Translation extension for Pi coding agent. Toggle translation mode and get three variants — Formal, Natural, and Informal — for any text you type.

Install

pi install npm:pi-lingo

Or add to your project's .pi/settings.json:

{
  "packages": ["npm:pi-lingo"]
}

What You Get

Commands

| Command | Description | |---------|-------------| | /lingo | Toggle translation mode on/off | | /lingo on | Enable translation mode | | /lingo off | Disable translation mode | | /lingo settings | Open language settings | | /lingo-settings | Configure target language (BCP 47) |

Features

• Toggle mode — Turn translation on/off mid-session without restarting Pi
• Three variants — Every translation returns Formal, Natural, and Informal versions
• BCP 47 language tags — Supports en, en-GB, es, zh-Hans, pt-BR, and any valid IETF language tag
• Persistent config — Target language and mode state survive across sessions
• Status indicator — Shows lingo:en (or your language) in the footer when active

Usage

Enable translation mode:

/lingo on

Type any text and Pi translates it automatically:

> Hola, ¿cómo estás?

*Formal*

Hello, how are you?

*Natural*

Hi, how's it going?

*Informal*

Hey, what's up?

Disable when you want normal Pi behavior:

/lingo off

Configure Language

/lingo-settings

Enter any valid BCP 47 tag, for example:

• en — English (generic)
• en-GB — British English
• es — Spanish
• de — German
• zh-Hans — Simplified Chinese
• fr-CA — Canadian French

Invalid tags are rejected with an error message.

Requirements

• Pi coding agent v0.37+
• An LLM provider configured in Pi

How It Works

When translation mode is active, the extension intercepts your input via Pi's input event and transforms it into a structured translation prompt. The LLM responds with three variants, which the extension reformats into clean markdown with italic headers.

The extension does not add tools to the LLM context — everything happens transparently through prompt transformation, so there is no extra token overhead when the mode is off.

License

MIT