Vercel Deploy Skill for Codex

Deploy applications and websites to Vercel with a claimable preview. Use when the user requests deployment actions like "deploy my app", "deploy and give me the link", "push this live", or "create a preview deployment". No authentication required - returns preview URL and claimable deployment link.

789 tokens

context cost

SKILL.md, read when the skill is used

files

ships runnable scripts

copies elsewhere

how many repositories repackaged it

stars on the repo

on the repository, not the skill itself

Install

one command, takes just this skill from the repository

npx skills add https://github.com/vercel-labs/vercel-deploy-codex-skill --skill vercel-deploy

What comes with it

7 348 bytes besides the instruction

assets/vercel.svg

openai.yaml

scripts/deploy.sh

The instruction itself

10 sections, as written by the author

Vercel Deploy

Deploy any project to Vercel instantly. No authentication required.

How It Works

Packages your project into a .tar.gz (excludes node_modules and .git)
Auto-detects framework from package.json
Uploads to deployment service
Returns Preview URL (live site) and Claim URL (transfer to your Vercel account)

Usage

bash scripts/deploy.sh [path]

Arguments:

path - Directory to deploy, or a .tgz file (defaults to current directory)

If you pass a directory, the script will create a .tar.gz before upload.

Examples:

# Deploy current directory
bash scripts/deploy.sh

# Deploy specific project
bash scripts/deploy.sh /path/to/project

# Deploy existing tarball
bash scripts/deploy.sh /path/to/project.tgz

Packaging Rules

Exclude node_modules and .git
If no package.json, keep framework as null
For static HTML with a single .html file, rename it to index.html before packaging

Output

Preparing deployment...
Creating deployment package...
Deploying...
✓ Deployment successful!

Preview URL: https://skill-deploy-abc123.vercel.app
Claim URL:   https://vercel.com/claim-deployment?code=...

The script also outputs JSON to stdout for programmatic use.

{
  "previewUrl": "https://skill-deploy-abc123.vercel.app",
  "claimUrl": "https://vercel.com/claim-deployment?code=...",
  "deploymentId": "dpl_...",
  "projectId": "prj_..."
}

Framework Detection

The script auto-detects frameworks from package.json. Supported frameworks include:

React: Next.js, Gatsby, Create React App, Remix, React Router
Vue: Nuxt, Vitepress, Vuepress, Gridsome
Svelte: SvelteKit, Svelte, Sapper
Other Frontend: Astro, Solid Start, Angular, Ember, Preact, Docusaurus
Backend: Express, Hono, Fastify, NestJS, Elysia, h3, Nitro
Build Tools: Vite, Parcel
And more: Blitz, Hydrogen, RedwoodJS, Storybook, Sanity, etc.

For static HTML projects (no package.json), framework is set to null.

Static HTML Projects

For projects without a package.json:

If there's a single .html file not named index.html, it gets renamed automatically
This ensures the page is served at the root URL (/)

Present Results to User

Always show both URLs:

✓ Deployment successful!

Preview URL: https://skill-deploy-abc123.vercel.app
Claim URL:   https://vercel.com/claim-deployment?code=...

View your site at the Preview URL.
To transfer this deployment to your Vercel account, visit the Claim URL.

Troubleshooting

Network Egress Error

If deployment fails due to network restrictions, tell the user:

Deployment failed due to network restrictions. To fix this:

1. Allow outbound access to *.vercel.com
2. Try deploying again

Other skills for the same job

different authors, same section of the catalogue

Deploying To Production

by ZhanlinCui

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Automate creating a GitHub repository and deploying a web project to Vercel. Use when the user asks to deploy a website/app to production, publish a project, or set up GitHub + Vercel deployment.

Deploy applications and websites to Vercel. Use this skill when the user requests deployment actions such as "Deploy my app", "Deploy this to production", "Create a preview deployment", "Deploy and give me the link", or "Push this live". No authentication required - returns preview URL and claimable deployment link.

793 tokens scripts

Azure Kubernetes Automatic Readiness

by microsoft

vendor ×3

Assess Kubernetes workloads and cluster configuration for AKS Automatic compatibility. Identifies incompatibilities, generates fixes, and guides migration from AKS Standard to AKS Automatic. WHEN: migrate to AKS Automatic, check AKS Automatic readiness, validate manifests for Automatic, assess cluster for Automatic compatibility, fix deployment for Automatic compatibility, identify AKS Automatic migration blockers, is my cluster ready for AKS Automatic.

Discovers available Azure OpenAI model capacity across regions and projects. Analyzes quota limits, compares availability, and recommends optimal deployment locations based on capacity requirements. USE FOR: find capacity, check quota, where can I deploy, capacity discovery, best region for capacity, multi-project capacity search, quota analysis, model availability, region comparison, check TPM availability. DO NOT USE FOR: actual deployment (hand off to preset or customize after discovery), quota increase requests (direct user to Azure Portal), listing existing deployments.

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).

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).

Intelligently deploys Azure OpenAI models to optimal regions by analyzing capacity across all available regions. Automatically checks current region first and shows alternatives if needed. USE FOR: quick deployment, optimal region, best region, automatic region selection, fast setup, multi-region capacity check, high availability deployment, deploy to best location. DO NOT USE FOR: custom SKU selection (use customize), specific version selection (use customize), custom capacity configuration (use customize), PTU deployments (use customize).

This skill should be used when working with LaminDB, an open-source data framework for biology that makes data queryable, traceable, reproducible, and FAIR. Use when managing biological datasets (scRNA-seq, spatial, flow cytometry, etc.), tracking computational workflows, curating and validating data with biological ontologies, building data lakehouses, or ensuring data lineage and reproducibility in biological research. Covers data management, annotation, ontologies (genes, cell types, diseases, tissues), schema validation, integrations with workflow managers (Nextflow, Snakemake) and MLOps platforms (W&B, MLflow), and deployment strategies.

3.8k tokens

How to use it

Copy the folder

Take vercel-labs/vercel-deploy from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

The agent identifies a skill by the name field in its header. Two skills with the same name cannot sit side by side — one of them will be ignored.