Deployments Cicd logo

Deployments Cicd

Organization
vercel
deployments-cicd

Vercel deployment and CI/CD expert guidance. Use when deploying, promoting, rolling back, inspecting deployments, building with --prebuilt, or configuring CI workflow files for Vercel.

Overview

Publishervercel
Repositoryvercel-plugin
Skill namedeployments-cicd
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 Deployments Cicd 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/deployments-cicd .claude/skills/deployments-cicd
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Deployments Cicd 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 Deployments Cicd 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 Deployments Cicd 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 Deployments & CI/CD

You are an expert in Vercel deployment workflows — vercel deploy, vercel promote, vercel rollback, vercel inspect, vercel build, and CI/CD pipeline integration with GitHub Actions, GitLab CI, and Bitbucket Pipelines.

Deployment Commands

Preview Deployment

bash
# Deploy from project root (creates preview URL)
vercel

# Equivalent explicit form
vercel deploy

Preview deployments are created automatically for every push to a non-production branch when using Git integration. They provide a unique URL for testing.

Production Deployment

bash
# Deploy directly to production
vercel --prod
vercel deploy --prod

# Force a new deployment (skip cache)
vercel --prod --force

Build Locally, Deploy Build Output

bash
# Build locally (uses development env vars by default)
vercel build

# Build with production env vars
vercel build --prod

# Deploy only the build output (no remote build)
vercel deploy --prebuilt
vercel deploy --prebuilt --prod

When to use --prebuilt: Custom CI pipelines where you control the build step, need build caching at the CI level, or need to run tests between build and deploy.

Promote & Rollback

bash
# Promote a preview deployment to production
vercel promote <deployment-url-or-id>

# Rollback to the previous production deployment
vercel rollback

# Rollback to a specific deployment
vercel rollback <deployment-url-or-id>

Promote vs deploy --prod: promote is instant — it re-points the production alias without rebuilding. Use it when a preview deployment has been validated and is ready for production.

Inspect Deployments

bash
# View deployment details (build info, functions, metadata)
vercel inspect <deployment-url>

# List recent deployments
vercel ls

# View logs for a deployment
vercel logs <deployment-url>
vercel logs <deployment-url> --follow

CI/CD Integration

Required Environment Variables

Every CI pipeline needs these three variables:

bash
VERCEL_TOKEN=<your-token>        # Personal or team token
VERCEL_ORG_ID=<org-id>           # From .vercel/project.json
VERCEL_PROJECT_ID=<project-id>   # From .vercel/project.json

Set these as secrets in your CI provider. Never commit them to source control.

GitHub Actions

yaml
name: Deploy to Vercel
on:
  push:
    branches: [main]

jobs:
  deploy:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4

      - name: Install Vercel CLI
        run: npm install -g vercel

      - name: Pull Vercel Environment
        run: vercel pull --yes --environment=production --token=${{ secrets.VERCEL_TOKEN }}

      - name: Build
        run: vercel build --prod --token=${{ secrets.VERCEL_TOKEN }}

      - name: Deploy
        run: vercel deploy --prebuilt --prod --token=${{ secrets.VERCEL_TOKEN }}

OIDC Federation (Secure Backend Access)

Vercel OIDC federation is for secure backend access — letting your deployed Vercel functions authenticate with third-party services (AWS, GCP, HashiCorp Vault) without storing long-lived secrets. It does not replace VERCEL_TOKEN for CLI deployments.

What OIDC does: Your Vercel function requests a short-lived OIDC token from Vercel at runtime, then exchanges it with an external provider's STS/token endpoint for scoped credentials.

What OIDC does not do: Authenticate vercel pull/build/deploy in CI; those need a Vercel access token. Only vcr and Remote Cache offer CI-side OIDC exchanges.

When to use OIDC:

  • Serverless functions that need to call AWS APIs (S3, DynamoDB, SQS)
  • Functions authenticating to GCP services via Workload Identity Federation
  • Any runtime service-to-service auth where you want to avoid storing static secrets in Vercel env vars

GitLab CI

yaml
deploy:
  image: node:20
  stage: deploy
  script:
    - npm install -g vercel
    - vercel pull --yes --environment=production --token=$VERCEL_TOKEN
    - vercel build --prod --token=$VERCEL_TOKEN
    - vercel deploy --prebuilt --prod --token=$VERCEL_TOKEN
  only:
    - main

Bitbucket Pipelines

yaml
pipelines:
  branches:
    main:
      - step:
          name: Deploy to Vercel
          image: node:20
          script:
            - npm install -g vercel
            - vercel pull --yes --environment=production --token=$VERCEL_TOKEN
            - vercel build --prod --token=$VERCEL_TOKEN
            - vercel deploy --prebuilt --prod --token=$VERCEL_TOKEN

Common CI Patterns

Preview Deployments on PRs

yaml
# GitHub Actions
on:
  pull_request:
    types: [opened, synchronize]

jobs:
  preview:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - run: npm install -g vercel
      - run: vercel pull --yes --environment=preview --token=${{ secrets.VERCEL_TOKEN }}
      - run: vercel build --token=${{ secrets.VERCEL_TOKEN }}
      - id: deploy
        run: echo "url=$(vercel deploy --prebuilt --token=${{ secrets.VERCEL_TOKEN }})" >> $GITHUB_OUTPUT
      - name: Comment PR
        uses: actions/github-script@v7
        with:
          script: |
            github.rest.issues.createComment({
              issue_number: context.issue.number,
              owner: context.repo.owner,
              repo: context.repo.repo,
              body: `Preview: ${{ steps.deploy.outputs.url }}`
            })

Promote After Tests Pass

yaml
jobs:
  deploy-preview:
    # ... deploy preview ...
    outputs:
      url: ${{ steps.deploy.outputs.url }}

  e2e-tests:
    needs: deploy-preview
    runs-on: ubuntu-latest
    steps:
      - run: npx playwright test --base-url=${{ needs.deploy-preview.outputs.url }}

  promote:
    needs: [deploy-preview, e2e-tests]
    runs-on: ubuntu-latest
    if: github.ref == 'refs/heads/main'
    steps:
      - run: npm install -g vercel
      - run: vercel promote ${{ needs.deploy-preview.outputs.url }} --token=${{ secrets.VERCEL_TOKEN }}

Global CLI Flags for CI

FlagPurpose
--token <token>Authenticate (required in CI)
--yes / -ySkip confirmation prompts
--scope <team>Execute as a specific team
--cwd <dir>Set working directory

Best Practices

  1. Always use --prebuilt in CI — separates build from deploy, enables build caching and test gates
  2. Use vercel pull before build — ensures correct env vars and project settings
  3. Prefer promote over re-deploy — instant, no rebuild, same artifact
  4. Use OIDC federation for runtime backend access — lets Vercel functions auth to AWS/GCP without static secrets (does not replace VERCEL_TOKEN for CLI)
  5. Pin the Vercel CLI version in CInpm install -g vercel@latest can break unexpectedly
  6. Add --yes flag in CI — prevents interactive prompts from hanging pipelines

Deployment Strategy Matrix

ScenarioStrategyCommands
Standard team workflowGit-push deployPush to main/feature branches
Custom CI/CD (Actions, CircleCI)Prebuilt deployvercel build && vercel deploy --prebuilt
Monorepo with TurborepoAffected + remote cacheturbo run build --affected --remote-cache
Preview for every PRDefault behaviorAuto-creates preview URL per branch
Promote preview to productionCLI promotionvercel promote <url>
Atomic deploys with DB migrationsTwo-phaseRun migration → verify → vercel promote
Latency-sensitive regional dataVercel FunctionsKeep the Node.js default; set the function region near the data

Common Build Errors

ErrorCauseFix
ERR_PNPM_OUTDATED_LOCKFILELockfile doesn't match package.jsonRun pnpm install, commit lockfile
NEXT_NOT_FOUNDRoot directory misconfiguredSet rootDirectory in Project Settings
Invalid next.config.jsConfig syntax errorValidate config locally with next build
functions/api/*.js mismatchWrong file structureMove to app/api/ directory (App Router)
Error: EPERMFile permission issue in buildDon't chmod in build scripts; use postinstall

Deploy Summary Format

Present a structured deploy result block:

## Deploy Result
- **URL**: <deployment-url>
- **Target**: production | preview
- **Status**: READY | ERROR | BUILDING | QUEUED
- **Commit**: <short-sha>
- **Framework**: <detected-framework>
- **Build Duration**: <duration>

If the deployment failed, append:

- **Error**: <summary of failure from logs>

For production deploys, also include:

### Post-Deploy Observability
- **Error scan**: <N errors found / clean> (scanned via vercel logs --level error --since 1h)
- **Drains**: <N configured / none>
- **Monitoring**: <active / gaps identified>

Deploy Next Steps

Based on the deployment outcome:

  • Success (preview) → "Visit the preview URL to verify. When ready, run /deploy prod to promote to production."
  • Success (production) → "Your production site is live. Run /status to see the full project overview."
  • Build error → "Check the build logs above. Common fixes: verify build script in package.json, check for missing env vars with /env list, ensure dependencies are installed."
  • Missing env vars → "Run /env pull to sync environment variables locally, or /env list to review what's configured on Vercel."
  • Monorepo issues → "Ensure the correct project root is configured in Vercel project settings. Check vercel.json for rootDirectory."
  • Post-deploy errors detected → "Review errors above. Check vercel logs <url> --level error for details. If drains are configured, correlate with external monitoring."
  • No monitoring configured → "Set up drains or install an error tracking integration before the next production deploy. Run /status for a full observability diagnostic."

Official Documentation

Frequently asked questions

What does the Deployments Cicd AI skill do?

Vercel deployment and CI/CD expert guidance. Use when deploying, promoting, rolling back, inspecting deployments, building with --prebuilt, or configuring CI workflow files for Vercel.

Why use Deployments Cicd on TypingMind?

Because you install it once and use it with any model. Deployments Cicd 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 Deployments Cicd in TypingMind?

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

Which AI models can use Deployments Cicd?

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 Deployments Cicd?

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

Is the Deployments Cicd 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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