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Deploy with unified build/test/deploy intent across providers using .agent-loom/deploy.yml and per-provider adapters. Load when the user asks to deploy, ship to preview, release to production, or run a provider-agnostic deploy flow. Also triggers on "deploy anywhere", "deploy to Vercel", "deploy with GitHub Actions", "preview deploy", or "ship this". Runs preflight before any deploy — stops on missing secrets. Pairs with ci-cd-and-automation for pipeline design. Ships vercel and github-actions adapters; extensible adapter interface.

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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.

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• Validating Bicep files before deployment
• Previewing resource creation, modification, and deletion
• Checking deployment permissions

Assess whether source code is ready to deploy to Azure — the check BEFORE infrastructure work. Evaluates build health, app completeness, dependencies and local services, stack compatibility, and deployment feasibility. Answers questions about what your app needs before it can be deployed — frameworks, dependencies, and configuration. Checks whether dependencies are compatible and identifies deployment blockers and unsupported frameworks. WHEN: "evaluate my repo", "is my app ready to deploy", "what does my app need to deploy", "what do I need before deploying", "does my app need", "can I ship this to Azure", "scan my repo for issues", "is this app deployable", "check if my app is ready for Azure", "do I need a Dockerfile", "what's blocking my deployment", "are there any blockers", "are my dependencies compatible", "does Azure support my framework", "what needs to change before deploying", "check my app configuration".

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Assess whether source code is ready to deploy to Azure — the check BEFORE infrastructure work. Evaluates build health, app completeness, dependencies and local services, stack compatibility, and deployment feasibility. Answers questions about what your app needs before it can be deployed — frameworks, dependencies, and configuration. Checks whether dependencies are compatible and identifies deployment blockers and unsupported frameworks. WHEN: "evaluate my repo", "is my app ready to deploy", "what does my app need to deploy", "what do I need before deploying", "does my app need", "can I ship this to Azure", "scan my repo for issues", "is this app deployable", "check if my app is ready for Azure", "do I need a Dockerfile", "what's blocking my deployment", "are there any blockers", "are my dependencies compatible", "does Azure support my framework", "what needs to change before deploying", "check my app configuration".

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Plan and coordinate the deployment of a model to Amazon SageMaker AI. Use this skill whenever the user wants to deploy, host, serve, or expose a model on SageMaker or AWS — including phrases like "deploy a model", "host this LLM on AWS", "serve this embedding model", "deploy a reranker", "deploy a text-to-image / diffusion model", "host this for async inference", "create an endpoint", "serve my fine-tuned model", or any request that involves making a model available for inference on AWS. Use this even when the user is vague (e.g. "I just want to get this running on AWS, you figure it out"). Works for text-generation LLMs, embedding models, rerankers, classifiers, text-to-image / diffusion models — picks the right serving stack and chooses between real-time and async inference. This is the entry-point skill for SageMaker deployment work — it asks clarifying questions, picks a deployment pathway, and coordinates the other deployment skills.

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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.

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• Testing a protected Preview deployment
• Diagnosing Vercel login redirects and protection errors
• Identifying access rules needed for Production testing

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

[omh] Release rollout needing health signals: release checklist, deploy decision, health signals, rollback gate, and post-deploy status. Use when the user says: deploy-and-monitor, deploy and monitor, deploy monitor, deployment monitoring, release monitor, post deploy, post-deploy, rollback.

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Triggers a Power Platform Pipeline deployment run for a Power Pages solution. Selects a target stage, validates the package, optionally configures deployment settings (environment variables, connection references), then deploys and polls for completion. Use when asked to: "deploy pipeline", "run pipeline", "trigger deployment", "deploy to staging", "deploy to production", "run power platform pipeline", "deploy solution via pipeline", "promote solution", "push to staging", "push to production".

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Run a pre and post-deployment checklist. Use when the user says "deploy to production", "deployment checklist", "release checklist", "pre-deploy checks", "is this ready to deploy", "deployment readiness", "reduce deployment risk", "change management", or is about to ship code to production and wants to reduce failure risk - even if they don't explicitly say "deployment checklist".

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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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Design, implement, or audit a Change Advisory Board (CAB) process for Salesforce deployments: change classification (standard, normal, emergency), required approvals, deployment gate sequencing, and integration with external ITSM tooling. NOT for scoring how risky one release is - use admin/deployment-risk-assessment. NOT for executing or troubleshooting the deployment itself - use admin/change-management-and-deployment. Also covers: the CAB charter (membership, quorum, decision rights), the CAB decision record artefact and its linter, the change classification matrix, the deployOptions a CAB decision pins (checkOnly, testLevel, rollbackOnError, ignoreWarnings, runTests), the 10-day quick-deploy validity window, the deploy-window vs freeze-register check, and the emergency (ECAB) path with its mandatory retrospective.

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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.

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• Investigating a failed deployment
• Checking the status of a website release
• Reading logs to investigate production errors

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.

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• Wait for a PR preview deployment to become ready
• Monitor the deployment for a specific commit
• Receive a notification when deployment succeeds or fails

Audit Solidity contracts in Foundry projects before deployment using code analysis, randomized tests, manual review, and private key checks. Produces a JSON report with findings, remediation, test coverage, and a pass or fail deployment decision.

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• Auditing Solidity contracts before deployment
• Adding fuzz and invariant tests for contracts that move funds
• Reviewing private key storage and deployment scripts

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

Audit Solidity contracts in Foundry projects before deployment using code analysis, randomized tests, manual review, and private key checks. Produces a JSON report with findings, remediation, test coverage, and a pass or fail deployment decision.

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• Auditing Solidity contracts before deployment
• Adding fuzz and invariant tests for contracts that move funds
• Reviewing private key storage and deployment scripts

Deploy ClawHub to production or publish a stable CLI release to npm. Check prerequisites for the exact commit, run the release workflow, and verify the deployed behavior or published version with traceable release evidence.

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• Deploy ClawHub to production
• Publish a stable ClawHub CLI version
• Check release prerequisites for a specific commit

Turn an idea, existing code, or a previous Codex conversation into a runnable OpenAI Agents SDK app. Supports local verification, focused evaluations of agent behavior, and local deployment through the Deployment Manager.

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• Build a runnable agent prototype from an idea
• Adapt existing code into an agent app
• Turn a previous Codex conversation into an app

Deploy applications to Render by inspecting the code and preparing service settings. Generate a render.yaml configuration or create services through MCP, an agent tool connection, then check deployment status, logs, and basic service health.

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• Publish a web application or static site on Render
• Prepare deployment settings for apps with databases or workers
• Generate and validate render.yaml

Deploy web projects to Netlify with its command-line tool. The skill checks login status, links or creates a site, detects build settings, and publishes a preview or production version with deployment URLs and log links.

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• Publishing a web project to Netlify
• Sharing a preview URL for review
• Linking a Git repository to an existing Netlify site

Take an existing pull request through automated check results, user approval, merging, deployment verification, and a production health check. Report outcomes and timings, with investigation or rollback options when deployment problems appear.

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• Merge a pull request after CI and test checks
• Verify deployment after a merge
• Check production status, response time, and page content

Guides deployment of Open Harness on Vercel, from collecting credentials and setting environment variables to checking the first session. Separates a basic setup from a fuller setup with GitHub repository access.

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• Deploying a personal copy of Open Harness
• Identifying credentials needed for a basic deployment
• Adding GitHub repository access

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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