30分钟用Vercel Eve构建AI代码审查机器人

Build an AI code review bot in 30 minutes with Vercel Eve

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WorkOS—Make your app enterprise-ready today

Claire built an AI agent that reviews pull requests, scores their risk, auto-approves the safest ones, and sends anything questionable to Slack. In this episode, she breaks down how she built the entire thing in one Codex session using Vercel Eve, and why letting AI review AI-generated code may be a lot less risky than it sounds.

Biggest takeaways:

• You don’t need a human to review every AI-generated PR. That may sound reckless, but it’s increasingly looking like the smarter operating model. Intercom has already shown this can work at scale: PRs approved by its AI system move five times as fast as human-reviewed ones and have a lower revert rate. In other words, the AI-reviewed code isn’t just shipping faster—it’s less likely to need fixing after it reaches production.
• The key is having a clear way to separate changes that can skip human review from ones that can’t. The risk model used here scores each PR across six dimensions: the size of the change, its blast radius, how easily it can be reversed, its data and security implications, its operational impact, and whether tests and CI have actually been completed. Anything below 24 points is classified as low risk and cleared by the agent; anything above 64 goes straight to a human for review. The exact numbers matter less than turning a vague judgment call into a repeatable system.
• Vercel’s Eve may be the fastest way to deploy a serious internal AI agent across Slack and GitHub without spending weeks building infrastructure. It handles the annoying plumbing—connectors, refresh tokens, sandboxing, and routing across channels—so the actual work becomes writing instructions and skills in Markdown instead of babysitting OAuth flows.
• A useful internal agent can now be built and deployed in a single Codex session, starting with a prompt that’s only a couple of sentences long. In this case, the initial ask was essentially: build a GitHub bot that waits for checks to pass; grades each PR as low, medium, or high risk; and automatically approves the low-risk ones. Everything after that was steering and refinement, not a giant up-front specification.
• Browser use removes much of the configuration tax that makes agent setup feel harder than it should. Creating a Slack bot and GitHub app manually normally means clicking through endless permission screens, choosing scopes, and managing tokens. Codex handled almost all of that through the browser. The human’s job was mostly to click “save” and complete 2FA. What usually takes hours took minutes.
• SOC 2 compliance and automatically approved PRs are not inherently at odds. The important part is making the process legible: the risk model needs to be reflected in the company’s code-review and security policies, every decision needs to be logged, and the resulting audit trail needs to be easy to query and defend. The security team’s role is to help design the right framework, not simply block automation because it feels unfamiliar.
• The operational design matters just as much as the underlying technology. Merge Mommy doesn’t actually merge anything. Instead, it posts a gray check in GitHub as a signal, then sends a Slack message with the risk score and a note saying the PR is ready to approve and merge. That small handoff preserves human accountability for the final action while eliminating most of the cognitive work involved in reviewing a routine change.
• Evals are what keep internal agents trustworthy after the novelty wears off. Intercom logs every PR review its agent produces and then has an engineer assess whether the score and recommendation were correct. That’s the same discipline strong teams already bring to customer-facing AI products. Internal agents may feel less visible, but when they touch something as important as the codebase, they need the same protection against regressions.
• The surprising thing about building an Eve agent is how little “building” is actually involved. The full instructions for Merge Mommy fit on roughly a page: a few paragraphs, a handful of bullets, and a short skill file. There isn’t much framework-specific magic to learn. The core skill is simply being able to explain, clearly and precisely, what the agent should do.

Blog from this episode:

How I Built ‘Merge Mommy’: My AI Bot for Auto-Reviewing Pull Requests with Vercel Eve: https://www.chatprd.ai/how-i-ai/merge-mommy-vercel-eve-ai-bot-for-auto-reviewing-pull-requests

Claude Code for normal people: skills, voice mode, and how to collaborate with AI

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• Bolt.new—Turn your idea into a real product
• Hyperagent—Deploy fleets of agents that handle real work

Grace Clarke is an AI educator and former marketing consultant who rebuilt her entire service business around Claude. In this episode, she walks through the three Claude skills that run her business: an hourly client pipeline, a proposal builder, and a voice guide that teaches Claude how she thinks. She also shows how she replaced Gmail with her own Claude-powered inbox, why she believes intent engineering matters more than prompt engineering, and how non-technical people can start building useful AI workflows without writing code.

Biggest takeaways:

• You don’t need to be technical to build a business workflow with Claude. You need to be clear about what’s broken. Grace started by opening Claude Code and talking for a few minutes about everything driving her crazy: too many emails, client communication that didn’t feel warm enough, and 20 hours of admin every week. Claude helped turn that into an operating system for her business.
• Intent engineering matters more than prompt engineering. Grace doesn’t spend time crafting the perfect prompt. She explains the problem, describes the outcome she wants, and asks Claude to come back with a proposal. Her philosophy is simple: the burden of figuring out what to do next shouldn’t always fall on the person. Give Claude enough context, and it should be able to study how you work and bring you strong ideas to react to.
• Grace’s voice guide isn’t really a writing guide. It’s closer to a “think like Grace” file. It captures how she makes decisions, what she believes about teaching versus consulting, what good communication sounds like, and even the kinds of LinkedIn posts she never wants to resemble. Whenever she sees something that makes her cringe, she sends Claude a voice note and updates the guide.
• Doing all of your email inside Gmail means almost none of that work compounds. Every response contains useful context: how a client communicates, what tone works with them, what they care about, and the history of the relationship. But if that context stays buried in Gmail, the AI never gets smarter from it. Grace moved her inbox workflow into Claude so every interaction can become useful context for the next one.
• Grace has developed a surprisingly elegant way to move between Claude Code and Cowork. She uses Claude Code when she wants something proactive that will go figure things out. Then, when she wants a more visual and approachable environment, she has Claude Code create a Markdown handoff file and drops it into a new Cowork session. The result is a simple way to carry context from one environment to another without starting over.