Build with AI

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, it can read the relevant protocol guidance and contract while helping you implement an integration without repeated manual copy and paste.

A starting prompt

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

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 can use the corpus to draft the integration. You still need to select the appropriate protocol, supply the surrounding project context, configure the application, review the generated code, and verify the flow against the published contract.

If the assistant needs to operate your Sudomimus account or developer settings while it works, give it the Sudomimus 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 — 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 — tool-specific tips for Claude, Cursor, ChatGPT and others, and how to get better integration code out of them.
• Sudomimus CLI for AI agents — the command-line control surface for account checks, local automation, and future developer operations.

Why this works

The documentation covers the choice between Connect, OIDC, and native direct-issue; their protocol flows; the shared identity model; and the three-layer rule model. Giving an assistant the relevant pages lets it work from the same published material as a human integrator. Re-fetch pages when the contract may have changed, and treat generated code as an implementation draft that still needs project-specific review and testing.