Build with AI agents

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Machine-readable docs

• llms.txt: the documentation index. Every model with its connect slug and typed SDK package, plus the core guides. The standard first fetch for an agent.
• llms-full.txt: the entire documentation in one file. Large; prefer the index or a specific page.
• Any page as Markdown: append .md to any docs URL (for example /quickstart.md), or send an Accept: text/markdown header to the normal URL.

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Docs search over MCP

claude mcp add --transport http reactor-docs https://docs.reactor.inc/mcp

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Bootstrap prompt

Build me an app on Reactor (https://docs.reactor.inc).
1. Fetch https://docs.reactor.inc/llms.txt and pick a model from the Models
   section. Each entry lists the model's connect slug and typed SDK package.
2. Scaffold with: npx create-reactor-app my-app --model=<slug>
   (run it without --model to list available models).
3. I'll put my API key in .env as REACTOR_API_KEY. Never put the key in
   client-side code; the scaffolded app already mints short-lived tokens
   server-side via POST https://api.reactor.inc/tokens.
4. Read the model's schema page (linked from llms.txt) before sending
   commands, and take model slugs from the docs, never from memory.

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What agents should know

• Every model has its own command and event schema. The model catalog links each model’s schema, prompt guide, and tutorial.
• Model slugs are not guessable from names: some models are namespaced differently, and some expose one slug per experience. Always read the slug off the model’s own pages.
• create-reactor-app scaffolds a complete app for any model in the catalog, with the SDK installed and a secure token route wired up.