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Env Vars

Organization
vercel
env-vars

Vercel environment variable expert guidance. Use when working with .env files, vercel env commands, OIDC tokens, or managing environment-specific configuration.

Overview

Publishervercel
Repositoryvercel-plugin
Skill nameenv-vars
Stars
286
Forks
56
Bundled files
Instructions only
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 vercel on GitHub. Read the source before you install it.

Installation

Install the Env Vars 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.
  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/vercel/vercel-plugin.git /tmp/vercel-plugin
mkdir -p .claude/skills
cp -r /tmp/vercel-plugin/skills/env-vars .claude/skills/env-vars
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Env Vars 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 Env Vars 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 Env Vars 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 Environment Variables

You are an expert in Vercel environment variable management — .env file conventions, the vercel env CLI, OIDC token lifecycle, and environment-specific configuration.

.env File Hierarchy

Vercel and Next.js load environment variables in a specific order. Later files override earlier ones:

FilePurposeGit-tracked?
.envDefault values for all environmentsYes
.env.localLocal overrides and secretsNo (gitignored)
.env.developmentDevelopment-specific defaultsYes
.env.development.localLocal dev overridesNo
.env.productionProduction-specific defaultsYes
.env.production.localLocal prod overridesNo
.env.testTest-specific defaultsYes
.env.test.localLocal test overridesNo

Load Order (Next.js)

  1. .env (lowest priority)
  2. .env.[environment] (development, production, or test)
  3. .env.local (skipped in test environment)
  4. .env.[environment].local (highest priority, skipped in test)

Critical Rules

  • Never commit secrets to .env, .env.development, or .env.production — use .local variants or Vercel environment variables
  • .env.local is always gitignored by Next.js — this is where vercel env pull writes secrets
  • Variables prefixed with NEXT_PUBLIC_ are exposed to the browser bundle — never put secrets in NEXT_PUBLIC_ vars
  • All other variables are server-only (API routes, Server Components, middleware)

vercel env CLI

Pull Environment Variables

bash
# Pull all env vars for the current environment into .env.local
vercel env pull .env.local

# Pull for a specific environment
vercel env pull .env.local --environment=production
vercel env pull .env.local --environment=preview
vercel env pull .env.local --environment=development

# Overwrite existing file without prompting
vercel env pull .env.local --yes

# Pull to a custom file
vercel env pull .env.production.local --environment=production

Add Environment Variables

bash
# Interactive — prompts for value and environments
vercel env add MY_SECRET

# Non-interactive: read the value from a file so it never lands in shell
# history or process arguments (echo "value" | ... does both)
vercel env add MY_SECRET production < ./secret.txt

# Add to production and preview in one command
vercel env add MY_SECRET production preview < ./secret.txt

# Development must be a separate command: combining it with production or
# preview returns an error because development cannot be sensitive
vercel env add MY_SECRET development < ./dev-secret.txt

# Production and preview default to sensitive (value hidden after creation).
# Opt out only for non-secret values that must stay readable in the dashboard:
echo "public-value" | vercel env add NEXT_PUBLIC_FLAG production --no-sensitive

# Update an existing value
vercel env update MY_SECRET production < ./secret.txt

Team policy can enforce sensitive values; when it does, --no-sensitive is ignored for production and preview and vercel env add rejects development targets.

List Environment Variables

bash
# List all environment variables
vercel env ls

# Filter by environment
vercel env ls production

Remove Environment Variables

bash
# Remove from specific environment
vercel env rm MY_SECRET production

# Remove from all environments
vercel env rm MY_SECRET

Bootstrap Flow (Fresh Clone / New Machine)

Use this sequence when setting up a project from scratch:

bash
# 1) Link first so pulls target the correct Vercel project
vercel link --yes --project <name-or-id> --scope <team>

# 2) Pull env vars into .env.local
vercel env pull .env.local --yes

# 3) Verify required keys from .env.example exist in .env.local
while IFS='=' read -r key _; do
  [[ -z "$key" || "$key" == \#* ]] && continue
  grep -q "^${key}=" .env.local || echo "Missing in .env.local: $key"
done < .env.example

Temporary Path: Run With Vercel Envs Without Writing a File

If you need Vercel environment variables immediately but do not want to write .env.local yet:

bash
vercel env run -- npm run dev

This is useful for quick validation during bootstrap, but still pull .env.local for a normal local workflow.

Re-pull After Secret or Provisioning Changes

After creating/updating secrets (vercel env add, dashboard changes) or provisioning integrations that add env vars (for example Neon/Upstash), re-run:

bash
vercel env pull .env.local --yes

OIDC Token Lifecycle

Vercel uses OIDC (OpenID Connect) tokens for secure, keyless authentication between your app and Vercel services (AI Gateway, storage, etc.).

How It Works

  1. On Vercel deployments: VERCEL_OIDC_TOKEN is automatically injected as a short-lived JWT and auto-refreshed — zero configuration needed
  2. Local development: vercel env pull .env.local provisions a VERCEL_OIDC_TOKEN valid for ~12 hours
  3. Token expiry: When the local OIDC token expires, re-run vercel env pull .env.local --yes to get a fresh one. Consider re-pulling at the start of each dev session to avoid mid-session auth failures

Common OIDC Patterns

ts
// The @vercel/oidc package reads VERCEL_OIDC_TOKEN automatically
import { getVercelOidcToken } from '@vercel/oidc'

// AI Gateway uses OIDC by default — no manual token handling needed
import { gateway } from 'ai'
const result = await generateText({
  model: gateway('openai/gpt-5.2'),
  prompt: 'Hello',
})

Troubleshooting OIDC

SymptomCauseFix
VERCEL_OIDC_TOKEN missing locallyHaven't pulled env varsvercel env pull .env.local
Auth errors after ~12h locallyToken expiredvercel env pull .env.local --yes
Works on Vercel, fails locallyToken not in .env.localvercel env pull .env.local
AI_GATEWAY_API_KEY vs OIDCBoth set, key takes priorityRemove AI_GATEWAY_API_KEY to use OIDC

Environment-Specific Configuration

Vercel Dashboard vs .env Files

Use CaseWhere to Set
Secrets (API keys, tokens)Vercel Dashboard (https://vercel.com/{team}/{project}/settings/environment-variables) or vercel env add
Public config (site URL, feature flags).env or .env.[environment] files
Local-only overrides.env.local
CI/CD secretsVercel Dashboard (https://vercel.com/{team}/{project}/settings/environment-variables) with environment scoping

Environment Scoping on Vercel

Variables set in the Vercel Dashboard at https://vercel.com/{team}/{project}/settings/environment-variables can be scoped to:

  • Production — only vercel.app production deployments
  • Preview — branch/PR deployments
  • Developmentvercel dev and vercel env pull

A variable can be assigned to one, two, or all three environments.

Git Branch Overrides

Preview environment variables can be scoped to specific Git branches:

bash
# Add a variable only for the "staging" branch
vercel env add DATABASE_URL preview --git-branch=staging < ./staging-database-url.txt

Gotchas

vercel env pull Overwrites Custom Variables

vercel env pull .env.local replaces the entire file — any manually added variables (custom secrets, local overrides, debug flags) are lost. Always back up or re-add custom vars after pulling:

bash
# Save custom vars before pulling
grep -v '^#' .env.local | grep -v '^VERCEL_\|^POSTGRES_\|^NEXT_PUBLIC_' > .env.custom.bak
vercel env pull .env.local --yes
cat .env.custom.bak >> .env.local  # Re-append custom vars

Or maintain custom vars in a separate .env.development.local file (loaded after .env.local by Next.js).

Scripts Don't Auto-Load .env.local

Only Next.js auto-loads .env.local. Standalone scripts (drizzle-kit, tsx, custom Node scripts) need explicit loading:

bash
# Use dotenv-cli
npm install -D dotenv-cli
npx dotenv -e .env.local -- npx drizzle-kit push
npx dotenv -e .env.local -- npx tsx scripts/seed.ts

# Or source manually
source <(grep -v '^#' .env.local | sed 's/^/export /') && node scripts/migrate.js

Best Practices

  1. Use vercel env pull as part of your setup workflow — document it in your README
  2. Never hardcode secrets — always use environment variables
  3. Scope narrowly — don't give preview deployments production database access
  4. Rotate OIDC tokens regularly in local dev — re-pull when you see auth errors
  5. Use .env.example — commit a template with empty values so teammates know which vars are needed
  6. Prefix client-side vars with NEXT_PUBLIC_ — and never put secrets in them
  7. Keep custom vars in .env.development.local — protects them from vercel env pull overwrites

Official Documentation

Frequently asked questions

What does the Env Vars AI skill do?

Vercel environment variable expert guidance. Use when working with .env files, vercel env commands, OIDC tokens, or managing environment-specific configuration.

Why use Env Vars on TypingMind?

Because you install it once and use it with any model. Env Vars 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 Env Vars in TypingMind?

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

Which AI models can use Env Vars?

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 Env Vars?

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

Is the Env Vars AI skill free?

It is published on GitHub by vercel. Check the repository for licensing terms. 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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