Reviewing Code Locally with AI Agents

Most developers using AI today are focused on generation, and getting code written faster. But, one of the highest-leverage things you can do with an AI agent locally is something simple but powerful: reviewing code before you push it to source control.

This article covers why local AI code review is worth adding to your workflow, why it sometimes makes sense to hand that review to a different agent than the one you're coding with, and a basic prompt to get you started.

Why Review Code Locally with AI?

Pull request reviews are great. But by the time code hits a PR, you've often already context-switched, your reviewer is context-switching, and the feedback loop is slow. Running a review locally before you even push catches issues earlier and costs less. If you've ever opened a PR and had a service like CodeRabbit review it, you'll know what I mean. Ideally, you want your code to be as reviewed as possible alone before the PR.

Here's what makes AI-assisted local review genuinely useful:

It's tireless and consistent

An AI agent will check every staged file with the same attention. It won't miss the TODO: Fix you buried because it was skimming.

It gives you a second perspective in seconds

Even if you wrote the code yourself five minutes ago, having it reflected back through a structured review surfaces things you glossed over, like missing error handling and edge cases you mentally noted and forgot.

It can be scoped

You don't have to review everything. You can ask it to review only your staged changes, or only a specific feature area.

It keeps feedback private

Sometimes you want to sanity-check something before your colleagues (or the world) see it. A local AI review is just between you and the agent.

Why You Might Hand Off to a Different Agent

Your primary coding agent is great at a lot of things. But that doesn't mean it's the best tool for every review.

Different agents have different strengths. Some are better at reasoning about security vulnerabilities. Others have been fine-tuned on specific languages or frameworks, or tend to give more opinionated feedback on architecture.

There's also a useful second-opinion argument. When the same agent that helped you write the code also reviews it, there's a risk of blind spots compounding. The agent may be anchored to the same assumptions it made during generation. A different model, with different training and different tendencies, can approach the same code with genuinely fresh eyes.

Finally, there's the benchmarking case. You might simply want to know which agent gives better reviews for your codebase. Running the same diff through two agents and comparing the output is a fast way to develop an intuition for where each one excels.

A Basic Prompt to Get Started

You don't need a full skill file to start. Here's a simple prompt you can drop into any AI agent in your terminal:

Review my staged git changes.

Run: git diff --cached

Look at what's changed and assess it across these severity levels:
- 🔴 Critical — bugs, security issues, data loss risks
- 🟠 Error — logic errors, missing error handling
- 🟡 Warning — code smells, performance concerns, edge cases
- 🔵 Suggestion — better approaches, readability improvements
- ⚪ Nitpick — style, naming, minor preferences

Group findings by severity. For each finding, include the file and line reference, a description of the issue, and a suggested fix where applicable. End with a short summary and whether the changes look ready to merge.

That's it. Run it after git add and before git push. You'll be surprised what it catches.

For a feature-scoped review rather than a diff review, adjust the first two lines:

Review the authentication feature in this codebase.

Use Glob and Grep to find all relevant files, then read them.

Taking It Further

The prompt above works, but a proper skill file goes much further, handling argument parsing, auto-detecting whether you have staged changes, scoping to a feature, delegating to other agents, and formatting output consistently every time.

If you want to build a production-quality code review skill file from scratch and understand exactly how it works, we cover it in Unlearn, and more importantly show you how to integrate it into your daily workflow.

The habit of reviewing before pushing is one of the easiest wins in AI-assisted development. The tooling is already there. You just need to use it.