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Access and test protected Vercel deployments with agent-browser using an existing Vercel identity and a short-lived authentication token. Keep deployment protection enabled while diagnosing login redirects, environment access mismatches, and application authentication errors.
Use when a practitioner needs to classify the risk level of an upcoming Salesforce deployment, define rollback trigger conditions, or assign decision authority before the release window opens. Triggers: 'how risky is this release', 'what needs rollback planning', 'classify deployment risk', 'pre-deployment risk checklist', 'rollback decision authority', 'high risk metadata types'. NOT for executing the deployment itself, writing CI/CD pipeline code, or troubleshooting failures after a deployment has already run — use admin/change-management-and-deployment for execution planning and devops/deployment-error-troubleshooting for post-failure diagnosis.
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Monitor a specific Vercel deployment in Codex and report when it becomes ready or fails. The skill identifies the exact deployment, checks its recorded state, and includes dashboard and preview links in the notification.
Check Bicep infrastructure definitions before deploying to Azure. Validate syntax, preview resource changes, check deployment permissions, and produce a Markdown report of issues and recommendations using Azure CLI or Azure Developer CLI workflows.
Interactive guided deployment flow for Azure OpenAI models with full customization control. Step-by-step selection of model version, SKU (GlobalStandard/Standard/ProvisionedManaged), capacity, RAI policy (content filter), and advanced options (dynamic quota, priority processing, spillover). USE FOR: custom deployment, customize model deployment, choose version, select SKU, set capacity, configure content filter, RAI policy, deployment options, detailed deployment, advanced deployment, PTU deployment, provisioned throughput. DO NOT USE FOR: quick deployment to optimal region (use preset).
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Unified Azure OpenAI model deployment skill with intelligent intent-based routing. Handles quick preset deployments, fully customized deployments (version/SKU/capacity/RAI policy), and capacity discovery across regions and projects. USE FOR: deploy model, deploy gpt, create deployment, model deployment, deploy openai model, set up model, provision model, find capacity, check model availability, where can I deploy, best region for model, capacity analysis. DO NOT USE FOR: listing existing deployments (use foundry_models_deployments_list MCP tool), deleting deployments, agent creation (use agent/create), project creation (use project/create).
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Interactive guided deployment flow for Azure OpenAI models with full customization control. Step-by-step selection of model version, SKU (GlobalStandard/Standard/ProvisionedManaged), capacity, RAI policy (content filter), and advanced options (dynamic quota, priority processing, spillover). USE FOR: custom deployment, customize model deployment, choose version, select SKU, set capacity, configure content filter, RAI policy, deployment options, detailed deployment, advanced deployment, PTU deployment, provisioned throughput. DO NOT USE FOR: quick deployment to optimal region (use preset).
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Unified Azure OpenAI model deployment skill with intelligent intent-based routing. Handles quick preset deployments, fully customized deployments (version/SKU/capacity/RAI policy), and capacity discovery across regions and projects. USE FOR: deploy model, deploy gpt, create deployment, model deployment, deploy openai model, set up model, provision model, find capacity, check model availability, where can I deploy, best region for model, capacity analysis. DO NOT USE FOR: listing existing deployments (use foundry_models_deployments_list MCP tool), deleting deployments, agent creation (use agent/create), project creation (use project/create).
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Use when planning, reviewing, or troubleshooting Salesforce metadata releases and admin deployment processes. Triggers: 'change set', 'deployment plan', 'rollback', 'DevOps Center', 'SFDX deploy', 'release checklist', 'production deployment', 'validation only deploy', 'quick deploy', 'test level', 'rollbackOnError', 'destructive changes', 'package.xml for an admin release', 'deployment status'. NOT for change set upload mechanics — use devops/change-set-deployment. NOT for scoring release risk up front — use admin/deployment-risk-assessment.
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Use when working with Aws Codedeploy — aWS CodeDeploy application and deployment management. Covers applications, deployment groups, deployment history, deployment configurations, instance health, target revisions, and rollback settings. Use when inspecting deployments, debugging failed deployments, reviewing deployment strategies, or auditing deployment group configurations.
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Set up Kubernetes deployments with ArgoCD or Flux CD using GitOps, an approach that stores the desired system configuration in Git. Configure automatic synchronization, staged rollouts, secret management, and deployment workflows across environments or clusters.
Generate Kubernetes YAML files that define how applications run, connect, receive configuration, and use persistent storage. Include resource limits, health checks, security settings, labels, and validation guidance for production-oriented deployments.
Deploy Model Garden models to Agent Platform endpoints, the addresses used for model requests. Check supported hardware and estimated hourly costs, inspect deployment progress, test predictions, and remove deployed resources after use.
Plan how to serve a model on Amazon SageMaker AI. The skill uses model type, traffic, response time, and cost constraints to recommend a deployment route, check instance quotas, and coordinate specialized setup and deployment skills.
Pre-deployment privacy compliance checklist for AI/ML systems covering DPIA completion, lawful basis verification, transparency notices, human oversight mechanisms, bias testing, and post-deployment monitoring setup. Keywords: AI deployment, privacy checklist, go-live, model deployment, compliance gate.
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Inspect Vercel websites and apps, check deployment status, and read build and runtime logs to investigate production errors or failed releases. The skill also supports project management and deployment through Vercel’s official MCP server.
Prepare and verify application deployments to Azure Container Apps using azd and Bicep. Review environment settings, remote builds, identity permissions, and deployment changes, then check running revisions, service health, and frontend-to-backend connections.
Deploy apps and websites to Vercel and get a URL to share. The skill selects a deployment method based on Git integration and authentication, using preview deployments by default and production deployments when explicitly requested.
Create CI/CD configurations, which automate build, test, and deployment steps, alongside Terraform infrastructure templates and Kubernetes deployment files. Generate health-check and rollback instructions to review before applying changes to cloud infrastructure or application environments.
Prepare a graded Claude Managed Agent for recurring schedules, event triggers, or on-demand use. Validate timing, build deployment request data, optionally include self-evaluation for each run, and document the next steps in a roadmap.
Deploy SageMaker endpoints, the services that answer model requests, with automatic capacity scaling, CloudWatch alarms, and resource tags. Supports real-time and asynchronous inference, scaling down to zero instances, deployment checks, and resource cleanup.
Surfaces PAC CLI upload errors and Dataverse async operation errors, pattern-matches against a known failure catalog, and optionally auto-fixes identified issues. Use when asked to: "diagnose deployment", "debug deployment", "deployment failed", "show deployment errors", "fix deployment issues", "show upload logs", "why did my deploy fail", or "troubleshoot upload".
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Generate GitHub Actions deployment workflows for automated deployment to staging and production environments on cloud platforms (AWS, GCP, Azure). Use when setting up continuous deployment pipelines, creating deployment automation, or configuring multi-environment deployment strategies. Includes templates for environment-specific deployments with approval gates, secrets management, and rollback capabilities.
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World-class continuous integration and deployment - GitHub Actions, GitLab CI, deployment strategies, and the battle scars from pipelines that broke productionUse when "ci/cd, cicd, pipeline, github actions, gitlab ci, circleci, jenkins, workflow, deployment, deploy, release, blue green, canary, rollback, build, test automation, continuous integration, continuous deployment, cicd, github-actions, gitlab-ci, deployment, automation, devops, pipelines, continuous-integration, continuous-deployment" mentioned.
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