---
title: Build with AI
description: Hand the entire Sudomimus documentation to your AI coding assistant
  and let it write the integration for your stack.
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---

Sudomimus's documentation is published in a **machine-readable form** alongside the human-facing pages. If your editor or chat tool can fetch a URL, you can hand the whole docs corpus to your AI assistant in one shot and let it write the integration code for your stack — in one message, not a copy-paste marathon.

## The one-shot prompt

Paste this into Claude, Cursor, ChatGPT, or any AI tool with web access, filling in the angle-bracketed parts:

```text
Read https://docs.sudomimus.com/llms-full.txt and integrate Sudomimus
authentication into my application.

Stack: <your stack, e.g. Next.js 14 + Drizzle + Postgres>
Auth methods: <e.g. passkeys + email OTP>
Application anchor: <your-application>
Callback URL: <your callback URL>
```

The assistant reads the full corpus and writes the integration. The docs describe the protocol; you supply the surrounding context — your framework, your auth methods, your anchor.

If the assistant needs to operate your Sudomimus account or developer settings while it works, give it the [Sudomimus CLI](/en-us/ai/cli/) instead of browser instructions. The CLI exposes login, session, and account commands in a form an agent can call directly.

## Where to go next

- **[What to feed your AI](/en-us/ai/endpoints/)** — the machine-readable endpoints (`llms.txt`, the abridged and full corpora, per-page Markdown) and the OpenAPI reference, with guidance on which to use when.
- **[Driving your AI assistant](/en-us/ai/assistants/)** — tool-specific tips for Claude, Cursor, ChatGPT and others, and how to get better integration code out of them.
- **[Sudomimus CLI for AI agents](/en-us/ai/cli/)** — the command-line control surface for account checks, local automation, and future developer operations.

## Why this works

Everything an integrator needs is in the docs: the choice between Connect, OIDC, and native direct-issue; their protocol flows; the shared identity model; and the three-layer rule model. An assistant with the corpus in context has the same material a human integrator reads — so it can both write the code and answer follow-up questions without re-fetching.