Headless Chrome browser automation inside ephemeral Vercel Sandbox microVMs for any Vercel-deployed framework.
Works with
Spins up isolated Linux VMs on demand to run agent-browser commands, then shuts down automatically; works with Next.js, SvelteKit, Nuxt, Remix, Astro, and other Vercel frameworks
Supports multi-step workflows with persistent browser sessions across sequential commands (navigation, form filling, screenshots, accessibility snapshots)
Includes sandbox snapshots for sub-second s
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionvercel-sandboxExecute the skills CLI command in your project's root directory to begin installation:
Fetches vercel-sandbox from vercel-labs/agent-browser and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate vercel-sandbox. Access via /vercel-sandbox in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
Submit your Claude Code skill and start earning
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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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.).
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.
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();
}
}
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 };
});
}
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(awaitMake data-driven prioritization decisions faster
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
mattpocock/skills
Keeps context tight: vercel-sandbox is the kind of skill you can hand to a new teammate without a long onboarding doc.
Keeps context tight: vercel-sandbox is the kind of skill you can hand to a new teammate without a long onboarding doc.
I recommend vercel-sandbox for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
We added vercel-sandbox from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
vercel-sandbox has been reliable in day-to-day use. Documentation quality is above average for community skills.
vercel-sandbox fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
vercel-sandbox has been reliable in day-to-day use. Documentation quality is above average for community skills.
vercel-sandbox reduced setup friction for our internal harness; good balance of opinion and flexibility.
We added vercel-sandbox from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
Solid pick for teams standardizing on skills: vercel-sandbox is focused, and the summary matches what you get after install.
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