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Deploy Release Test

OrganizationPopular
vercel
deploy-release-test

Validate a commit-specific Next.js preview package and manually trigger the entire Next.js deployment test suite through the test_e2e_deploy_release.yml GitHub Actions workflow. Use only when asked to run the full deploy test suite or this workflow specifically from an internal vercel/next.js PR branch. Do not use for focused deployment-test sanity checks; run the relevant tests locally with pnpm test-deploy instead. Covers resolving the latest branch SHA, waiting for vercel-packages, preserving default workflow inputs, dispatching the workflow, and verifying the run.

Overview

Publishervercel
Repositorynext.js
Skill namedeploy-release-test
Stars
143.1K
Forks
34.7K
Bundled files
1
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.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

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

Installation

Install the Deploy Release Test 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/vercel/next.js/tree/canary/.agents/skills/deploy-release-test
  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/next.js.git /tmp/next.js
mkdir -p .claude/skills
cp -r /tmp/next.js/.agents/skills/deploy-release-test .claude/skills/deploy-release-test
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Deploy Release Test 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 Deploy Release Test 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 Deploy Release Test 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.

Deploy Release Test

Use this skill only when manually running the entire Next.js deployment test suite for a pull request. Treat package validation as a hard gate: never dispatch the workflow until the exact commit's redirected tarball responds successfully.

Scope

Do not use this workflow to sanity-check one deployment test or a focused group of tests. Follow Running Deploy Tests Locally instead:

bash
NEXT_TEST_VERSION=https://vercel-packages.vercel.app/next/commits/<commit-sha>/next pnpm test-deploy <path-to-test>

If a request to "run deploy tests" does not explicitly call for the entire suite, prefer the focused local workflow and scope it to the affected tests.

Inputs

  • Accept a PR number or determine the PR from the current branch.
  • Use repository vercel/next.js and workflow .github/workflows/test_e2e_deploy_release.yml.
  • Require the PR head branch to exist in vercel/next.js. A fork branch cannot run this secret-bearing workflow; use the repository's PR adoption process first when appropriate.

Workflow

  1. Resolve the PR branch and its latest commit from GitHub, not merely from the local checkout:

    bash
    gh pr view <pr-number> --repo vercel/next.js \
      --json number,url,headRefName,headRefOid,isCrossRepository

    If no PR number was supplied, omit <pr-number> to detect the PR from the current branch. Stop if isCrossRepository is true. Record headRefName as the branch and headRefOid as the commit SHA.

  2. Construct the exact package URL:

    text
    https://vercel-packages.vercel.app/next/commits/<commit-sha>/next
  3. Wait for the package to become downloadable before dispatching anything:

    bash
    node scripts/wait-for-preview-tarball.mjs --commit-sha <commit-sha>

    Keep the wait in an ongoing terminal session and poll it so the user still receives progress updates. The helper uses HEAD, follows the redirect to Vercel Blob, and only succeeds when the final artifact is available. Do not substitute a check that accepts the initial redirect: that endpoint can redirect even while the blob still returns 404.

    If the helper times out or reports an authorization or build failure, do not dispatch the workflow. Report the failure and inspect the commit's build-and-deploy / upload-preview-tarballs checks if useful.

  4. Resolve the PR again immediately after validation. Compare the current headRefName and headRefOid with the recorded values. If either changed, return to step 2 and validate the new commit-specific URL. Never reuse the old package URL for a moved branch.

  5. Trigger the workflow from the PR branch and pass only nextVersion:

    bash
    gh workflow run test_e2e_deploy_release.yml \
      --repo vercel/next.js \
      --ref <pr-branch> \
      -f nextVersion=https://vercel-packages.vercel.app/next/commits/<commit-sha>/next

    Do not pass any other -f values. Leaving them unspecified preserves the workflow defaults, including vercelCliVersion: vercel@latest and empty optional overrides.

  6. Find and verify the newly created run:

    bash
    gh run list --repo vercel/next.js \
      --workflow test_e2e_deploy_release.yml \
      --branch <pr-branch> \
      --event workflow_dispatch \
      --limit 5 \
      --json databaseId,url,status,conclusion,headBranch,headSha,displayTitle,createdAt

    Confirm that the newest matching run has:

    • headBranch equal to the PR branch
    • headSha equal to the validated commit SHA
    • displayTitle containing the exact commit-specific package URL

    A race can still move the branch between the final check and dispatch. If the run's headSha differs, report the mismatch and do not trigger another run until the new SHA's package has been validated.

Completion Report

Report the PR branch, validated SHA and package URL, workflow run URL, and its current status. Do not wait for the full deployment suite unless the user asks you to monitor it.

Related Skills

  • $pr-status-triage - Inspect failures if the deployment workflow does not pass.
  • $create-pr - Create or update the internal PR branch before deployment testing.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Deploy Release Test AI skill do?

Validate a commit-specific Next.js preview package and manually trigger the entire Next.js deployment test suite through the test_e2e_deploy_release.yml GitHub Actions workflow. Use only when asked to run the full deploy test suite or this workflow specifically from an internal vercel/next.js PR branch. Do not use for focused deployment-test sanity checks; run the relevant tests locally with pnpm test-deploy instead. Covers resolving the latest branch SHA, waiting for vercel-packages, preserving default workflow inputs, dispatching the workflow, and verifying the run.

Why use Deploy Release Test on TypingMind?

Because you install it once and use it with any model. Deploy Release Test 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 Deploy Release Test in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/vercel/next.js/tree/canary/.agents/skills/deploy-release-test. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Deploy Release Test?

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 Deploy Release Test?

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

Is the Deploy Release Test AI skill free?

Yes. It is published on GitHub by vercel 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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