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vercel-sandboxVercel sandbox 搜索

Agent Skill

vercel-sandbox 用于处理浏览器自动化、网页检查和页面信息提取,适合在 OpenClaw 中需要让 Agent 打开页面、读取网页或验证前端流程时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

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2,664

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安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:vercel-sandbox(Vercel sandbox 搜索)
来源仓库:https://github.com/daxiangnaoyang/vercel-sandbox
安装命令:
openclaw skills install vercel-sandbox
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install vercel-sandbox

简介

vercel-sandbox 在 microVM 内运行代理浏览器,实现 Vercel 应用的自动化测试。

  • 适用于 OpenClaw 中前端 E2E 测试或用户行为模拟场景。
  • 支持 Chrome DevTools 协议,可捕获网络请求与 DOM 快照。
  • 使用前应限制沙箱网络出口,防止意外数据外传或恶意脚本执行。
  • 建议每次会话后重置环境,避免状态污染影响后续测试用例。

SKILL.md

name
vercel-sandbox
description
Run agent-browser + Chrome inside Vercel Sandbox microVMs for browser automation from any Vercel-deployed app. Use when the user needs browser automation in a Vercel app (Next.js, SvelteKit, Nuxt, Remix, Astro, etc.), wants to run headless Chrome without binary size limits, needs persistent browser sessions across commands, or wants ephemeral isolated browser environments. Triggers include "Vercel Sandbox browser", "microVM Chrome", "agent-browser in sandbox", "browser automation on Vercel", or any task requiring Chrome in a Vercel Sandbox.

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

VariableRequiredDescription
AGENT_BROWSER_SNAPSHOT_IDNo (but recommended)Pre-built sandbox snapshot ID for sub-second startup (see above)
VERCEL_TOKENNoVercel personal access token (for local dev; OIDC is automatic on Vercel)
VERCEL_TEAM_IDNoVercel team ID (for local dev)
VERCEL_PROJECT_IDNoVercel project ID (for local dev)

Framework Examples

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

FrameworkServer code location
Next.jsServer actions, API routes, route handlers
SvelteKit+page.server.ts, +server.ts
Nuxtserver/api/, server/routes/
Remixloader, 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.

适合场景

01

OpenClaw 用户查找和安装 Skill 时

02

用户想查找某类 Agent Skill 时

03

需要根据任务场景推荐可安装能力包时

04

需要对比不同来源的安装命令和来源信息时

能力概览

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能力 3

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能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

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安装前确认

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