Vercel Sandbox Agent Skill

Vercel Sandbox guidance — ephemeral Firecracker microVMs for running untrusted code safely. Supports AI agents, code generation, and experimentation. Use when executing user-generated or AI-generated code in isolation.

6k tokens

context cost

the whole folder, loaded on every use

files

instructions only

copies elsewhere

how many repositories repackaged it

240

stars on the repo

on the repository, not the skill itself

Install

one command, takes just this skill from the repository

npx skills add https://github.com/vercel/vercel-plugin --skill vercel-sandbox

What comes with it

2 679 bytes besides the instruction

overlay.yaml

The instruction itself

13 sections, as written by the author

Browser Automation with Vercel Sandbox

Run agent-browser + headless Chrome inside ephemeral Vercel Sandbox microVMs. A Linux VM spins up on demand, executes browser commands, and shuts down. Works with any Vercel-deployed framework (Next.js, SvelteKit, Nuxt, Remix, Astro, etc.).

Dependencies

pnpm add @vercel/sandbox

The sandbox VM needs system dependencies for Chromium plus agent-browser itself. Use sandbox snapshots (below) to pre-install everything for sub-second startup.

Core Pattern

import { Sandbox } from "@vercel/sandbox";

// System libraries required by Chromium on the sandbox VM (Amazon Linux / dnf)
const CHROMIUM_SYSTEM_DEPS = [
  "nss", "nspr", "libxkbcommon", "atk", "at-spi2-atk", "at-spi2-core",
  "libXcomposite", "libXdamage", "libXrandr", "libXfixes", "libXcursor",
  "libXi", "libXtst", "libXScrnSaver", "libXext", "mesa-libgbm", "libdrm",
  "mesa-libGL", "mesa-libEGL", "cups-libs", "alsa-lib", "pango", "cairo",
  "gtk3", "dbus-libs",
];

function getSandboxCredentials() {
  if (
    process.env.VERCEL_TOKEN &&
    process.env.VERCEL_TEAM_ID &&
    process.env.VERCEL_PROJECT_ID
  ) {
    return {
      token: process.env.VERCEL_TOKEN,
      teamId: process.env.VERCEL_TEAM_ID,
      projectId: process.env.VERCEL_PROJECT_ID,
    };
  }
  return {};
}

async function withBrowser<T>(
  fn: (sandbox: InstanceType<typeof Sandbox>) => Promise<T>,
): Promise<T> {
  const snapshotId = process.env.AGENT_BROWSER_SNAPSHOT_ID;
  const credentials = getSandboxCredentials();

  const sandbox = snapshotId
    ? await Sandbox.create({
        ...credentials,
        source: { type: "snapshot", snapshotId },
        timeout: 120_000,
      })
    : await Sandbox.create({ ...credentials, runtime: "node24", timeout: 120_000 });

  if (!snapshotId) {
    await sandbox.runCommand("sh", [
      "-c",
      `sudo dnf clean all 2>&1 && sudo dnf install -y --skip-broken ${CHROMIUM_SYSTEM_DEPS.join(" ")} 2>&1 && sudo ldconfig 2>&1`,
    ]);
    await sandbox.runCommand("npm", ["install", "-g", "agent-browser"]);
    await sandbox.runCommand("npx", ["agent-browser", "install"]);
  }

  try {
    return await fn(sandbox);
  } finally {
    await sandbox.stop();
  }
}

Screenshot

The screenshot --json command saves to a file and returns the path. Read the file back as base64:

export async function screenshotUrl(url: string) {
  return withBrowser(async (sandbox) => {
    await sandbox.runCommand("agent-browser", ["open", url]);

    const titleResult = await sandbox.runCommand("agent-browser", [
      "get", "title", "--json",
    ]);
    const title = JSON.parse(await titleResult.stdout())?.data?.title || url;

    const ssResult = await sandbox.runCommand("agent-browser", [
      "screenshot", "--json",
    ]);
    const ssPath = JSON.parse(await ssResult.stdout())?.data?.path;
    const b64Result = await sandbox.runCommand("base64", ["-w", "0", ssPath]);
    const screenshot = (await b64Result.stdout()).trim();

    await sandbox.runCommand("agent-browser", ["close"]);

    return { title, screenshot };
  });
}

Accessibility Snapshot

export async function snapshotUrl(url: string) {
  return withBrowser(async (sandbox) => {
    await sandbox.runCommand("agent-browser", ["open", url]);

    const titleResult = await sandbox.runCommand("agent-browser", [
      "get", "title", "--json",
    ]);
    const title = JSON.parse(await titleResult.stdout())?.data?.title || url;

    const snapResult = await sandbox.runCommand("agent-browser", [
      "snapshot", "-i", "-c",
    ]);
    const snapshot = await snapResult.stdout();

    await sandbox.runCommand("agent-browser", ["close"]);

    return { title, snapshot };
  });
}

Multi-Step Workflows

The sandbox persists between commands, so you can run full automation sequences:

export async function fillAndSubmitForm(url: string, data: Record<string, string>) {
  return withBrowser(async (sandbox) => {
    await sandbox.runCommand("agent-browser", ["open", url]);

    const snapResult = await sandbox.runCommand("agent-browser", [
      "snapshot", "-i",
    ]);
    const snapshot = await snapResult.stdout();
    // Parse snapshot to find element refs...

    for (const [ref, value] of Object.entries(data)) {
      await sandbox.runCommand("agent-browser", ["fill", ref, value]);
    }

    await sandbox.runCommand("agent-browser", ["click", "@e5"]);
    await sandbox.runCommand("agent-browser", ["wait", "--load", "networkidle"]);

    const ssResult = await sandbox.runCommand("agent-browser", [
      "screenshot", "--json",
    ]);
    const ssPath = JSON.parse(await ssResult.stdout())?.data?.path;
    const b64Result = await sandbox.runCommand("base64", ["-w", "0", ssPath]);
    const screenshot = (await b64Result.stdout()).trim();

    await sandbox.runCommand("agent-browser", ["close"]);

    return { screenshot };
  });
}

Sandbox Snapshots (Fast Startup)

A sandbox snapshot is a saved VM image of a Vercel Sandbox with system dependencies + agent-browser + Chromium already installed. Think of it like a Docker image -- instead of installing dependencies from scratch every time, the sandbox boots from the pre-built image.

This is unrelated to agent-browser's *accessibility snapshot* feature (agent-browser snapshot), which dumps a page's accessibility tree. A sandbox snapshot is a Vercel infrastructure concept for fast VM startup.

Without a sandbox snapshot, each run installs system deps + agent-browser + Chromium (~30s). With one, startup is sub-second.

Creating a sandbox snapshot

The snapshot must include system dependencies (via dnf), agent-browser, and Chromium:

import { Sandbox } from "@vercel/sandbox";

const CHROMIUM_SYSTEM_DEPS = [
  "nss", "nspr", "libxkbcommon", "atk", "at-spi2-atk", "at-spi2-core",
  "libXcomposite", "libXdamage", "libXrandr", "libXfixes", "libXcursor",
  "libXi", "libXtst", "libXScrnSaver", "libXext", "mesa-libgbm", "libdrm",
  "mesa-libGL", "mesa-libEGL", "cups-libs", "alsa-lib", "pango", "cairo",
  "gtk3", "dbus-libs",
];

async function createSnapshot(): Promise<string> {
  const sandbox = await Sandbox.create({
    runtime: "node24",
    timeout: 300_000,
  });

  await sandbox.runCommand("sh", [
    "-c",
    `sudo dnf clean all 2>&1 && sudo dnf install -y --skip-broken ${CHROMIUM_SYSTEM_DEPS.join(" ")} 2>&1 && sudo ldconfig 2>&1`,
  ]);
  await sandbox.runCommand("npm", ["install", "-g", "agent-browser"]);
  await sandbox.runCommand("npx", ["agent-browser", "install"]);

  const snapshot = await sandbox.snapshot();
  return snapshot.snapshotId;
}

Run this once, then set the environment variable:

AGENT_BROWSER_SNAPSHOT_ID=snap_xxxxxxxxxxxx

A helper script is available in the demo app:

npx tsx examples/environments/scripts/create-snapshot.ts

Recommended for any production deployment using the Sandbox pattern.

Authentication

On Vercel deployments, the Sandbox SDK authenticates automatically via OIDC. For local development or explicit control, set:

VERCEL_TOKEN=<personal-access-token>
VERCEL_TEAM_ID=<team-id>
VERCEL_PROJECT_ID=<project-id>

These are spread into Sandbox.create() calls. When absent, the SDK falls back to VERCEL_OIDC_TOKEN (automatic on Vercel).

Scheduled Workflows (Cron)

Combine with Vercel Cron Jobs for recurring browser tasks:

// app/api/cron/route.ts  (or equivalent in your framework)
export async function GET() {
  const result = await withBrowser(async (sandbox) => {
    await sandbox.runCommand("agent-browser", ["open", "https://example.com/pricing"]);
    const snap = await sandbox.runCommand("agent-browser", ["snapshot", "-i", "-c"]);
    await sandbox.runCommand("agent-browser", ["close"]);
    return await snap.stdout();
  });

  // Process results, send alerts, store data...
  return Response.json({ ok: true, snapshot: result });
}

// vercel.json
{ "crons": [{ "path": "/api/cron", "schedule": "0 9 * * *" }] }

Environment Variables

| Variable | Required | Description |

|---|---|---|

| AGENT_BROWSER_SNAPSHOT_ID | No (but recommended) | Pre-built sandbox snapshot ID for sub-second startup (see above) |

| VERCEL_TOKEN | No | Vercel personal access token (for local dev; OIDC is automatic on Vercel) |

| VERCEL_TEAM_ID | No | Vercel team ID (for local dev) |

| VERCEL_PROJECT_ID | No | Vercel project ID (for local dev) |

Framework Examples

The pattern works identically across frameworks. The only difference is where you put the server-side code:

| Framework | Server code location |

|---|---|

| Next.js | Server actions, API routes, route handlers |

| SvelteKit | +page.server.ts, +server.ts |

| Nuxt | server/api/, server/routes/ |

| Remix | loader, action functions |

| Astro | .astro frontmatter, API routes |

Example

See examples/environments/ in the agent-browser repo for a working app with the Vercel Sandbox pattern, including a sandbox snapshot creation script, streaming progress UI, and rate limiting.

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How to use it

Copy the folder

Take vercel/vercel-sandbox from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

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Install what it needs

The instructions reference npx, pnpm. Without those the skill loads but fails at the first command.