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".
npx skills add https://github.com/vercel-labs/agent-skills --skill deploy-to-vercel
Deploy any project to Vercel. Always deploy as preview (not production) unless the user explicitly asks for production.
The goal is to get the user into the best long-term setup: their project linked to Vercel with git-push deploys. Every method below tries to move the user closer to that state.
Run all four checks before deciding which method to use:
# 1. Check for a git remote
git remote get-url origin 2>/dev/null
# 2. Check if locally linked to a Vercel project (either file means linked)
cat .vercel/project.json 2>/dev/null || cat .vercel/repo.json 2>/dev/null
# 3. Check if the Vercel CLI is installed and authenticated
vercel whoami 2>/dev/null
# 4. List available teams (if authenticated)
vercel teams list --format json 2>/dev/null
If the user belongs to multiple teams, present all available team slugs as a bulleted list and ask which one to deploy to. Once the user picks a team, proceed immediately to the next step — do not ask for additional confirmation.
Pass the team slug via --scope on all subsequent CLI commands (vercel deploy, vercel link, vercel inspect, etc.):
vercel deploy [path] -y --no-wait --scope <team-slug>
If the project is already linked (.vercel/project.json or .vercel/repo.json exists), the orgId in those files determines the team — no need to ask again. If there is only one team (or just a personal account), skip the prompt and use it directly.
About the .vercel/ directory: A linked project has either:
.vercel/project.json — created by vercel link (single project linking). Contains projectId and orgId..vercel/repo.json — created by vercel link --repo (repo-based linking). Contains orgId, remoteName, and a projects array mapping directories to Vercel project IDs.Either file means the project is linked. Check for both.
Do NOT use vercel project inspect, vercel ls, or vercel link to detect state in an unlinked directory — without a .vercel/ config, they will interactively prompt (or with --yes, silently link as a side-effect). Only vercel whoami is safe to run anywhere.
.vercel/ exists) + has git remote → Git PushThis is the ideal state. The project is linked and has git integration.
This project is connected to Vercel via git. I can commit and push to
trigger a deployment. Want me to proceed?
git add .
git commit -m "deploy: <description of changes>"
git push
Vercel automatically builds from the push. Non-production branches get preview deployments; the production branch (usually main) gets a production deployment.
sleep 5
vercel ls --format json
The JSON output has a deployments array. Find the latest entry — its url field is the preview URL.
If the CLI is not authenticated, tell the user to check the Vercel dashboard or the commit status checks on their git provider for the preview URL.
.vercel/ exists) + no git remote → vercel deployThe project is linked but there's no git repo. Deploy directly with the CLI.
vercel deploy [path] -y --no-wait
Use --no-wait so the CLI returns immediately with the deployment URL instead of blocking until the build finishes (builds can take a while). Then check on the deployment status with:
vercel inspect <deployment-url>
For production deploys (only if user explicitly asks):
vercel deploy [path] --prod -y --no-wait
The CLI is working but the project isn't linked yet. This is the opportunity to get the user into the best state.
Linking this project to <team name> on Vercel. This will create a Vercel
project to deploy to and enable automatic deployments on future git pushes.
vercel link --repo --scope <team-slug>
This reads the git remote URL and matches it to existing Vercel projects that deploy from that repo. It creates .vercel/repo.json. This is much more reliable than vercel link (without --repo), which tries to match by directory name and often fails when the local folder and Vercel project are named differently.
If there is no git remote, fall back to standard linking:
vercel link --scope <team-slug>
This prompts the user to select or create a project. It creates .vercel/project.json.
vercel deploy [path] -y --no-wait --scope <team-slug>, then vercel inspect <url> to check statusThe Vercel CLI isn't set up at all.
npm install -g vercel
vercel login
The user completes auth in their browser. If running in a non-interactive environment where login is not possible, skip to the no-auth fallback below.
vercel teams list --format json as a bulleted list. If only one team / personal account, skip. Once selected, proceed immediately.--repo if a git remote exists, plain vercel link otherwise): vercel link --repo --scope <team-slug> # if git remote exists
vercel link --scope <team-slug> # if no git remote
vercel deploy -y --no-wait --scope <team-slug>, then vercel inspect <url> to check status).When to use: Last resort when the CLI can't be installed or authenticated in the claude.ai sandbox. This requires no authentication — it returns a Preview URL (live site) and a Claim URL (transfer to your Vercel account).
bash /mnt/skills/user/deploy-to-vercel/resources/deploy.sh [path]
Arguments:
path - Directory to deploy, or a .tgz file (defaults to current directory)Examples:
# Deploy current directory
bash /mnt/skills/user/deploy-to-vercel/resources/deploy.sh
# Deploy specific project
bash /mnt/skills/user/deploy-to-vercel/resources/deploy.sh /path/to/project
# Deploy existing tarball
bash /mnt/skills/user/deploy-to-vercel/resources/deploy.sh /path/to/project.tgz
The script auto-detects the framework from package.json, packages the project (excluding node_modules, .git, .env), uploads it, and waits for the build to complete.
Tell the user: "Your deployment is ready at [previewUrl]. Claim it at [claimUrl] to manage your deployment."
When to use: In the Codex sandbox where the CLI may not be authenticated. Codex runs in a sandboxed environment by default — try the CLI first, and fall back to the deploy script if auth fails.
command -v vercel
vercel is installed, try deploying with the CLI: vercel deploy [path] -y --no-wait
vercel is not installed, or the CLI fails with "No existing credentials found", use the fallback script: skill_dir="<path-to-skill>"
# Deploy current directory
bash "$skill_dir/resources/deploy-codex.sh"
# Deploy specific project
bash "$skill_dir/resources/deploy-codex.sh" /path/to/project
# Deploy existing tarball
bash "$skill_dir/resources/deploy-codex.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."
Escalated network access: Only escalate the actual deploy command if sandboxing blocks the network call (sandbox_permissions=require_escalated). Do not escalate the command -v vercel check.
You have full shell access. Do NOT use the /mnt/skills/ path. Follow the decision flow above using the CLI directly.
For the no-auth fallback, run the deploy script from the skill's installed location:
bash ~/.claude/skills/deploy-to-vercel/resources/deploy.sh [path]
The path may vary depending on where the user installed the skill.
You likely cannot run vercel login or git push. Go directly to the no-auth fallback — claude.ai sandbox.
Codex runs in a sandbox. Check if the CLI is available first, then fall back to the deploy script. Go to the no-auth fallback — Codex sandbox.
Always show the user the deployment URL.
vercel ls --format json to find the preview URL. If the CLI isn't authenticated, tell the user to check the Vercel dashboard or commit status checks.vercel deploy --no-wait. Use vercel inspect <url> to check build status and report it to the user. Deployment successful!
Preview URL: https://my-app-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.
Do not curl or fetch the deployed URL to verify it works. Just return the link.
If deployment fails due to network restrictions on claude.ai, tell the user:
Deployment failed due to network restrictions. To fix this:
1. Go to https://claude.ai/settings/capabilities
2. Add *.vercel.com to the allowed domains
3. Try deploying again
If deployment fails due to network issues (timeouts, DNS errors, connection resets) in a sandboxed environment, rerun with escalated permissions (sandbox_permissions=require_escalated). Do not escalate the initial CLI availability check — only escalate the actual deploy command.
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?
If vercel login or vercel deploy fails with authentication errors, fall back to the no-auth deploy script (claude.ai or Codex variant, depending on the environment).
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.
Latch platform for bioinformatics workflows. Build pipelines with Latch SDK, @workflow/@task decorators, deploy serverless workflows, LatchFile/LatchDir, Nextflow/Snakemake integration.
Run Python code in the cloud with serverless containers, GPUs, and autoscaling. Use when deploying ML models, running batch processing jobs, scheduling compute-intensive tasks, or serving APIs that require GPU acceleration or dynamic scaling.
Take vercel-labs/deploy-to-vercel from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
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.
The instructions reference npm.
Without those the skill loads but fails at the first command.