DEV Community: Rajiv Naskar

Preparing GitHub Repositories for AI Coding Assistants

Rajiv Naskar — Sat, 22 Aug 2026 12:37:32 +0000

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.

Links

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

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

Open-source core:
https://github.com/Kishoren1/repoprep-app

Repoprep: A Simple Way to Prepare Project Context for Claude, ChatGPT, and Other AI Tools

Rajiv Naskar — Sat, 14 Mar 2026 09:09:09 +0000

AI coding assistants are much more useful when they can see the relevant parts of a project instead of just a single file or a few pasted snippets.

The problem is getting that context into the AI in the first place.

For a small project, it is easy enough to copy a few files into Claude or ChatGPT. With a larger project, things become messy pretty quickly. There may be hundreds of files, dependency directories, build output, generated files, binaries, lock files, and other content that an AI model does not really need to see.

Manually sorting through all of that takes time, and it is easy to leave out something important.

Repoprep is a browser-based tool designed to simplify that part of the workflow. It takes a project folder, ZIP archive, or collection of files and turns the useful content into a single structured context file that can be given to an AI tool.

What is Repoprep?

Repoprep packages a project into a format that is easier to provide to AI coding assistants.

Instead of doing this:

Open file → copy → paste → open another file → copy → paste → repeat

the workflow becomes:

Select a project → review the files → generate the context → paste or upload

The generated output contains the project structure first, followed by the contents of the selected files with their paths clearly labelled.

This makes it easier for an AI model to understand how the files relate to one another.

The output is plain text, so it can be used with Claude, ChatGPT, Gemini, Cursor, GitHub Copilot, Mistral, or other tools that accept text or file input.

What happens to the project files?

One of the main design choices in Repoprep is that local files are processed in the browser.

A project folder or ZIP does not need to be uploaded to a Repoprep server for processing. The browser reads the files, filters them, extracts their contents, and creates the final context document locally.

That makes the tool useful for developers who do not want to upload a private codebase to another service just to prepare an AI prompt.

Repoprep does not need an account for the free workflow.

For Pro licence verification, an email address and licence information are used for the purchase verification process, but the project files themselves are processed locally.

What gets included and excluded?

A project contains a lot more than the source code you actually want an AI model to see.

For example, a typical JavaScript or TypeScript project may contain:

node_modules
.git
build output
.next
dist
build
cache directories
lock files
source maps
images and other binary files

Including all of that would make the context unnecessarily large.

Repoprep automatically filters common sources of noise while keeping the files that are more likely to be useful for development work. Sensitive files such as .env are also blocked automatically.

The exact files can still be reviewed before generating the final output, so the result does not have to be an all-or-nothing export.

The generated context file

The output is intentionally simple.

A typical context file looks roughly like this:

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

my-project/
├── src/
│ ├── App.tsx
│ └── index.ts
├── package.json
└── README.md

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

--- src/App.tsx ---

...file contents...

--- src/index.ts ---

...file contents...

--- package.json ---

...file contents...

The directory tree gives the AI a quick overview of the project, while the labelled file sections provide the actual source content.

This format also makes the resulting context readable to a person. You can open the generated file and see exactly what is being passed to the AI.

Token size matters

A context file is only useful if it fits within the limits of the AI model being used.

Repoprep includes a token count estimator in the Pro version so larger projects can be checked before the generated context is pasted into an AI conversation.

The count is an estimate rather than a model-specific guarantee. Tokenization differs between models, so it is better used as a practical guide when deciding how much of a project to include.

For smaller projects, you may not need this at all. For larger repositories, it becomes much more useful.

More than source code

Repoprep is not limited to JavaScript or TypeScript projects.

It supports a wide range of code and text formats, including common files used with Python, Go, Rust, Java, C/C++, SQL, HTML, CSS, JSON, YAML, Markdown, shell scripts, and others.

It can also extract text from formats such as PDF, DOCX, and XLSX.

Binary files such as images are skipped rather than being treated as source text.

This makes the workflow useful outside normal software repositories as well. A project folder can contain documentation, configuration, specifications, and other text-based material that may be relevant to an AI-assisted task.

GitHub and GitLab workflows

There is also a Chrome extension for working directly from repository pages.

Instead of downloading a repository first, the extension can be used from GitHub or GitLab to select files and generate an AI-ready context from the repository.

This is particularly useful when working with an unfamiliar codebase or when you want to inspect a repository with an AI assistant without manually copying files one at a time.

For local projects, the web app can still be used directly with a folder or ZIP archive.

Who is it useful for?

Repoprep is mainly aimed at developers who regularly use AI for software development.

Some common situations include:

Debugging

Provide the relevant source files and configuration together instead of pasting them individually.

Refactoring

Give the AI enough surrounding code to understand how a component, module, or feature fits into the rest of the project.

Working with an unfamiliar repository

Generate a structured overview of the codebase before asking questions about how it works.

Code reviews

Package the relevant files into a single context that can be reviewed by an AI assistant.

Project handoffs

Create a portable snapshot of the files relevant to a particular AI session without manually assembling the context each time.

The important point is that Repoprep does not replace the AI assistant. It handles the preparation step before the actual AI conversation begins.

Free and Pro versions

The free version is intended for smaller tasks and supports up to 25 files and 2 MB per session.

The Pro version is a one-time $6 purchase, rather than a recurring subscription. It increases the limit to 200 files and 20 MB and adds the token count estimator.

The product currently follows a simple model: use the free version for smaller projects and upgrade when the larger project limits become useful.

A simpler way to get project context into AI

AI-assisted development has made it much easier to ask questions about a codebase, but the quality of the answer still depends on the context provided to the model.

For small tasks, copying a couple of files is fine.

For larger tasks, preparing that context manually can become a repetitive part of the workflow.

Repoprep is built to handle that preparation step: take the relevant project files, remove common noise, preserve the project structure, and produce one readable context file that can be used with the AI tool of your choice.

There is no installation required for the web app, and local projects can be processed directly in the browser.

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