Guide developers through creating and updating ChatGPT and MCP apps.
Covers the full lifecycle: brainstorming ideas against UX guidelines, bootstrapping projects, implementing tools/views, debugging, running dev servers, deploying and connecting apps to ChatGPT.
Use when a user wants to create or update a ChatGPT app, MCP app, MCP server or use the Skybridge framework.
Guide developers through creating and updating ChatGPT and MCP apps.
Covers the full lifecycle: brainstorming ideas against UX guidelines, bootstrapping projects, implementing tools/views, debugging, running dev servers, deploying and connecting apps to ChatGPT.
Use when a user wants to create or update a ChatGPT app, MCP app, MCP server or use the Skybridge framework.
Creating Apps 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.
Setup
Copy template → copy-template.md: when starting a new project with ready SPEC.md
Run locally → run-locally.md: when ready to test, need dev server, use devtools to render views or connect to ChatGPT/Claude