A technical tour of Vercel's Agent Skills collection: five curated skills, 100+ rules, and practical ways to plug them into real-world AI coding workflows.

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Vercel Agent Skills: Structured Knowledge for AI Coding Agents

AI coding agents are powerful, but they’re only as reliable as the knowledge you hand them. Vercel’s Agent Skills collection is a concrete step toward repeatable, auditable expertise: packaged instructions and rules that an agent can load on demand instead of improvising every time.

This post explains what agent skills are, what’s inside Vercel’s collection, and how you can use these skills to make AI-assisted coding more consistent in real projects.

What are agent skills?

Agent skills are structured knowledge bundles for AI agents: a folder with a SKILL.md file (metadata + instructions) and optional scripts, templates, or references. Instead of copying a long prompt into every session, you install a skill once and the agent loads it only when the task matches. This keeps context lightweight while making behavior predictable.

In practice, skills act like version-controlled playbooks for agents. They can encode performance rules, code review checklists, deployment workflows, or design guidelines with real code examples.

Vercel’s collection overview

Vercel’s agent-skills repo ships five production-grade skills and 100+ rules, focused on front-end performance, UI quality, and deployment automation. The collection is not just “tips”—it’s a structured rule system designed for automated use in AI coding workflows.

The five skills are:

react-best-practices — React + Next.js performance optimization rules
web-design-guidelines — UI audit rules across accessibility, performance, and UX
react-native-guidelines — React Native performance + architecture patterns
composition-patterns — scalable React component composition patterns
vercel-deploy-claimable — one-command deploys to Vercel with claimable ownership

Skill-by-skill breakdown (with examples)

1) react-best-practices

This skill codifies Vercel Engineering’s React and Next.js performance guidance into 40+ (and growing) rules. The rules are prioritized by impact, which makes them excellent for automated reviews and targeted refactors.

Example rule: parallelize independent async work.

const user = await fetchUser()
const posts = await fetchPosts()
const comments = await fetchComments()

const [user, posts, comments] = await Promise.all([
  fetchUser(),
  fetchPosts(),
  fetchComments()
])

Why it matters: It’s a tangible, enforceable rule an agent can detect and fix without guesswork.

2) web-design-guidelines

This skill is a UI audit engine. It fetches Vercel’s web interface guidelines and checks your code against 100+ rules spanning accessibility, typography, interaction, performance, and i18n.

In other words: if your agent can already review UI code, this skill makes the review complete and consistent.

Use cases:

“Audit this UI for accessibility regressions.”
“Check my component for focus-visible, form validation, and hover states.”
“Make sure performance rules (preconnect, lazy loading, virtualization) are followed.”

3) react-native-guidelines

Mobile performance and architecture issues are easy to miss. This skill gives AI agents platform-specific rules for React Native and Expo apps.

Example rule: virtualize any scrollable list.

function Feed({ items }: { items: Item[] }) {
  return (
    <ScrollView>
      {items.map((item) => (
        <ItemCard key={item.id} item={item} />
      ))}
    </ScrollView>
  )
}

import { FlashList } from '@shopify/flash-list'

function Feed({ items }: { items: Item[] }) {
  return (
    <FlashList
      data={items}
      renderItem={({ item }) => <ItemCard item={item} />}
      keyExtractor={(item) => item.id}
    />
  )
}

Why it matters: These are agent-friendly rules that have clear, mechanical fixes.

4) composition-patterns

This skill focuses on component API design and how to avoid unreadable “boolean prop soup.” It gives practical patterns for explicit, composable components instead of monolithic conditionals.

Example rule: avoid boolean prop proliferation.

function Composer({ isThread, isDMThread, isEditing }: Props) {
  return (
    <form>
      {isDMThread ? <AlsoSendToDMField /> : isThread ? <AlsoSendToChannelField /> : null}
      {isEditing ? <EditActions /> : <DefaultActions />}
    </form>
  )
}

function ThreadComposer({ channelId }: { channelId: string }) {
  return (
    <Composer.Frame>
      <Composer.Input />
      <AlsoSendToChannelField id={channelId} />
      <Composer.Footer>
        <Composer.Formatting />
        <Composer.Submit />
      </Composer.Footer>
    </Composer.Frame>
  )
}

Why it matters: This is not a stylistic preference—these patterns reduce API complexity and make refactors safer for both humans and agents.

5) vercel-deploy-claimable

This skill is pure workflow automation: package a project, auto-detect the framework, deploy to Vercel, and return a preview URL + claim link.

Example usage:

bash /mnt/skills/user/vercel-deploy/scripts/deploy.sh /path/to/project

Example output:

✓ Deployment successful!

Preview URL: https://skill-deploy-abc123.vercel.app
Claim URL:   https://vercel.com/claim-deployment?code=...

Why it matters: It bridges AI assistance with real deployment, while keeping ownership transfer explicit and safe.

Why this matters for AI coding agents

Agent skills shift AI coding from “clever autocomplete” to repeatable engineering practice:

Consistency: Agents follow the same rules every time.
Auditability: Skills are readable, versioned files.
Precision: Rules target specific, fixable issues instead of vague advice.
Scalability: Teams can codify standards once and reuse them across projects.

For teams already using AI tools, this is how you go from “helpful suggestions” to reliable automation.

How this compares to the OpenClaw skills approach

OpenClaw also uses the Agent Skills format, but its focus is runtime skill loading and precedence. It supports bundled skills, user-managed skills, and workspace-level overrides, with a clear priority order and load-time filtering based on environment, config, and available binaries.

Vercel’s collection is different: it’s curated, opinionated expertise that you install into any Agent Skills-compatible tool. OpenClaw gives you the plumbing; Vercel gives you the playbooks.

The two are complementary: use OpenClaw to manage where skills live and how they’re loaded, and Vercel’s skills to define what the agent should do.

How developers can use these skills

Install the collection
```
npx add-skill vercel-labs/agent-skills
```
Let your agent auto-trigger skills
- Ask it to review a Next.js page for performance
- Request a UI audit for accessibility or UX issues
- Deploy a project and get a live preview URL
Customize or extend
- Fork the repo and add team-specific rules
- Add a references/ folder with internal docs
- Create a new skill for your deployment pipeline or design system

Final take

Vercel’s Agent Skills collection isn’t a marketing demo—it’s real engineering guidance packaged for AI. If you’re already relying on agents to write and review code, this is the missing layer that makes the output predictable, safe, and scalable.

If you want AI to act like a senior engineer, give it senior-grade playbooks.