AI app for it and development · no coding needed
Source-linked codebase understanding console
Reduce time to understand an unfamiliar repository while keeping every answer traceable to source.
Made for: Engineering teams and maintainers onboarding to unfamiliar repositories
What it does for you
The problem
Developers spend hours tracing unfamiliar code, dependencies and architecture across repositories, docs and wikis.
What it gives you
Source-linked explanations, summaries and architecture views
What you give it
Repository codedocumentationwiki contentcommit historypackage metadata
Build your own version of CodebaseChat, ExplainGithub and more
One app with what these 6 AI tools do, yours to keep and change: CodebaseChat, ExplainGithub, Depth AI, GitHub Chat, GitHits beta 0.9, Copilot Chat in GitHub Mobile.
Everything these tools do, in one app
- Natural language querying Ask questions in plain language and get explanations or insights about code.Found in CodebaseChat, ExplainGithub, Depth AI and 2 more
- Code explanations Provides clear explanations of code snippets, functions, or concepts.Found in CodebaseChat, ExplainGithub, GitHub Chat and 1 more
- Multi-language support Works with multiple programming languages across different projects.Found in CodebaseChat, ExplainGithub, Depth AI
- Code summarization Generates summaries that outline the purpose and flow of code blocks or modules.Found in CodebaseChat, ExplainGithub
- Real-time interaction Allows developers to ask follow-up questions and clarify uncertainties as they code.Found in CodebaseChat
- GitHub integration Works directly within GitHub repositories for seamless access.Found in ExplainGithub, GitHub Chat, Copilot Chat in GitHub Mobile
- Code architecture insights Answers complex questions about code architecture and entry points.Found in Depth AI
- Incremental indexing Updates the knowledge base with every commit to reflect the latest code changes.Found in Depth AI
- Code graph generation Creates an AI-generated graph that captures abstractions and relationships in the codebase.Found in Depth AI
- No manual tagging Eliminates the need for manual tagging or summarization of files.Found in Depth AI
- URL modification access Access the chat feature by simply modifying the GitHub URL.Found in GitHub Chat
- Query multiple content types Supports querying across code, documentation, and wiki content within a repository.Found in GitHub Chat
- Version-aware indexing Builds an index of open-source code specific to the version your project uses.Found in GitHits beta 0.9
- Dependency inspection Allows agents to inspect package dependencies, vulnerabilities, changelogs, and upgrade changes.Found in GitHits beta 0.9
- Code examples from real implementations Surfaces code examples from repositories, issues, discussions, and pull requests, linked back to the source.Found in GitHits beta 0.9
- License filtering Filters out repositories with copyleft, unknown, or missing license information.Found in GitHits beta 0.9
- Agent integration Integrates with AI coding agents like Claude Code, Codex, and Cursor via a single init command.Found in GitHits beta 0.9
- Mobile access Provides AI assistance on mobile devices within the GitHub mobile app.Found in Copilot Chat in GitHub Mobile
How it works, step by step
- Answer plain-language questions about a repository
- Explain code snippets, functions and concepts
- Support multiple programming languages across projects
- Summarize the purpose and flow of modules
- Allow follow-up questions during coding
- Work inside GitHub repositories
- Answer architecture and entry-point questions
- Re-index on every commit
- Generate a code graph of abstractions and relationships
- Index without manual tagging
- Open the assistant by modifying the repository URL
- Query code, documentation and wiki content together
- Index open-source code at the version the project uses
- Inspect dependencies, vulnerabilities, changelogs and upgrade changes
- Surface code examples from repositories, issues, discussions and pull requests with source links
- Filter repositories with copyleft, unknown or missing licenses
- Integrate with coding agents through one init command
- Provide mobile access inside the GitHub mobile app
Build it yourself with your AI system
Build this app yourself, no coding needed
Start with a quick version you can try in a few minutes. Like it? Then build the full app by copying and pasting our step-by-step instructions: everything is prepared for you.
Sign in to see how to build it yourself
Build a quick version to try, or get the full app pack for Source-linked codebase understanding console with the step-by-step building instructions. You don't need any technical skills: you copy, paste and answer a few questions. Both are included in the membership.
4 Have it built for you days to a few weeks
Rather not do it yourself, or want it fully tailored to your data, your way of working and your brand? Nexibeo builds Source-linked codebase understanding console with you.
What's in the app pack
Included in the Complete AI Training membership.
- The building instructions your AI follows, step by step
- The questions your AI will ask you about your business before it starts
- A clickable demo you can open in your browser, to see how it should work
- A detailed blueprint of the screens, the information it keeps and the checks it runs
Become a member to get the app packAlready a member? Sign in
The files, for the technically curious
- START-HERE.mdHow to build it with your own AI (read first)3 KB
- README.mdOverview and links4 KB
- questions.mdQuestions to answer before you build3 KB
- prompt-cloudflare.mdThe full build prompt, hosted on Cloudflare26 KB
- prompt-vps.mdThe same build on your own server (Docker)26 KB
- spec.jsonData model, API, AI pipeline, acceptance criteria13 KB
- demo/index.htmlThe working demo on sample data195 KB
Questions
Do I need to know how to code?
No. You copy and paste the prompts on this page into ChatGPT or Claude, and the AI does the building. When it asks you something, you answer in your own words.
What does it cost?
The quick version, the app pack and the step-by-step instructions are for members: you pay the membership price, not a price per app (see the plans). Building the full app uses your own ChatGPT or Claude subscription. Putting it online is often cheap or no cost at the start, and your AI tells you before anything costs money.
How long does it take?
The quick version: about two minutes. The real app: an afternoon for a first version you can use, longer if you want every feature.
Can I change it to fit my business?
Yes. Tell your AI what to change in plain words, like “add a column for the price” or “use our logo and colours”. Or have Nexibeo build and customise it for you.
More detailsHow the AI works, safeguards and what to build first
Reduce time to understand an unfamiliar repository while keeping every answer traceable to source. For engineering teams and maintainers onboarding to unfamiliar repositories, convert repository code, documentation, wiki content, commit history and package metadata into source-linked explanations, summaries and architecture views. The benefit is a testable hypothesis, measured through time to first correct answer and accepted explanations per review hour; do not assume that AI output alone produces business value.
Confirm the buyer's problem and scope, collect repository code, documentation, wiki content, commit history and package metadata, then follow this sequence: 1. Answer plain-language questions about a repository. 2. Explain code snippets, functions and concepts. 3. Summarize the purpose and flow of modules. Resolve uncertain cases with qualified reviewers, approve source-linked explanations, summaries and architecture views, and measure time to first correct answer and accepted explanations per review hour against a documented baseline.
How the AI works
Use AI to interpret permitted inputs, suggest structured mappings and generate candidate outputs for the stated task modules. Use deterministic code for indexing, schema validation, license filtering, dependency parsing and reproducible tests. Review source-linked explanations and uncertainty before accepting results. Indexing scope and license filtering are configurable; final architectural and security judgments remain with the engineering team. A model suggestion is never a verified fact, professional decision or authorization to act.
Safeguards
Preserve source attribution, license compliance, secret redaction and access permissions. Engineering owners approve substantive architectural and security conclusions and repository scope. One repository host, one language family and read-only indexing; final architectural and security judgments remain with the engineering team. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.
What to build first
Pilot scope: One repository host, one language family and read-only indexing; final architectural and security judgments remain with the engineering team. Implement one approved input format, a bounded representative case set and the first three task modules: answer plain-language questions about a repository; explain code snippets, functions and concepts; summarize the purpose and flow of modules. Support the remaining modules with operator review: architecture answers, code graph, dependency inspection, license filtering and agent integration. Include source references, corrections, basic organization access, approval states, export and value measurement. Use managed operator assistance for unresolved exceptions. The cost estimate covers this narrow prototype, not unrestricted multi-tenant scale, complex production integrations, specialist certification or physical operations.
What it can connect to
Customer-owned repositories, documentation and wiki sources. Repository hosts, issue trackers, package registries and coding agents. Start with file exchange and validate destination specifications before promising direct write access. Start with authorized read-only access. Validate current provider access, usage rights and schema behavior before promising a connector.
The screens in detail
Primary screens: Repository connection and index status, Source-linked assistant, Administrator console. Use a repository list with index freshness, a large central conversation and explanation canvas, and a right-hand panel for cited files, graph views and dependency findings. Let users compare explanation versions side by side. Display indexed, stale and failed states. Provide a shareable answer link with citations anchored to the relevant file and commit. Make the task-specific outcome source-linked explanations, summaries and architecture views visible beside its evidence, review state and value baseline.
