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Framework TUI Bubble Tea en Go par Charmbracelet

Bubble Tea est un framework Go puissant pour créer des interfaces utilisateur dans le terminal, basé sur l'architecture Elm. Développé par Charmbracelet, il prend en charge les applications TUI en ligne, plein écran et mixtes avec un rendu haute performance basé sur les cellules et des vues déclaratives.

Qu'est-ce que Framework TUI Bubble Tea en Go par Charmbracelet ?

Bubble Tea est un framework Go pour construire des interfaces utilisateur en mode terminal (TUI), inspiré de l'architecture Elm. Il est développé par Charmbracelet et permet de créer des applications interactives en ligne, plein écran ou mixtes. Le framework intègre un moteur de rendu orienté cellule offrant de hautes performances, ainsi que des vues déclaratives pour une gestion simplifiée de l'état de l'interface. Bubble Tea est utilisé par les développeurs Go pour des outils CLI, des jeux textuels, des éditeurs dans le terminal, et bien plus encore. Il fonctionne sur tous les principaux systèmes d'exploitation (Linux, macOS, Windows) et peut être intégré avec d'autres bibliothèques Charm comme Bubbles pour des composants réutilisables. L'installation se fait via `go get` et la documentation inclut des exemples pratiques, notamment des GIFs animés montrant les capacités du framework.

Compatible avec~Claude Code~Codex CLI~Cursor
npx skills add https://github.com/agentskillexchange/skills/tree/main/skills/bubble-tea-go-tui-framework
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Framework TUI Bubble Tea en Go par Charmbracelet preview
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Documentation

Bubble Tea Framework TUI en Go par Charmbracelet

Bubble Tea est un framework Go puissant pour construire des interfaces utilisateur dans le terminal, basé sur l'architecture Elm. Développé par Charmbracelet, il prend en charge les applications TUI en ligne, plein écran et mixtes avec un rendu haute performance orienté cellule et des vues déclaratives.

Installation

Notes d'utilisation de base ou de démarrage :

Source

Individual skills in this repo

This repo contains 20 individual skills — each has its own dedicated page.

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Add graph-backed memory and context retrieval to agent workflows

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