You already have an AI coding agent. Probably more than one. The day-to-day mechanics of writing code have already shifted in ways that would have been hard to imagine three years ago.

But there’s a gap. You can feel it every time you accept a suggestion and then immediately rewrite half of it because that’s not how we do it here. The AI is technically right, but it’s not right for your codebase. That’s the gap Scrubby closes, and it’s why we think codebase intelligence is going to be a non-negotiable layer of the AI dev stack within the next year or two.

What you actually spend your time on

If you keep an honest log of where your hours go, the pattern is pretty clear. Writing brand-new logic is maybe 20% of your day. The rest is some mix of reading other people’s code to figure out how things work in this part of the repo, hunting for the right place to put a new file, and adjusting AI suggestions because they almost-fit but-not-quite.

Almost none of that is the fun part of being a developer. It’s connective tissue, and it’s exactly what an AI agent armed with codebase intelligence eliminates from your day.

The “almost-fits” problem

Right now, your AI assistant has a context window full of three things: your current file, a few files the editor’s context engine guessed might be relevant, and whatever it remembers from earlier in the conversation. That’s a lot of tokens, but it’s the wrong tokens. None of it tells the model that this part of the codebase has its own error-handling convention that diverges from the rest of the app for a reason that goes back to a 2024 incident nobody wrote down.

Without that context, the model produces code that compiles and tests pass, but lands in PR review with five comments about structure. You spend an hour fixing what should never have been written that way to begin with.

Recent research from CodeRabbit found that AI-authored PRs contain roughly 1.7× more issues overall than human-authored ones, with logic errors 75% more common and security issues up to 2.74× higher. The model is fine, so the context is the problem.

What Scrubby gives your AI agent

Scrubby connects to your repo, builds an index of its domains, conventions, connections, and history, and exposes that knowledge to your AI agent through an MCP server.

Once it’s wired up, your agent gets a small set of new tools it can call whenever it needs to:

  • scrubby_review on the file it’s about to edit, returning what the file does, what conventions apply to it, and what domains it connects to.
  • scrubby_get_domains to see the architectural map of your repo, so it stops guessing from filenames.
  • scrubby_get_network to understand the blast radius of a change before making it.
  • scrubby_review_changeset to check a set of changes against your team’s actual patterns before you push.

The agent learns to call these tools the same way it already calls read_file and grep, naturally and when it needs the information. The difference is that the answers it gets back are real answers grounded in your codebase, instead of best guesses from a model that’s never seen your repo before.

The day-to-day shift

Here’s what changes once Scrubby is in your loop:

You stop rewriting suggestions. The code your agent generates fits the file it’s going into. You’re back to reviewing logic instead of structure.

Onboarding stops being a tax. When you (or a new teammate) drop into an unfamiliar part of the codebase, the agent already knows the conventions there. You don’t have to spend two days learning where things go before you can write the first line.

Your token usage drops. This one surprises people. When the agent has Scrubby to query, it stops aimlessly grepping through your repo for context. Studies show AI coding agents waste up to 80% of their tokens just finding things. Scrubby short-circuits that, so the agent asks one targeted question, gets a structured answer, and gets back to writing code.

PR review becomes signal instead of noise. When the agent has already considered conventions before generating code, the comments you get from human reviewers are about logic and design, instead of about wrong directories and unfamiliar patterns.

Your AI agent stops feeling like a stranger. This is the subtle one. After a week of working with Scrubby in the loop, the code coming out of your agent starts to feel like code your team would have written. The model didn’t change, but the context did.

What about teams that don’t use AI agents heavily?

Honestly, those teams are getting rarer. But even if you’re a holdout, Scrubby still helps. The GitHub App reviews PRs against your codebase’s actual patterns, catching the class of issue that linters and generic AI reviewers can’t see. New hires get a tool that surfaces the unwritten rules of your codebase as they write. Senior engineers stop being the only people who remember why a thing is structured the way it is.

But if you are using AI agents heavily, which most of us are now, Scrubby is the difference between an assistant that’s fast at being wrong and an assistant that’s fast at being right.

Getting started

The fastest way to see what Scrubby does for your day-to-day is to install the MCP server in your AI editor and the GitHub App on your repo. The first index runs in the background, and within a few minutes your agent has access to the codebase intelligence layer. Most developers feel the difference in the first hour.

The bigger shift takes a couple of weeks. You’ll notice you’ve stopped rewriting AI output as often. You’ll notice your PRs are getting through review faster. You’ll notice you’re getting back the time you used to spend on connective-tissue work, and spending it on the parts of the job that are actually interesting.

That’s the version of AI-powered development we’ve been promised for a few years now. Scrubby is how you actually get there.

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