Vercel Deploy Skill for Codex

Deploy applications and websites to Vercel. 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".

663 tokens

context cost

SKILL.md, read when the skill is used

files

ships runnable scripts

copies elsewhere

how many repositories repackaged it

24 488

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/openai/skills --skill vercel-deploy

What comes with it

11 277 bytes besides the instruction

LICENSE.txt

agents/openai.yaml

assets/vercel-small.svg

assets/vercel.png

scripts/deploy.sh

The instruction itself

8 sections, as written by the author

Vercel Deploy

Deploy any project to Vercel instantly. Always deploy as preview (not production) unless the user explicitly asks for production.

Prerequisites

Check whether the Vercel CLI is installed without escalated permissions (for example, command -v vercel).
Only escalate the actual deploy command if sandboxing blocks the deployment network calls (sandbox_permissions=require_escalated).
The deployment might take a few minutes. Use appropriate timeout values.

Quick Start

Check whether the Vercel CLI is installed (no escalation for this check):

command -v vercel

If vercel is installed, run this (with a 10 minute timeout):

vercel deploy [path] -y

Important: Use a 10 minute (600000ms) timeout for the deploy command since builds can take a while.

If vercel is not installed, or if the CLI fails with "No existing credentials found", use the fallback method below.

Fallback (No Auth)

If CLI fails with auth error, use the deploy script:

skill_dir="<path-to-skill>"

# Deploy current directory
bash "$skill_dir/scripts/deploy.sh"

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

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

The script handles framework detection, packaging, and deployment. It waits for the build to complete and returns JSON with previewUrl and claimUrl.

Tell the user: "Your deployment is ready at [previewUrl]. Claim it at [claimUrl] to manage your deployment."

Production Deploys

Only if user explicitly asks:

vercel deploy [path] --prod -y

Output

Show the user the deployment URL. For fallback deployments, also show the claim URL.

Do not curl or fetch the deployed URL to verify it works. Just return the link.

Troubleshooting

Escalated Network Access

If deployment fails due to network issues (timeouts, DNS errors, connection resets), rerun the actual deploy command with escalated permissions (use sandbox_permissions=require_escalated). Do not escalate the command -v vercel installation check. The deploy requires escalated network access when sandbox networking blocks outbound requests.

Example guidance to the user:

The deploy needs escalated network access to deploy to Vercel. I can rerun the command with escalated permissions—want me to proceed?

Other skills for the same job

different authors, same section of the catalogue

Deploying To Production

by ZhanlinCui

×4

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 openai/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.