Over the past year, I've spent a lot of time working with large language models to understand, document, and analyze codebases. One problem kept appearing: modern repositories are simply too large and noisy for AI tools to consume effectively.
Repositories contain thousands of files, generated artifacts, dependencies, and sometimes even secrets that should never be shared with external systems. Preparing a repository for AI assistance often becomes a manual and repetitive task.
So I built RepoLens.
RepoLens is a CLI tool that analyzes repositories, scrubs secrets, optimizes token usage, and packages projects into AI-ready context packs for tools like ChatGPT, Claude, and Gemini.
What RepoLens Does
RepoLens provides a simple workflow:
Analyze a repository.
Remove unnecessary files and directories.
Detect and scrub secrets.
Optimize the content for token efficiency.
Export an AI-ready context pack.
Key capabilities include:
Repository analysis
Secret detection and redaction
Token optimization
AI-ready context packaging
Support for multiple programming languages
Command-line interface for automation and scripting
Why I Built It
I am a mathematics lecturer and I build software products independently in my spare time.
As I worked with AI systems, I found myself repeatedly preparing repositories before sharing them with LLMs. I wanted a tool that could automate this process and make AI-assisted development more efficient.
RepoLens started as a small utility for my own workflow but gradually evolved into a standalone product.
Building with Limited Resources
RepoLens was built while working full-time and with limited resources. Building products independently forces you to move quickly, learn continuously, and focus on solving real problems.
One thing I have learned is that useful technology can be built from anywhere. You do not need a large team or significant funding to start building products that people around the world can use.
What's Next?
RepoLens is still evolving. Some areas I am actively exploring include:
Incremental context packs
Additional language support
Better token optimization strategies
Improved repository visualization
Integrations with AI development workflows
I'd Love Your Feedback
If you regularly work with AI coding assistants, I'd love to hear:
How do you prepare repositories for AI tools?
What would make a context-packaging tool more useful?
Which AI workflows should RepoLens support next?
RepoLens is part of my broader effort at DestLabs to build useful software products for developers, creators, and businesses.
Thank you for reading, and I look forward to your feedback!
GitHub: [https://github.com/DestLabs/RepoLens/releases]
Website: [https://destlabs.netlify.app/repolens]
Top comments (4)
AI-ready context packs are a strong idea if they stay inspectable. The risk is turning a repo into a giant summary that nobody can audit. I like packs that include source pointers and exclusions, so the agent and the human both know what context was used.
Thank you for the comment. I strongly agree that context pack outputs should not become black-box summaries that replace the underlying source code.
RepoLens builds context packs as .pack and JSON files that can be audited and inspected. It preserves file boundaries, allows users to export either full code or lightweight summaries (--signatures), and supports excluding files and directories (--exclude).
I particularly like your suggestion about source pointers and manifests. Making context packs reproducible, inspectable, and easy to verify for both humans and AI agents is an important part of the project's direction.
That direction sounds right. Preserving file boundaries and letting users choose full code versus signatures is a useful split. The manifest/source-pointer layer is what turns a context pack from a blob into something a human can audit and reproduce.
You're suggesting a metadata/manifest layer, and I think that's one of the best feature ideas I've received for RepoLens so far. It aligns perfectly with the goal of making AI context packs transparent, reproducible, and easy to audit. I'll definitely include it in the next iteration of RepoLens. Thank you for the thoughtful suggestion!