Creating Plugins For LLMs

Those are conversational experiences that extend AI assistants through tools and custom UI views. 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 view, the human interacts with it, the LLM sees its state. Internalize this before writing code: the view 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? → Read discover.md first. Nothing else until SPEC.md exists.

SPEC.md exists? → Read SPEC.md, then follow architecture.md to design the change. Update SPEC.md, then read the relevant Implementation references below before writing code.

Migrating from Skybridge < 0.36.x? → Read migrate-to-v1.md first. Users may reference skybridge >= 0.36.x as v1.

Migrating from Skybridge 1.x to 2.x? → Fetch the v2.0.0 release notes first and follow them.

Building an ecommerce app? → Read ecommerce.md first.

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, use devtools to render views or connect to ChatGPT/Claude
3. Evals → evals.md: when checking that a real model reaches the right tools from natural prompts, in a test

Architecture

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

Implementation