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Access Protected Vercel Deployment

Organization
vercel
access-protected-vercel-deployment

Access and test Vercel deployments protected by Vercel Authentication, SSO, or Deployment Protection. Use when curl, agent-browser, Playwright, or another automated request reaches a Vercel login or protection page; when a protected preview or production URL returns 401 or 403; when TRUSTED_SOURCES_ENVIRONMENT_MISMATCH appears; or when choosing between `vercel curl` and the `x-vercel-trusted-oidc-idp-token` header.

Overview

Publishervercel
Repositoryvercel-plugin
Skill nameaccess-protected-vercel-deployment
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 Access Protected Vercel Deployment 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/access-protected-vercel-deployment .claude/skills/access-protected-vercel-deployment
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Access Protected Vercel Deployment 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 Access Protected Vercel Deployment 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 Access Protected Vercel Deployment 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.

Access Protected Vercel Deployments

Use the caller's existing Vercel authentication. Do not disable Deployment Protection or ask for a long-lived bypass secret as the first solution.

Choose the access path

HTTP requests: use vercel curl

For response bodies, headers, health checks, and API calls, replace raw curl with vercel curl (vc curl). It accepts native curl options and uses Vercel authentication to access protected preview and production deployments.

bash
vc curl https://my-app.vercel.app/api/health
vc curl https://app.example.com/api/health
vc curl my-app.vercel.app/api/users -X POST \
  -H "Content-Type: application/json" \
  -d '{"name":"Ada"}'
vc curl /api/health

The path-only form targets the linked project's production deployment. Pass a full URL when the exact deployment matters.

If authentication fails, check the local identity and project before changing protection settings:

bash
vc whoami

Inspect .vercel/project.json to confirm the linked project and team. Run vc link only when the directory is not linked or is linked to the wrong project. Run vc login only when the CLI reports that no authenticated user is available.

Browser automation: attach the development OIDC token as a header

Browser requests must include the short-lived local token as a request header:

text
x-vercel-trusted-oidc-idp-token: <VERCEL_OIDC_TOKEN>

Use a browser tool that supports origin-scoped request headers. With agent-browser, inject development variables without printing or persisting the token:

bash
vc env run -- sh -c \
  'test -n "$VERCEL_OIDC_TOKEN" && agent-browser open "$1" --headers "{\"x-vercel-trusted-oidc-idp-token\":\"$VERCEL_OIDC_TOKEN\"}"' \
  sh https://my-app.vercel.app

Then continue the normal browser workflow in the same session. For Playwright or another browser driver, set the same header in the browser context's extra HTTP headers before the first navigation.

If the local CLI version does not provide the token through vc env run, refresh local development credentials with:

bash
vc env pull .env.local --yes

Load the file through the project's existing dotenv mechanism. Never print the token, paste its value into source code, or commit .env.local.

Use x-vercel-trusted-oidc-idp-token for Trusted Sources. Do not substitute x-vercel-oidc-token; that header carries an OIDC token into a Vercel Function and serves a different purpose.

Trusted Sources rules

A local development token for a linked Vercel project can access that same project's Preview deployments by default. It does not automatically access protected Production deployments. For protected Production, the project's own Trusted Sources entry must allow developmentproduction.

Do not ask the user to configure Trusted Sources for the normal same-project Preview case.

Configuration is needed when:

  • the target is a protected Production deployment and the caller uses a local development token;
  • the caller belongs to another Vercel project or team;
  • the target project's self-access rules were customized; or
  • the response is TRUSTED_SOURCES_ENVIRONMENT_MISMATCH.

In the target project, open Settings → Deployment Protection → Trusted Sources. Add or edit the caller and allow the required fromto environment pair. A local token has the development environment, so access to protected Production requires developmentproduction.

Treat this as an access-control change: explain the exact rule required and obtain authorization before changing it. Do not broaden unrelated environment pairs.

Diagnose the response

  • A Vercel login, SSO, or Deployment Protection page means the request did not use an accepted authentication path.
  • TRUSTED_SOURCES_ENVIRONMENT_MISMATCH means the token is valid but its caller environment is not allowed to reach the target environment.
  • An application-generated 401 or 403 after Vercel protection is bypassed belongs to the application's own authentication and must be debugged separately.
  • A deployment marked "target": "production" can still be protected. Do not assume production is public.

Avoid

  • Do not disable Deployment Protection to make automation pass.
  • Do not send raw unauthenticated curl repeatedly after receiving the protection page.
  • Do not start an interactive SSO browser login when vc curl or an origin-scoped OIDC header can authenticate the request.
  • Do not expose VERCEL_OIDC_TOKEN in logs, screenshots, committed files, or user-facing output.

Related skills

  • General Vercel CLI usage: ⤳ skill: vercel-cli
  • End-to-end application verification: ⤳ skill: verification

Frequently asked questions

What does the Access Protected Vercel Deployment AI skill do?

Access and test Vercel deployments protected by Vercel Authentication, SSO, or Deployment Protection. Use when curl, agent-browser, Playwright, or another automated request reaches a Vercel login or protection page; when a protected preview or production URL returns 401 or 403; when TRUSTED_SOURCES_ENVIRONMENT_MISMATCH appears; or when choosing between `vercel curl` and the `x-vercel-trusted-oidc-idp-token` header.

Why use Access Protected Vercel Deployment on TypingMind?

Because you install it once and use it with any model. Access Protected Vercel Deployment 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 Access Protected Vercel Deployment in TypingMind?

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

Which AI models can use Access Protected Vercel Deployment?

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 Access Protected Vercel Deployment?

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

Is the Access Protected Vercel Deployment 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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