "coderabbit metrics", "coderabbit observability", "monitor coderabbit", "coderabbit alerts", "coderabbit dashboard".
| Configure CI/CD for Cohere integrations with GitHub Actions and automated testing. Use when setting up automated testing for Chat/Embed/Rerank, configuring CI pipelines, or testing Cohere-powered applications. Trigger with phrases like "cohere CI", "cohere GitHub Actions", "cohere automated tests", "CI cohere", "cohere pipeline".
| Optimize Cohere costs through model selection, token budgets, and usage monitoring. Use when analyzing Cohere billing, reducing API costs, or implementing usage monitoring and budget alerts. Trigger with phrases like "cohere cost", "cohere billing", "reduce cohere costs", "cohere pricing", "cohere expensive", "cohere budget".
| Deploy Cohere-powered applications to Vercel, Fly.io, and Cloud Run. Use when deploying Cohere API v2 apps to production, configuring platform-specific secrets, or setting up deployment pipelines. Trigger with phrases like "deploy cohere", "cohere Vercel", "cohere production deploy", "cohere Cloud Run", "cohere Fly.io".
| Configure Cohere across development, staging, and production environments. Use when setting up multi-environment deployments, configuring per-environment API keys, model selection, and rate limit strategies. Trigger with phrases like "cohere environments", "cohere staging", "cohere dev prod", "cohere environment setup", "cohere config by env".
| Set up comprehensive observability for Cohere API v2 with metrics, traces, and alerts. Use when implementing monitoring for Chat/Embed/Rerank operations, setting up dashboards, or configuring alerts for Cohere integrations. Trigger with phrases like "cohere monitoring", "cohere metrics", "cohere observability", "monitor cohere", "cohere alerts", "cohere tracing".
| Execute Cohere production deployment checklist and rollback procedures. Use when deploying Cohere integrations to production, preparing for launch, or implementing go-live procedures for Cohere-powered apps. Trigger with phrases like "cohere production", "deploy cohere", "cohere go-live", "cohere launch checklist".
Collect comprehensive infrastructure performance metrics across compute, storage, network, containers, load balancers, and databases. Use when monitoring system performance or troubleshooting infrastructure issues. Trigger with phrases like "collect infrastructure metrics", "monitor server performance", or "track system resources".
|
| Integrate CoreWeave deployments into CI/CD pipelines with GitHub Actions. Use when automating container builds, deploying inference services from CI, or validating GPU manifests in pull requests. Trigger with phrases like "coreweave CI", "coreweave github actions", "coreweave pipeline", "automate coreweave deploy".
| Deploy KServe InferenceService on CoreWeave with autoscaling and GPU scheduling. Use when serving ML models with KServe, configuring scale-to-zero, or deploying production inference endpoints on CoreWeave. Trigger with phrases like "coreweave inference service", "coreweave kserve", "coreweave model serving", "deploy model on coreweave".
| Deploy inference services on CoreWeave with Helm charts and Kustomize. Use when deploying multi-model inference, managing GPU deployments at scale, or templating CoreWeave manifests. Trigger with phrases like "deploy coreweave", "coreweave helm", "coreweave kustomize", "coreweave deployment patterns".
| Deploy a GPU workload on CoreWeave with kubectl. Use when running your first GPU job, testing inference, or verifying CoreWeave cluster access. Trigger with phrases like "coreweave hello world", "coreweave first deploy", "coreweave gpu test", "run on coreweave".
| Configure CoreWeave Kubernetes Service (CKS) access with kubeconfig and API tokens. Use when setting up kubectl access to CoreWeave, configuring CKS clusters, or authenticating with CoreWeave cloud services. Trigger with phrases like "install coreweave", "setup coreweave", "coreweave kubeconfig", "coreweave auth", "connect to coreweave".
| Set up local development workflow for CoreWeave GPU deployments. Use when building containers locally, testing YAML manifests, or iterating on model serving configurations before deploying. Trigger with phrases like "coreweave dev setup", "coreweave local testing", "develop for coreweave", "coreweave container build".
| Migrate ML workloads from AWS/GCP/Azure to CoreWeave GPU cloud. Use when moving inference services from hyperscaler GPU instances, migrating training pipelines, or evaluating CoreWeave vs cloud GPU costs. Trigger with phrases like "migrate to coreweave", "coreweave migration", "move from aws to coreweave", "coreweave vs aws gpu".
| Configure CoreWeave across development, staging, and production environments. Use when setting up multi-environment GPU infrastructure, separating namespaces, or managing per-environment GPU quotas. Trigger with phrases like "coreweave environments", "coreweave staging", "coreweave multi-env", "coreweave namespace setup".
| Set up GPU monitoring and observability for CoreWeave workloads. Use when implementing GPU metrics dashboards, configuring alerts, or tracking inference latency and throughput. Trigger with phrases like "coreweave monitoring", "coreweave observability", "coreweave gpu metrics", "coreweave grafana".
| Reference architecture for CoreWeave GPU cloud deployments. Use when designing ML infrastructure, planning multi-model serving, or establishing CoreWeave deployment standards. Trigger with phrases like "coreweave architecture", "coreweave design", "coreweave infrastructure", "coreweave best practices".
| Secure CoreWeave deployments with RBAC, network policies, and secrets management. Use when hardening GPU workloads, managing model access, or configuring namespace isolation. Trigger with phrases like "coreweave security", "coreweave rbac", "secure coreweave", "coreweave secrets".
| Upgrade CoreWeave deployments and migrate between GPU types. Use when migrating from A100 to H100, upgrading CUDA versions, or updating inference server versions. Trigger with phrases like "upgrade coreweave", "coreweave gpu migration", "coreweave cuda upgrade", "migrate coreweave".
| Monitor CoreWeave cluster events and GPU workload status. Use when tracking pod lifecycle events, monitoring GPU utilization, or alerting on inference service health changes. Trigger with phrases like "coreweave events", "coreweave monitoring", "coreweave pod alerts", "coreweave gpu monitoring".
| Cost Optimization Analyzer - Auto-activating skill for AWS Skills. Part of the AWS Skills skill category.
| This skill enables Claude to monitor and analyze CPU usage patterns within applications. It helps identify CPU hotspots, analyze algorithmic complexity, and detect blocking operations. Use this skill when the user asks to "monitor CPU usage", "optimize CPU performance", "analyze CPU load", or "find CPU bottlenecks". It assists in identifying inefficient loops, regex performance issues, and provides optimization recommendations. This skill is designed for improving application performance by addressing CPU-intensive operations.
| Execute this skill enables AI assistant to create intelligent alerting rules for proactive performance monitoring. it is triggered when the user requests to "create alerts", "define monitoring rules", or "set up alerting". the skill helps define thresholds, rou... Use when generating or creating new content. Trigger with phrases like 'generate', 'create', or 'scaffold'.
| Deploy applications to Kubernetes with production-ready manifests. Supports Deployments, Services, Ingress, HPA, ConfigMaps, Secrets, StatefulSets, and NetworkPolicies. Includes health checks, resource limits, auto-scaling, and TLS termination. Use when working with creating kubernetes deployments. Trigger with 'creating', 'kubernetes', 'deployments'.
| Configure BYOK API keys for OpenAI, Anthropic, Google, Azure, and custom models in Cursor. Triggers on "cursor api key", "cursor openai key", "cursor anthropic key", "own api key cursor", "BYOK cursor", "cursor azure key".
| Configure Customer.io CI/CD integration with automated testing. Use when setting up GitHub Actions, integration test suites, or pre-commit validation for Customer.io code. "customer.io pipeline", "customer.io automated testing".
| Deploy Customer.io integrations to production cloud platforms. Use when deploying to Cloud Run, Vercel, AWS Lambda, or Kubernetes with proper secrets management and health checks. "customer.io kubernetes", "customer.io lambda", "customer.io vercel".
| Configure Customer.io multi-environment setup with workspace isolation. Use when setting up dev/staging/prod workspaces, environment-aware clients, or Kubernetes config overlays. "customer.io dev prod", "customer.io workspace isolation".
| Set up Customer.io monitoring and observability. Use when implementing metrics, structured logging, alerting, or Grafana dashboards for Customer.io integrations. "customer.io dashboard", "customer.io alerts", "customer.io observability".
| Execute Customer.io production deployment checklist. Use when preparing for production launch, auditing integration quality, or performing pre-launch validation. "deploy customer.io", "customer.io go-live", "customer.io launch".
| Dagster Pipeline Creator - Auto-activating skill for Data Pipelines. Part of the Data Pipelines skill category.
| Data Augmentation Pipeline - Auto-activating skill for ML Training. Part of the ML Training skill category.
| This skill empowers Claude to preprocess and clean data using automated pipelines. It is designed to streamline data preparation for machine learning tasks, implementing best practices for data validation, transformation, and error handling. Claude should use this skill when the user requests data preprocessing, data cleaning, ETL tasks, or mentions the need for automated pipelines for data preparation. Trigger terms include "preprocess data", "clean data", "ETL pipeline", "data transformation", and "data validation". The skill ensures data quality and prepares it for effective analysis and model training.
| This skill enables Claude to monitor database transactions for performance and lock issues using the database-transaction-monitor plugin. It is triggered when the user requests transaction monitoring, lock detection, or rollback rate analysis for databases. Use this skill when the user mentions "monitor database transactions," "detect long-running transactions," "identify lock contention," "track rollback rates," or asks about "transaction anomalies." The skill leverages the `/txn-monitor` command to provide real-time alerts and insights into database health.
| Configure Databricks CI/CD integration with GitHub Actions and Asset Bundles. Use when setting up automated testing, configuring CI pipelines, or integrating Databricks deployments into your build process. Trigger with phrases like "databricks CI", "databricks GitHub Actions", "databricks automated tests", "CI databricks", "databricks pipeline".
| Use when building ML pipelines, training models, or deploying to production. Trigger with phrases like "databricks ML", "mlflow training", "databricks model", "feature store", "model registry".
| Optimize Databricks costs with cluster policies, spot instances, and monitoring. Use when reducing cloud spend, implementing cost controls, or analyzing Databricks usage costs. Trigger with phrases like "databricks cost", "reduce databricks spend", "databricks billing", "databricks cost optimization", "cluster cost".
| Deploy Databricks jobs and pipelines with Declarative Automation Bundles. Use when deploying jobs to different environments, managing deployments, or setting up deployment automation. Trigger with phrases like "databricks deploy", "asset bundles", "databricks deployment", "deploy to production", "bundle deploy".
| Configure Databricks across development, staging, and production environments. Use when setting up multi-environment deployments, configuring per-environment secrets, or implementing environment-specific Databricks configurations. Trigger with phrases like "databricks environments", "databricks staging", "databricks dev prod", "databricks environment setup", "databricks config by env".
| Set up comprehensive observability for Databricks with metrics, traces, and alerts. Use when implementing monitoring for Databricks jobs, setting up dashboards, or configuring alerting for pipeline health. Trigger with phrases like "databricks monitoring", "databricks metrics", "databricks observability", "monitor databricks", "databricks alerts", "databricks logging".
| Execute Databricks production deployment checklist and rollback procedures. Use when deploying Databricks jobs to production, preparing for launch, or implementing go-live procedures. Trigger with phrases like "databricks production", "deploy databricks", "databricks go-live", "databricks launch checklist".
| Configure Deepgram CI/CD integration for automated testing and deployment. Use when setting up continuous integration pipelines, automated testing, or deployment workflows for Deepgram integrations. "deepgram github actions", "deepgram automated testing".
| Optimize Deepgram costs and usage for budget-conscious deployments. Use when reducing transcription costs, implementing usage controls, or optimizing pricing tier utilization. "deepgram budget", "optimize deepgram usage", "deepgram billing".
| Deploy Deepgram integrations to production environments. Use when deploying to cloud platforms, configuring containers, or setting up Deepgram in Docker/Kubernetes/serverless. "deepgram production deploy", "deepgram cloud run", "deepgram lambda".
| Deep dive into migrating to Deepgram from other transcription providers. Use when migrating from AWS Transcribe, Google Cloud STT, Azure Speech, OpenAI Whisper, AssemblyAI, or Rev.ai to Deepgram. "deepgram from AWS", "deepgram from Google", "replace whisper with deepgram".
| Set up comprehensive observability for Deepgram integrations. Use when implementing monitoring, setting up dashboards, or configuring alerting for Deepgram integration health. "monitor deepgram", "deepgram alerts", "deepgram dashboard".