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Guides Kubernetes security configuration with network restrictions, Pod execution rules, and role-based access control. Use its configuration examples to separate workloads, limit permissions, and enforce deployment requirements across a cluster.
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Build GitLab workflows that automate building, testing, and deployment. The skill covers pipeline stages, dependency caching, runners that execute jobs, and manual approval before production deployment, with examples for Docker, Kubernetes, and Terraform.
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Create rollout plans for application and infrastructure changes. Organize readiness checks, deployment steps, health indicators, rollback procedures, and stakeholder communications, adapting the detail to the target environment, change type, and level of risk.
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Set up a Python project with AWS CDK, a toolkit for defining cloud infrastructure in code. Follow guidance on prerequisites, AWS credentials, virtual environments, dependencies, template generation, deployment preparation, and reviewing changes before deployment.
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Create, review, migrate, or troubleshoot Azure Developer CLI projects. Align application settings, infrastructure definitions, deployment environments, secrets, and automated delivery workflows while checking that configuration files and deployment requirements agree.
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Convert an existing SGLang cookbook page to shared templates driven by model configuration. Preserve its deployment commands and interactive choices, transfer eligible benchmark measurements, and check equivalence, links, and rendered behavior.
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Turn a codebase into architecture documentation with Mermaid diagrams. Using the C4 model, a way to describe systems at different levels of detail, the skill explains external relationships, applications, internal components, deployment, and request flows.
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Prepare a deployment checklist covering tests, approvals, database changes, release steps, and monitoring. Tailor it to the release and document rollback triggers, the conditions for reverting a change, before production deployment begins.
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Inspect project files to identify languages and build tools, then generate starter GitHub Actions or GitLab CI workflows. Set up automated checks, caching, and deployment stages that reflect the project’s commands and target environments.
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Deploy applications to Render by inspecting code and preparing build settings, environment variables, and required services. Generates render.yaml deployment configuration or creates services through MCP, then checks deployment status, logs, and basic health.
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Deploy apps and websites to Vercel and get a URL to share. The workflow defaults to preview deployments, supports production when explicitly requested, and includes an alternative deployment script when CLI credentials are unavailable.
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Plan and build AI agents on Google Cloud, from gathering requirements to choosing a single-agent or multi-agent design. Produce architecture diagrams, deployment code and instructions, and validation plans tailored to the workload.
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Review Google Cloud operations and develop guidance for monitoring, incident response, resource sizing, and automated deployments. Use framework-based questions and checklists to identify gaps in production readiness and plan ongoing operational improvements.
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Design hybrid search on AlloyDB by combining meaning-based search, keywords, and SQL filters. Turn requirements into architecture diagrams, database setup scripts, deployment guidance, and validation plans for search applications on Google Cloud.
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Guides an application’s first deployment to GKE, Google’s managed Kubernetes service. Covers application assessment, container creation, image storage, deployment configuration, security settings, health checks, and verification that the application is running.
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Design Google Cloud infrastructure with Terraform, validate it locally, and assess the planned changes for security, cost, and reliability. The skill then imports templates into Application Design Center, deploys them, investigates failures, and checks the deployed services.
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Helps deploy, manage, and troubleshoot applications on Kubernetes using kubectl and Helm. Covers container logs, resource settings, network access, and deployment strategies to investigate failures and review how applications run.
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Design automated build, test, and deployment workflows for GitHub Actions, GitLab CI/CD, and Jenkins. Covers job ordering, dependency caching, secret handling, and gradual releases that use health checks before expanding deployment.
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Create Dockerfiles, automated build and deployment pipelines, Kubernetes configurations, and infrastructure templates. The skill also prepares deployment checks, rollback procedures, and incident response runbooks to support application releases and production operations.
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Prepare a deployment checklist covering tests, approvals, database changes, release steps, and monitoring. Tailor it to the release and document rollback triggers, the conditions for reverting a change, before production deployment begins.
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Configure Convex deployment environment variables for secrets such as API keys, read them from application actions, and check the configured values while keeping secrets out of source code and Git.
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Helps prevent changes to the wrong Convex environment by identifying and announcing the target before each operation. Requires fresh consent for production actions and enforces a session-wide read-only mode when requested.
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Set up NVIDIA infrastructure for synthetic data generation on MicroK8s or Azure AKS. Coordinate Kubernetes, inference services, and OSMO workflow execution, with prerequisite checks, deployment validation, capacity planning, and failure investigation before workloads are retried.
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Check whether a Dynamo deployment is ready for disaggregated serving, where input processing and token generation run separately. Read-only probes inspect transport settings, GPU networking capabilities, and NIXL transfer-test readiness, then report gaps and next steps.
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