Preparing GitHub Repositories for AI Coding Assistants

When an AI coding assistant needs to understand an existing project, the difficult part is often not the question. It is preparing the codebase.

A GitHub repository may contain hundreds of files, while a particular task usually depends on a much smaller part of it. Finding those files, copying them into an AI chat, checking what can be left out, and doing the same thing again for the next question gets tedious surprisingly fast.

Downloading the repository first solves part of the problem, but it also adds another step to the workflow.

A more convenient approach is to work directly from the repository page and prepare only the context that is relevant to the task.

That is the idea behind the Repoprep Chrome extension.

It works from GitHub and GitLab repository pages, where files can be searched, selected, and turned into a single structured context file for Claude, ChatGPT, Gemini, or other AI tools.

Start with the repository you already have open

The extension is designed around a simple workflow.

You are already looking at a repository on GitHub or GitLab. Instead of cloning or downloading it just to prepare some files for an AI conversation, open the Repoprep extension from the browser.

It identifies the repository and branch, reads the file tree, and shows the available files inside the extension.

From there, you can search through the list and choose what should be included.

The extension also shows useful information about the selected files, including file count, size, and estimated token count.

The result is a much shorter path from repository → selected files → AI context.

You usually don't need the entire repository

This is where repository context gets interesting.

Suppose you are looking at a Next.js project and want to understand its authentication flow.

You probably do not need every page, stylesheet, image, test fixture, and configuration file in the project.

The useful context might be limited to:

the login components
authentication utilities
middleware
session-related code
a relevant API route
package.json
a small amount of documentation

Selecting only those files gives the AI a much more focused view of the problem.

The same approach works for debugging, code reviews, refactoring, or simply trying to understand an unfamiliar project.

The goal is not to give the AI as much code as possible.

It is to give it the code that actually matters.

Searching through a large repository

Manually opening folders and copying files becomes especially annoying when the repository is large.

Repoprep provides a search field inside the extension, so the file list can be narrowed down quickly.

Looking for authentication code?

Search for auth.

Tracing middleware?

Search for middleware.

Working on checkout?

Search for checkout, cart, or whichever part of the project is relevant.

The file paths remain visible, so it is easier to understand where each file fits in the project before selecting it.

Automatic filtering helps with the less useful stuff

Not every file in a repository belongs in an AI context.

A project may contain dependency directories, generated files, build output, binaries, source maps, lock files, and other material that adds little value to a coding conversation.

There is also a more important category: sensitive files.

Environment files, API keys, SSH keys, cloud credentials, and similar secrets should not be casually copied into an AI prompt.

Repoprep automatically filters common examples of both noise and sensitive content before the context is generated.

The selected files can still be reviewed, so you can see what is going into the final context rather than blindly exporting the repository.

The extension shows what you are selecting

The selection screen is useful when deciding how much of a repository to include.

For example, you might start with 91 available files and select the ones relevant to your task. The extension shows the current selection and gives you an idea of the size before extraction.

That makes it easier to trim a context when you realize that some files are unrelated.

It also avoids the usual situation where you paste a large amount of code into an AI chat first and only discover later that the context is much bigger than expected.

The generated context keeps the project structure

Once the selection is ready, Repoprep extracts the files and builds a structured context document.

The paths are preserved, so the output still tells the AI where each file belongs in the project.

A simplified example might look like this:

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

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

===== FILE CONTENTS =====

--- src/components/auth/LoginForm.tsx ---

...file contents...

--- src/lib/auth.ts ---

...file contents...

--- src/middleware.ts ---

...file contents...

That structure is useful when several files depend on one another.

Instead of giving the AI a collection of copied snippets with no clear relationship, the context shows both the project layout and the corresponding source.

The completed context can then be copied directly to the clipboard.

A practical example

Imagine a GitHub repository with around 100 files.

You want to ask:

Why does the user's cart disappear after login?

A reasonable context might include:

LoginForm.tsx
auth.ts
middleware.ts
the cart store
the checkout component
the relevant API route
package.json

There is no obvious reason to include every unrelated page and asset in the project.

With the extension, you can search for the relevant files, select them, check the resulting context size, and extract them together.

The actual question can then focus on the problem rather than on the mechanics of preparing the prompt.

There is no obvious reason to include every unrelated page and asset in the project.

With the extension, you can search for the relevant files, select them, check the resulting context size, and extract them together.

The actual question can then focus on the problem rather than on the mechanics of preparing the prompt.

Private repositories are supported too

The extension is not limited to public repositories.

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

This allows the same workflow to be used with private codebases without turning the extension into a service that stores your repository.

The token is kept locally on the device.

GitHub and GitLab repository data is fetched directly from those services.

What about local projects?

The Chrome extension is most useful when the project is already on GitHub or GitLab.

For a project sitting on your computer, the regular Repoprep web app handles the same general workflow.

You can provide a local folder or ZIP, select the relevant files, and generate the context without uploading the project to a remote processing server.

So the two workflows are fairly straightforward:

GitHub **or **GitLab: use the Chrome extension.

Local project: use the web app.

Model and context size

The amount of code you give an AI assistant matters.

An unnecessarily large context can make a focused question harder to work with, and different models have different context limits.

The extension shows estimated token counts while preparing the files, which gives you a useful indication of how large the final context will be.

The Pro version also includes a model compatibility check for supported models, so you can see whether the current context is likely to fit before sending it.

The estimate is just that — an estimate — but it is useful when working with larger repositories.

GitHub and GitLab are only the starting point

The same idea can be useful in several kinds of development work.

For an unfamiliar open-source project, you can select the source files and documentation related to the part you are trying to understand.

For debugging, you can gather the affected components, utilities, middleware, and configuration into one context.

For refactoring, you can include the code around a component rather than giving the AI the entire repository.

For code review, you can prepare the relevant files without manually copying them one by one.

The workflow stays the same: find the relevant files, review the selection, generate the context, and then work with the AI.

A small change that removes a repetitive step

AI tools have made it much easier to work with unfamiliar codebases.

The awkward part is often everything that happens before the actual question.

You find the repository, figure out which files matter, copy them, remove the irrelevant ones, and repeat the process whenever the context changes.

Working directly from GitHub or GitLab removes much of that manual preparation.

Repoprep's Chrome extension is built around that one idea: use the repository that is already open in your browser, select the part that matters, and turn it into a clean context for the AI tool you are already using.