Preparing a GitHub Repository for Claude, ChatGPT, or Gemini

There is a slightly annoying step that comes before asking an AI coding assistant a question about an existing project.

Getting the code into the conversation.

For a small project, it is easy. Open a couple of files, copy them, paste them into Claude or ChatGPT, and move on.

A real repository is usually different.

You may have dozens or hundreds of files, and the question you are trying to answer may only involve ten of them. Downloading the whole repository does not solve that problem either. You still have to find the useful files, ignore the unrelated ones, and assemble everything into something the AI can actually work with.

That preparation can take longer than the question itself.

There is a simpler way to approach it when the project is already sitting on GitHub or GitLab: start from the repository page and select the part of the codebase that matters.

The repository is already in front of you

This is the basic idea behind the Repoprep Chrome extension.

GitHub + Repoprep extension overview

Open a GitHub or GitLab repository, click the extension, and it reads the repository’s file tree.

You can then search for files, select the ones you need, and generate a single context file from the selection.

There is no need to download the repository first just to prepare a prompt.

That sounds like a small change to the workflow, and it is. But it removes several small jobs that tend to get repeated throughout the day.

Give the AI the relevant part, not everything

One of the easiest mistakes when using AI with a codebase is assuming that more context is always better.

Sometimes it is. Usually it depends on the question.

Imagine a Next.js repository with 150 files.

You want to find out why authentication stops working after a user refreshes the page.

You probably do not need all 150 files.

The useful context might be:

• the login component
• the session or authentication utility
• middleware
• the relevant API route
• a configuration file
• package.json

The rest may not add anything useful to that particular conversation.

Selecting the relevant files first gives the AI a much narrower problem to understand.

It also makes it easier for you to see exactly what you are giving it.

Searching is often faster than browsing

Large repositories are not always well organized, and even when they are, navigating through several nested directories just to find five related files can be tedious.

Repoprep Chrome extension on a GitHub repository

The extension includes a search field for the file list.

If you are looking at an unfamiliar project, you can search for things like:

auth

middleware

checkout

database

payment

or whatever part of the application you are investigating.

The matching files and their paths are then available for selection.

It is a small convenience, but this is one of those things that becomes more useful the more often you use it.

A repository contains a lot of things the AI probably does not need

Source files are only one part of most projects.

There are dependency directories, generated files, build output, binaries, lock files, source maps, and other project noise.

Then there are files that should not be pasted into an AI conversation at all.

Environment files and credentials are the obvious examples.

The extension filters common sources of noise and sensitive files before building the context.

It removes things such as node_modules, build output, binaries, .env files, API keys, SSH keys, and cloud credentials from the normal selection flow.

That serves two purposes.

It keeps the context smaller, and it reduces the chance of accidentally including something sensitive.

The file selection can still be reviewed before the context is generated. I think that part matters. Automatic filtering is useful, but it should not replace looking at what you are about to send.

The generated result keeps the file paths

Once the selection is ready, the extension extracts the files and combines them into a structured context file.

Extraction result of Repoprep

The original paths are retained.

For example:

===== PROJECT STRUCTURE =====

src/
├── components/
│   └── auth/
│       └── LoginForm.tsx
├── lib/
│   └── auth.ts
├── middleware.ts
└── package.json

The file contents follow underneath, with each file clearly separated.

This matters when the AI needs to understand how several parts of a project connect.

A long collection of pasted code is not particularly helpful if there is no clear indication of where each piece came from.

Keeping the paths makes the resulting context much easier to follow.

It is useful for focused questions

The workflow becomes especially useful when the question is narrower than the repository itself.

For example:

“Where is the user session created?”

You might only need the authentication utilities, middleware, and login flow.

Or:

“Why does this component rerender when the cart changes?”

You might only need the component, the relevant state/store code, and perhaps one or two parent components.

Or:

“How does this project handle database connections?”

You could select the database utility, configuration, API layer, and package information.

The idea is the same in each case: prepare the context around the question instead of treating the entire repository as the context.

Private repositories work too

Public repositories can be used directly.

For private GitHub or GitLab repositories, a personal access token can be added through the extension settings.

The token stays on the local device rather than being sent to a Repoprep server.

Repository data is fetched directly from GitHub or GitLab.

That means the same workflow can be used when the codebase is private without uploading the repository to another service just to prepare an AI prompt.

What about local projects?

The Chrome extension is aimed at repositories that are already on GitHub or GitLab.

For a project that is sitting on your computer, the Repoprep web app provides the corresponding workflow for local folders and ZIP files.

So there are really two starting points:

GitHub or GitLab repository: use the extension.

Local project: use the web app.

The goal is the same in both cases: select the useful files, remove the obvious noise, and create a context that is ready to use.

Context size is easier to see before you send it

There is another practical problem with combining a lot of source files: you do not always have a good sense of how large the result is.

The extension shows the number of selected files, approximate size, and estimated token count as you build the context.

That makes it easier to notice when a supposedly small selection has turned into a very large one.

The Pro version also includes a model compatibility check for supported models.

It is not a replacement for knowing the limits of the model you are using, but it gives you another useful indication before you paste a large context into a conversation.

A simple example

Suppose you open a GitHub repository and find around 90 files.

You are trying to understand one part of the application.

Instead of downloading everything, you search the repository for the relevant files and end up selecting:

LoginForm.tsx

auth.ts

middleware.ts

session.ts

api/login.ts

package.json

You review the selection, check the estimated context size, and generate the context.

Now the AI has the code needed to answer the question without having to work through the rest of the repository.

It is not a revolutionary change.

It is just less manual work.

The extension is intentionally simple

There are plenty of tools that try to turn AI coding into a complete development environment.

That is not really the problem this solves.

The extension handles one specific part of the workflow:

getting useful repository context into an AI tool.

You can still use Claude, ChatGPT, Gemini, or another assistant however you normally do.

Repoprep is simply the step before that conversation.

For someone who works with repositories regularly, removing a repetitive step is often more useful than adding another large feature.

The core is open source

The core parsing and context-building engine used by Repoprep is open source.

The repository is available here:

https://github.com/Kishoren1/repoprep-app

That also gives developers a way to look at how the context-building side works rather than treating the entire process as a black box.

A more practical way to work with repository context

AI assistants are becoming much better at working with codebases.

The part that still tends to be surprisingly manual is deciding what the AI should actually see.

For a small project, copying a few files is fine.

For a larger repository, it is easy to spend several minutes preparing the context before you even get to the question you wanted to ask.

Starting from the GitHub or GitLab page makes that process a little simpler.

Select the files that matter. Leave the rest behind. Generate the context. Then get back to the actual problem.

That is what the Repoprep Chrome extension is designed to do.

Chrome Web Store: https://chromewebstore.google.com/detail/repoprep/ocafmohghoihkdmmedcddpbggengjonc

Repoprep: https://www.repoprep.com/