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Vercel Cli

OrganizationPopular
nanocoai
vercel-cli

Deploy apps to Vercel. Use when asked to deploy, ship, or publish a web application, or manage Vercel projects, domains, and environment variables.

Overview

Publishernanocoai
Repositorynanoclaw
Skill namevercel-cli
Stars
30.9K
Forks
12.8K
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by nanocoai on GitHub. Read the source before you install it.

Installation

Install the Vercel Cli AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/nanocoai/nanoclaw/tree/main/.claude/skills/add-vercel/container-skills/vercel-cli
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/nanocoai/nanoclaw.git /tmp/nanoclaw
mkdir -p .claude/skills
cp -r /tmp/nanoclaw/.claude/skills/add-vercel/container-skills/vercel-cli .claude/skills/vercel-cli
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Vercel Cli in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Vercel Cli on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Vercel Cli is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Vercel CLI

You can deploy web applications to Vercel using the vercel CLI.

HARD RULE: You MUST NOT write HTML, CSS, or JavaScript yourself. When asked to build a website or web app, you MUST delegate to a Frontend Engineer subagent (see "Building Websites" section below). This is not optional. Violation wastes your context window on code that belongs in a separate agent.

Auth

Auth is handled by OneCLI — the HTTPS_PROXY injects the real token into API requests automatically. The Vercel CLI requires a token to be present to skip its local credential check, so always pass --token placeholder on every command. OneCLI replaces this with the real token at the proxy level.

Before any Vercel operation, verify auth:

bash
vercel whoami --token placeholder

If this fails with an auth error, ask the user to add a Vercel token to OneCLI. They can create one at https://vercel.com/account/tokens and register it via onecli secrets create on the host. Once added, retry vercel whoami.

Deploying

Always use --yes to skip interactive prompts and --token placeholder for auth (OneCLI replaces with real token).

bash
# Deploy to production
vercel deploy --yes --prod --token placeholder

# Deploy from a specific directory
vercel deploy --yes --prod --token placeholder --cwd /path/to/project

# Preview deployment (not production)
vercel deploy --yes --token placeholder

After deploying, verify the live URL:

bash
# Check deployment status
vercel inspect <deployment-url> --token placeholder

Pre-Send Checks (do this before sharing the URL)

Don't send the deployment URL to the user until you've confirmed it's actually working. At minimum:

  1. Local build passes — run npm run build (or the project's build command) before vercel deploy. If the build fails locally, fix it first; don't deploy broken code.
  2. Deployment succeeded — the vercel deploy output shows a "Production: https://..." URL and the status is READY (confirm with vercel inspect).
  3. Live URL responds — curl -sI <url> | head -1 should return HTTP/2 200 (or another 2xx/3xx). A 404/500 means something's broken even though Vercel reported success.
  4. Optional visual check — if agent-browser is loaded, open the URL and eyeball it. Helpful for catching broken layouts that a 200 response wouldn't reveal.

If any check fails, fix the issue and redeploy before reporting to the user.

Project Management

bash
# Link to an existing Vercel project (non-interactive)
vercel link --yes --token placeholder

# List recent deployments
vercel ls --token placeholder

# List all projects
vercel project ls --token placeholder

Domains

bash
# List domains
vercel domains ls --token placeholder

# Add a domain to the current project
vercel domains add example.com --token placeholder

Environment Variables

bash
# Pull env vars from Vercel to local .env
vercel env pull --token placeholder

# Add an env var (use echo to pipe the value — avoids interactive prompt)
echo "value" | vercel env add VAR_NAME production --token placeholder

Common Errors

ErrorFix
Error: No framework detectedEnsure the project has a package.json with a build script, or set the framework in vercel.json
Error: Rate limitedWait and retry. Don't loop — report to user
Error: You have reached your project limitUser needs to upgrade Vercel plan or delete unused projects
ENOTFOUND api.vercel.comNetwork issue. Check proxy connectivity
Auth error after vercel whoamiCredential may be expired. Ask the user to refresh the Vercel token in OneCLI

Building Websites — Delegate to Frontend Engineer

When asked to build, create, or redesign a website or web app, do NOT build it yourself. You MUST delegate to a Frontend Engineer agent. This is a two-step process and both steps are required:

Step 1 — Create the agent (skip if you already have a "frontend-engineer" destination):

create_agent({
  name: "Frontend Engineer",
  instructions: "You are a dedicated frontend engineer. Your frontend-engineer skill has your full workflow. Build what is requested, test it visually with agent-browser, deploy to Vercel, and send back the live URL + screenshots to your parent agent when done."
})

Step 2 — Send the build request (MANDATORY — do this immediately after step 1):

send_message(to: "frontend-engineer", text: "<full description of what to build, including design requirements, content, colors, and any assets>")

⚠️ CRITICAL: If you skip step 2, nothing happens. The agent exists but has no work. You MUST send the message. Do NOT tell the user "it's working on it" until you have actually called send_message.

After sending, tell the user you've handed it off and will share the result when it comes back. The Frontend Engineer will send you the live URL + screenshots when done — forward those to the user.

When to delegate vs do it yourself:

  • Delegate: building new sites, redesigns, multi-page apps, anything that needs visual testing
  • Do yourself: simple vercel deploy of an existing project, checking deployment status, managing domains/env vars

Best Practices

  • Run pnpm run build locally before deploying to catch build errors early
  • Use --cwd instead of cd to keep your working directory stable
  • For Next.js projects, vercel deploy auto-detects the framework — no extra config needed
  • Use vercel.json only when you need custom build settings, rewrites, or headers

Frequently asked questions

What does the Vercel Cli AI skill do?

Deploy apps to Vercel. Use when asked to deploy, ship, or publish a web application, or manage Vercel projects, domains, and environment variables.

Why use Vercel Cli on TypingMind?

Because you install it once and use it with any model. Vercel Cli is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Vercel Cli in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/nanocoai/nanoclaw/tree/main/.claude/skills/add-vercel/container-skills/vercel-cli. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Vercel Cli?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Vercel Cli?

As many as you like. As long as a model supports skills, you can use Vercel Cli with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Vercel Cli AI skill free?

Yes. It is published on GitHub by nanocoai under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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