Creating Plugins For LLMs

ChatGPT plugins are conversational experiences that extend ChatGPT through tools and custom UI views. They're built as MCP servers invoked during conversations.

⚠️ The plugin is consumed by two users at once: the human and the ChatGPT 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 plugin's requirements and design decisions. Keep it up to date as you work on the plugin.

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

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.

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 connection
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