How to Use Vercel Engineering Skills for Next.js Best Practices with VoltAgent

The Vercel Engineering Team publishes an official agent skill that audits Next.js applications for routing, caching, and image optimization best practices, executable via the VoltAgent CLI or the @voltagent/core SDK.

The VoltAgent/awesome-agent-skills repository catalogs production-grade capabilities from leading engineering teams, including Vercel Labs' Next.js Best Practices skill. This skill encapsulates current recommendations for next/image adoption, static generation configuration, and script optimization, enabling automated code quality checks through containerized analysis.

What Are Vercel Engineering Skills?

Vercel Engineering Skills are encapsulated, executable modules hosted on the Official Skills platform that bundledomain-specific expertise into reusable components. The next-best-practices skill specifically targets Next.js applications, performing static analysis via next lint, custom AST walkers, and configuration validation to identify deviations from the latest framework recommendations.

Each skill runs inside an isolated Model Context Protocol (MCP) server container, ensuring your source code and environment variables remain secure during analysis.

Architectural Overview

The skill follows a declarative architecture defined by metadata and runtime configuration files.

Skill Manifest Configuration

The skill.yaml file declares the skill's runtime environment, input parameters, and output schema. According to the repository listing【2†L21-L22】, this manifest instructs VoltAgent how to provision the container, mount your project filesystem (read-only), and initialize the Node.js execution context.

Core Implementation

The business logic resides in src/index.ts, where the skill:

1. Analyzes next.config.js for production optimizations.
2. Scans page components to validate getStaticProps and revalidate usage.
3. Executes next lint with Vercel's recommended ESLint configurations.
4. Generates structured diagnostics identifying missing next/image implementations or oversized static assets.

Execution Flow

When invoked, VoltAgent performs the following operations:

1. Spins up an isolated container based on the skill.yaml manifest.
2. Mounts your Next.js project into the container filesystem.
3. Executes the analysis pipeline defined in src/index.ts.
4. Returns a JSON or Markdown report containing severity-graded diagnostics.

Installation and Setup

Before running the skill, install the VoltAgent command-line interface globally.

npm i -g @voltagent/cli

Verify installation by checking the CLI version:

voltagent --version

No additional configuration is required, as the skill manages its own dependencies—including the next package and ESLint configurations—internally.

Running the Skill from the Command Line

Execute a one-off audit of your local Next.js project using the run command with the skill identifier vercel-labs/next-best-practices.

voltagent run vercel-labs/next-best-practices \
  --path ./my-next-app \
  --output markdown

Available flags include:

• --path: Path to your Next.js project directory (required).
• --output: Format results as markdown or json (default: json).
• --apply-fixes: Automatically refactor detectable issues (requires explicit consent).
• --ci: Silent mode optimized for continuous integration pipelines.

The CLI prints a structured report highlighting violations such as improper next/script usage or missing image optimization.

Programmatic Usage with TypeScript

Integrate the skill directly into your applications using the @voltagent/core SDK. Import createAgent and runSkill to execute analysis programmatically.

import { createAgent, runSkill } from "@voltagent/core";

const agent = await createAgent({ name: "next-advisor" });

const result = await runSkill(agent, {
  name: "vercel-labs/next-best-practices",
  inputs: {
    path: "./my-next-app",
    format: "json",
    applyFixes: false,
  },
});

console.log("Diagnostics:", result);

The result object contains an array of diagnostic objects with the following structure:

{
  "id": "next-image-usage",
  "severity": "info",
  "message": "All images should use <next/image> for optimal performance",
  "files": ["components/Header.tsx", "pages/index.tsx"]
}

This JSON payload can drive automated workflows, such as posting pull request comments or failing CI builds when critical issues are detected.

CI/CD Integration with GitHub Actions

Automate best-practice enforcement by adding the skill to your GitHub Actions workflow. The following configuration triggers analysis on JavaScript and TypeScript file changes.

name: Next.js Best-Practice Lint

on:
  push:
    paths:
      - "**/*.js"
      - "**/*.tsx"

jobs:
  next-best-practices:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v3
      - name: Install VoltAgent CLI
        run: npm i -g @voltagent/cli
      - name: Run Vercel Next.js Best-Practices Skill
        run: |
          voltagent run vercel-labs/next-best-practices \
            --path ./my-next-app \
            --output json > report.json
      - name: Upload Report
        uses: actions/upload-artifact@v3
        with:
          name: next-best-practices-report
          path: report.json

For automatic fixes in CI, add the --apply-fixes flag and commit changes using a GitHub Actions bot, though this requires careful review to prevent unintended modifications.

Key Implementation Files

The following files define the skill's behavior and are referenced in the VoltAgent/awesome-agent-skills repository:

• skill.yaml: Declares runtime parameters including Node.js version, input validation schema for path and format, and output specifications. Located at vercel-labs/next-best-practices/skill.yaml.
• src/index.ts: Contains the primary analysis logic, Next.js internal API usage, and diagnostic generation. Located at vercel-labs/next-best-practices/src/index.ts.
• package.json: Lists dependencies including next, eslint, and @voltagent/sdk. Located at vercel-labs/next-best-practices/package.json.
• README.md: Documents supported Next.js versions and detailed usage examples. Located at vercel-labs/next-best-practices/README.md.

Summary

• Vercel engineering skills provide containerized, automated audits for Next.js best practices via the VoltAgent platform.
• The next-best-practices skill analyzes next.config.js, page components, and asset usage to generate actionable diagnostics.
• Execute via CLI using voltagent run vercel-labs/next-best-practices or programmatically with the runSkill function from @voltagent/core.
• The skill runs in an isolated MCP container, ensuring secure, read-only access to your source code.
• Integrate into CI/CD pipelines using the JSON output mode to enforce quality gates on every commit.

Frequently Asked Questions

How does the Vercel Next.js skill differ from running next lint directly?

While next lint identifies ESLint violations, the Vercel engineering skill performs a comprehensive audit that includes static generation configuration checks, next/image usage analysis, and asset size validation. It also runs in an isolated container with standardized output formats (JSON/Markdown) suitable for automated workflows, whereas native next lint output requires additional parsing for CI integration.

Can the skill automatically fix issues in my codebase?

Yes, when you pass the --apply-fixes flag via CLI or set applyFixes: true in the programmatic API, the skill attempts automatic refactoring. This feature modifies files directly in your project directory, so it should only be used with version control in place and after reviewing the proposed changes in dry-run mode first.

What Next.js versions are supported by this skill?

The skill supports Next.js versions declared in its package.json dependencies, typically covering the current stable release and recent LTS versions. Consult the vercel-labs/next-best-practices/README.md file listed in the Awesome Agent Skills repository for the specific version compatibility matrix, as Vercel Labs updates the skill to track framework releases.

Is my source code secure when using this skill?

Yes. According to the implementation architecture, the skill executes inside a sandboxed MCP server container with read-only filesystem access to your project. Your source code never leaves the isolated environment, and environment variables are not accessible to the skill process, satisfying enterprise security requirements for third-party analysis tools.

{
  "mcpServers": {
    "instagit": {
      "command": "npx",
      "args": ["-y", "instagit@latest"]
    }
  }
}