How to generate a documentation site from a GitHub repo

The reason most codebases have no documentation isn't that the team doesn't value docs. It's that documentation starts from a blank page, and the blank page always loses to the next ticket. I've watched good engineers add an empty docs/ folder to a repo with real intent, and I've watched that folder still be empty a year later. The work was never the writing of any single page. It was the cold start — the staring at nothing, deciding what the first page even is.

So here's the inversion that actually breaks the deadlock: instead of starting from nothing, start from what you already have. Your code, your README, your config files, your folder structure — that's a draft of your documentation already, just in a form nobody can read. This guide is about pointing an AI at a GitHub repository, having it read the real code, and getting back a complete documentation site you can edit. Not a finished product. A first draft that exists, which beats a perfect draft that doesn't.

What "generate documentation from a repo" actually means

The phrase gets used for two very different things, and the difference matters before you pick a tool.

The first kind is API reference extraction — JSDoc, Doxygen, Sphinx autodoc, TypeDoc. These parse your code's signatures and comments and emit a reference. They're precise and they're genuinely useful, but they only document what you already annotated, and they produce reference material, not a docs site a new user can learn from. If your functions have no doc comments, you get an empty reference.

The second kind — the one this guide is about — is AI reading your codebase the way a new engineer would and writing prose documentation from it: what the project does, how to get started, how the major pieces fit together, what the key modules are for. It doesn't need pre-existing doc comments, because it's inferring intent from the code itself the way a human onboarding to the repo would. That's a different output and a different audience: humans landing on your project for the first time, plus the AI agents that now read docs more than humans do.

Both have a place. The rest of this guide is about the second, because it's the one that solves the blank-page problem.

What AI generation does well — and where it needs you

I want to be honest about this part, because the failure mode of every "AI writes your docs" pitch is implying the output is finished. It isn't, and selling it as finished is how you end up with the thing every developer rightly dreads: confident, plausible, subtly wrong documentation that's worse than none.

Here's the real division of labor.

AI is genuinely good at:

• The scaffold. Page structure, sensible categories, a getting-started page, a tour of the major modules. This is 80% mechanical and 100% of what stops you from starting.
• The boring-but-necessary pages. Installation, project layout, "what is this folder," configuration options. Pages that are tedious to write and easy to infer from the repo.
• A faithful summary of what the code does. When it's grounded in the actual files — not guessing from the project name — it describes real behavior.

AI cannot do, and you shouldn't expect it to:

• Know your intent. Why a thing exists, what you decided not to build, the gotcha that isn't in the code. That's in your head.
• Get the edge cases right. The auth flow that has a weird exception, the endpoint that's deprecated but still live. A model reading the code can't always tell.
• Replace review. Generated docs need a human read-through before they're published, the same way generated code does.

The right mental model is a fast intern who read your entire codebase overnight and left you a complete first draft on every page. You'd never ship their draft unread. You'd also never complain that it's a worse starting point than the blank page you had yesterday. Generation is for the cold start, not the finish line — and a tool that pretends otherwise is the one to distrust.

How to do it: from repo URL to live site

The mechanical version is short, because the point of the feature is that the hard part is automated. With Doccupine Import, the flow is:

1. Paste your GitHub repository URL. Public or private — private repos connect through GitHub OAuth so the importer can read them with your own access.
2. Choose where the docs repo lives. A Doccupine-managed account (nothing to set up) or your own GitHub account, if you want the generated docs in a repo you control from day one.
3. Watch it work. The importer runs a visible pipeline: it validates access, crawls the repository, has the AI plan a documentation outline, writes each page grounded in the relevant source files, and publishes the result. You see it move through the stages — files crawled, pages planned, "writing page 5 of 14" — rather than staring at a spinner.
4. Get a complete, deployed docs site. Not a zip of Markdown — a live documentation site with an auto-organized sidebar, categories the AI inferred from your code, full-text search, and the whole AI layer (RAG chat, llms.txt, and an MCP server) already on it.

Here's the whole flow end to end:

Under the hood it reads the files that actually carry meaning — source code across the common languages, READMEs, config — and skips the noise (node_modules, lock files, build output). It plans the structure first, then writes each page against the real files it's documenting, so the content is anchored to your code rather than invented around your repo's name.

The part that makes this safe: it's editable MDX, not a black box

This is the design decision that separates a useful generator from a gimmick, and it's the one I'd interrogate hardest if I were evaluating any tool in this category.

What comes out is standard MDX — the same plain, portable source format you'd get if you'd written the docs by hand, with frontmatter on every page and access to rich components (callouts, steps, tabs, cards). It is not a proprietary blob you can only view inside the product. That matters for three reasons:

• You can fix what the AI got wrong. Found a page that's confidently incorrect? It's a Markdown file. Edit it.
• You own the output. The generation is a starting point, not a lock-in. The same files run through the open-source CLI and self-host anywhere, exactly like docs you wrote yourself.
• It keeps improving as docs, not as one-time output. Once the draft exists, it enters a normal docs-as-code workflow, where doc changes ride along in the pull requests that change behavior — which is the only way documentation ever stays true.

A generator whose output you can't edit isn't generating documentation. It's renting you a view of your own codebase. The test for any tool here is simple: after it generates, can you open the files and change a sentence? If not, walk away.

When to reach for this — and when not to

Generation isn't always the right move, and saying so is the difference between a tool and a sales pitch.

Reach for it when:

• You have a real codebase and no docs, and the blank page is the only thing stopping you.
• You're onboarding a new project and want a structural starting point in minutes instead of an afternoon.
• You have a README that outgrew itself and want it expanded into an actual site.

Don't lead with it when:

• You already have good, hand-written docs. Then you want a generator that turns your existing Markdown into a site, not one that rewrites your work.
• Your docs are mostly conceptual — architecture decisions, "why," product narrative — that lives in your head and not in the code. The AI can't read what was never written down.
• You're documenting an API and need a precise, exhaustive reference. Generation gets you the surrounding guides fast, but the reference pages still want the deliberate structure a human gives them.

The honest framing: generation is the best answer to "I have no docs and can't get started," and a poor answer to "I have docs and want them better." Different problems, different tools.

Why generated docs are worth doing even if they're imperfect

The objection I hear most is "I don't want AI-written docs on my project." It's a fair instinct, and it's aimed at the wrong target. The choice in front of most teams isn't AI docs vs. great hand-written docs. It's AI docs vs. the empty docs/ folder you've had for a year. Compared to nothing — which is the actual status quo for most repositories — a reviewed first draft is a strict upgrade.

And the stakes went up, because docs aren't only read by people anymore. When a developer points Claude Code or Cursor at your project, the agent reads whatever documentation exists — and if none exists, it guesses your API from training data and ships the guess. An undocumented repo isn't neutral; it's actively feeding wrong answers to the tools building on it. A generated, human-reviewed docs site, exposed over an MCP server agents can query, turns that from a liability into an asset — in an afternoon instead of a quarter.

The short version

You don't have to choose between perfect documentation and no documentation. Point the importer at your GitHub repo, let the AI read your actual code and write the first draft, then do the part only you can do — correct it, add the intent the code doesn't carry, and ship it. The cold start is the expensive part, and it's the part you can now skip.

If you run an import and the output gets something about your project wrong in an interesting way, I'd genuinely like to see it — email [email protected]. Watching where the AI's read of a codebase diverges from the author's intent is some of the most useful signal we get.