AI Integration

Signals provides a dedicated endpoint for Large Language Models (LLMs) to consume the documentation. This is useful for adding the entire documentation set as context for your AI coding assistant.

Endpoint

https://dartsignals.dev/llms.txt

This endpoint returns all documentation pages concatenated into a single Markdown file.

Usage

Antigravity

You can ask Antigravity to read the documentation by providing the URL in your prompt:

Read https://dartsignals.dev/llms.txt and explain how to use computed signals.

Gemini CLI

You can use the Gemini CLI to query the documentation. Create a GEMINI.md file in the root of your project with the following content:

https://dartsignals.dev/llms.txt

Then you can query the CLI:

gemini "How do I use signals?"

Firebase Studio

In Firebase Studio, you can download the file as context and reference it locally by adding the file as context to the chat:

curl https://dartsignals.dev/llms.txt > signals.md

VS Code

If you are using GitHub Copilot or other AI extensions in VS Code:

1. Download the llms.txt file:

   curl https://dartsignals.dev/llms.txt > signals.md

2. Open the file in VS Code.
3. Reference it in your chat (e.g., @signals.md or by having it open).

Zed

In the Zed editor, you can add the documentation to the assistant’s context:

1. Open the Assistant panel (Cmd-?).
2. Type /file and select the downloaded signals.md (or paste the content).
3. Ask your question.

Claude Code

For Claude Code, you can add the documentation to your context:

claude --context https://dartsignals.dev/llms.txt

Codex

For tools powered by OpenAI Codex, you can provide the documentation as context by pasting the content or referencing the file if the tool supports it.