If your team still treats code review as a single human checkpoint at the end of a pull request, you are making review slower than it needs to be. Good teams now split review into layers. Machines catch the repetitive issues first. Humans spend their time on architecture, product judgment, and risk.

This article is for engineering teams that want faster pull requests without lowering standards. We will look at five ASE skills that automate the parts of code review that are deterministic, noisy, or easy to miss under time pressure. Together, they cover style, security, browser behavior, local guardrails, and regression risk.

Table of contents

• What good code review automation should catch
• 1. ESLint Code Review
• 2. Trivy Security Scanner for Containers and IaC
• 3. Playwright Cross-Browser Testing and Automation Framework
• 4. Lefthook Git Hooks Manager
• 5. Keploy API Test Generation and Regression Testing Platform
• A recommended stack by team size
• FAQ

What good code review automation should catch

Strong code review automation is not about replacing reviewers. It is about removing low-value repetition. A healthy pipeline should catch at least five classes of problems before a person comments on the diff:

• Style and correctness issues, such as undefined variables, dead imports, or unsafe patterns.
• Security findings, including vulnerable dependencies, leaked secrets, or risky infrastructure configuration.
• UI regressions that compile cleanly but break critical user paths.
• Local workflow mistakes, like skipped tests or commits that bypass team standards.
• Behavior regressions in APIs and integration flows.

That is why the best code review automation skills tend to come from different categories in the ASE catalog. You want multiple checkpoints that produce different kinds of evidence.

1. ESLint Code Review

If your team writes JavaScript or TypeScript, this is the most obvious starting point. ASE’s ESLint Code Review skill gives an agent a reliable way to inspect source changes through the ESLint ecosystem rather than vague natural-language heuristics.

That matters because good review automation needs stable interfaces. ESLint rules, flat config, plugins, and autofix behavior are explicit. An agent can explain why a rule failed, suggest a fix, or even apply a safe autofix before the reviewer gets involved.

Best use case: teams that want to eliminate noisy style comments and catch correctness issues before PR review starts.

# Example pre-review step
pnpm eslint . --max-warnings=0

# Common agent workflow
# 1. run ESLint
# 2. summarize failures by rule
# 3. autofix safe issues
# 4. leave only human-relevant concerns in the PR

On large teams, even saving 3 to 5 style comments per PR adds up quickly. Across 100 pull requests per month, that is hundreds of comments your senior engineers no longer need to type.

2. Trivy Security Scanner for Containers and IaC

Security review is where many teams still rely too heavily on memory. Reviewers notice obvious secrets, but they rarely catch every vulnerable package, container issue, or risky Terraform setting by sight alone. That is exactly where Trivy Security Scanner for Containers and IaC earns its place.

Trivy gives your review pipeline a structured security pass. It can scan dependencies, container images, repositories, and infrastructure-as-code files. For an agent, that turns security review from a fuzzy instruction like “look for problems” into a concrete, repeatable workflow.

Best use case: teams shipping containers, infrastructure changes, or dependency updates on a weekly basis.

# Example CI review stage
trivy fs .
trivy config .
trivy image ghcr.io/acme/app:pr-482

The United States National Institute of Standards and Technology (NIST) and OWASP both emphasize repeatable controls over ad hoc review, which is why automated scanners belong in modern review pipelines, not as an optional extra. See OWASP Top 10 and NIST Cybersecurity Framework for the broader rationale.

3. Playwright Cross-Browser Testing and Automation Framework

Static analysis cannot tell you whether the “Save” button disappeared, the checkout flow broke on WebKit, or a login modal now traps keyboard focus. For that, you need browser-level evidence. The Playwright Cross-Browser Testing and Automation Framework skill is one of the highest-leverage additions you can make to code review automation for frontend-heavy teams.

Playwright lets an agent run deterministic user flows, capture screenshots, compare outcomes across browsers, and report failures in plain English. It is especially effective when reviewers need proof that a change works, not just a claim in the PR description.

Best use case: product teams with web apps, admin dashboards, or checkout flows where visual correctness matters.

import { test, expect } from '@playwright/test';

test('pricing page CTA stays visible', async ({ page }) => {
  await page.goto('http://localhost:3000/pricing');
  await expect(page.getByRole('link', { name: 'Start free trial' })).toBeVisible();
});

If your team already uses the Microsoft Playwright MCP variant, it also fits neatly into agent-driven review workflows where browser traces and screenshots are part of the review artifact.

4. Lefthook Git Hooks Manager

Code review automation starts before CI. The earlier you catch an issue, the cheaper it is to fix. That is why Lefthook Git Hooks Manager belongs on this list even though it is not a “review engine” in the classic sense.

Lefthook standardizes pre-commit and pre-push checks across the team. An agent can use it to enforce local linting, test subsets, secret checks, or file validation before bad changes ever reach a pull request. That reduces CI churn and shortens the feedback loop from minutes to seconds.

Best use case: teams tired of seeing obviously broken code reach remote branches.

pre-commit:
  parallel: true
  commands:
    lint:
      run: pnpm eslint {staged_files}
    unit:
      run: pnpm vitest related {staged_files}
    secrets:
      run: trivy fs --scanners secret .

For teams that want a tighter review gate, combine Lefthook locally with the publishing checklist ideas from our post on skill quality verification once that article is live, or review the broader quality guidance in How to Build a Code Review Skill That Catches What Linters Miss.

5. Keploy API Test Generation and Regression Testing Platform

The hardest review bugs are often behavioral. The code looks reasonable. The tests are green. The diff is small. And yet the API now returns a subtly different payload that breaks a client three services away. Keploy API Test Generation and Regression Testing Platform is built for that problem.

Keploy helps agents record traffic, generate tests, and compare behavior across changes. That makes it a strong addition for backend teams that want review automation to focus on actual output changes, not just static code shape.

Best use case: API teams, platform groups, and services with lots of integrations.

# Example regression workflow
keploy record -c "npm run dev"
keploy test -c "npm run dev" --delay 10

GitHub’s own engineering guidance on code review emphasizes small changes and fast feedback loops. Behavior-level regression checks support that goal because they reduce the need for reviewers to mentally simulate every downstream effect. See GitHub engineering advice for the broader review philosophy.

A recommended stack by team size

You do not need to install all five skills on day one. The right code review automation stack depends on your codebase and release risk.

A practical rollout target is this: by the time a reviewer opens a PR, at least 80% of style issues, 100% of dependency and IaC scans, and the top 3 to 5 critical user journeys should already have machine-generated evidence attached.

Frequently asked questions

What is the best skill for automating code review?

For JavaScript and TypeScript teams, ESLint Code Review is the best first skill because it removes the largest volume of repetitive comments. But the best overall setup uses multiple skills, not one.

Can AI code review replace human reviewers?

No. AI review is strongest at deterministic checks, policy enforcement, and summarizing evidence. Human reviewers still need to judge architecture, product tradeoffs, naming, and long-term maintainability.

How do you automate code review without slowing CI?

Run the fastest checks locally with Lefthook, keep lint and security scans incremental where possible, and reserve heavier browser or regression suites for high-risk pull requests or protected branches.

Final thought

The fastest code review teams are not the ones with the fewest standards. They are the ones that push the repeatable checks to automation and protect human attention for the calls that actually require judgment.

If you are building that workflow now, start with ESLint Code Review and Lefthook, then layer in Trivy, Playwright, and Keploy as your risk surface grows.

And if you want more patterns for agent-driven engineering workflows, read our earlier posts on building a code review skill and CI/CD and deployment skills.