Creating Apps For LLMs

Those are conversational experiences that extend AI assistants through tools and custom UI widgets. They’re built as MCP servers invoked during conversations.

⚠️ The app is consumed by two users at once: the human and the AI Assistant LLM. They collaborate through the widget—the human interacts with it, the LLM sees its state. Internalize this before writing code: the widget is your shared surface.

SPEC.md keeps track of the app’s requirements and design decisions. Keep it up to date as you work on the app.

No SPEC.md? Stop. → Read discover.md first. Nothing else until SPEC.md exists.

Setup

1. Copy template → copy-template.md: when starting a new project with ready SPEC.md
2. Run locally → run-locally.md: when ready to test, need dev server or ChatGPT/Claude connection

Architecture

Design or evolve UX flows and API shape → architecture.md

Implementation

• Fetch and render data → fetch-and-render-data.md: when implementing server handlers and widget data fetching
• State and context → state-and-context.md: when persisting widget UI state and updating LLM context
• Prompt LLM → prompt-llm.md: when widget needs to trigger LLM response
• UI guidelines → ui-guidelines.md: display modes, layout constraints, theme, device, and locale
• External links → open-external-links.md: when redirecting to external URLs or setting “open in app” target
• OAuth → oauth.md: when tools need user authentication to access user-specific data
• CSP → csp.md: when declaring allowed domains for fetch, assets, redirects, or iframes

Deploy

• Ship to production → deploy.md: when ready to deploy via Alpic
• Publish to ChatGPT/Claude Directories → publish.md: when ready to submit for review

Full API docs: https://docs.skybridge.tech/api-reference.md