'deployment' 검색 결과

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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• Restricting communication between application components
• Giving ServiceAccounts only the permissions they need
• Running Pods with restricted privileges

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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• Automating builds and tests in GitLab
• Reviewing caches to improve pipeline performance
• Separating staging and production deployments

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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• Prepare a production deployment plan
• Define rollback steps and decision criteria
• Set post-deployment health checks

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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• Setting up a new Python project for AWS CDK
• Preparing a virtual environment and dependencies
• Generating templates and reviewing deployment changes

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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• Creating an Azure Developer CLI project structure
• Reviewing azure.yaml against Bicep or Terraform definitions
• Organizing environment settings and secret handling

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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• Migrating a legacy SGLang cookbook page
• Moving deployment choices into model configuration
• Transferring reproducible benchmark measurements

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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• Explaining a system overview to stakeholders
• Documenting relationships between apps and databases
• Mapping internal components of a feature

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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• Prepare checks for an upcoming production release
• Review a release with database migrations
• Verify feature flags and CI status before deployment

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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• Setting up automated checks for a new project
• Reworking fragile pipeline configuration
• Moving between GitHub Actions and GitLab CI

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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• Publish a web application on Render
• Prepare deployment configuration for multiple services
• Set up databases and scheduled jobs

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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• Share an app through a preview URL
• Deploy a project folder or existing tarball
• Publish to production on explicit request

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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• Designing product search with meaning-based matching and attribute filters
• Planning result reranking and AI quality checks
• Preparing AlloyDB and Cloud Run deployment guidance

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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• Reviewing production readiness
• Planning monitoring and logging
• Improving incident response procedures

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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• Plan an AI agent architecture on Google Cloud
• Choose between single-agent and multi-agent designs
• Prepare deployment code and step-by-step instructions

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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• Deploying an existing application to GKE for the first time
• Creating a Dockerfile and registering a container image
• Preparing deployment configuration with health checks

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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• Designing and locally validating Terraform for GCP
• Assessing infrastructure plans before deployment
• Importing and deploying Application Design Center templates

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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• Investigating repeatedly crashing containers
• Reviewing deployment and resource settings
• Troubleshooting service connectivity and DNS

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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• Automating builds, tests, and deployments
• Reducing pipeline time with caching and parallel jobs
• Planning gradual releases and rollbacks

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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• Setting up automated build and deployment pipelines
• Packaging applications with Docker
• Managing Kubernetes and cloud infrastructure as code

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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• Prepare checks for an upcoming production release
• Review a release with database migrations
• Verify feature flags and CI status before deployment

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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• Setting API keys for a Convex deployment
• Reading secrets from actions
• Managing separate values for each deployment

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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• Checking the target environment before a Convex deployment
• Requiring explicit consent for each production operation
• Keeping a session read-only during an investigation

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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• Set up a synthetic data generation environment on MicroK8s
• Deploy an Azure AKS environment with OSMO and inference services
• Check GPU quotas and capacity before scaling

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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• Check communication readiness after deployment
• Inspect transport setup before benchmarking
• Investigate suspected communication bottlenecks