Akira is a modular CLI & GUI tool to help beginners and power users manage command-line aliases, auto-complete suggestions, and plugins. It’s designed to make working with Git, Docker, Linux, Node, Python and more super easy — right from your terminal or with a friendly GUI.
✅ Nested JSON alias groups (gh, dc, os, py, np)
✅ Auto-creates ~/.akira_aliases.json with smart defaults on first run
✅ CLI commands for suggestions & alias resolution
✅ Fyne GUI to view, add, and search aliases interactively
✅ Shell completions for Bash, Zsh, and PowerShell
✅ Modular Go project structure with internal packages
akira/
├── cmd/akira/main.go # CLI entry
├── internal/
│ ├── alias/ # Nested alias loader, saver, resolver
│ ├── filecomplete/ # Suggestion builder
│ ├── plugins/ # Plugin loader
│ ├── discover/ # Path discovery
│ └── gui/ # Fyne GUI
├── scripts/
│ ├── akira_completion.sh
│ ├── akira_completion.zsh
│ └── akira_completion.ps1
├── go.mod
├── go.sum
└── README.md
Linux / Mac:
go build -o akira ./cmd/akiraWindows:
go build -ldflags "-H windows" -o akira.exe ./cmd/akiraInstall globally:
go install ./cmd/akiraakira suggest gh
# gh status -> git status
# gh commit -> git commit
akira run-alias gh status
# Resolved to: git statusakira gui- View all aliases by group
- Add new aliases visually
- Try out live suggestions
Bash:
source scripts/akira_completion.shZsh:
source scripts/akira_completion.zshPowerShell:
. scripts/akira_completion.ps1✅ Now akira [TAB] completes your commands.
| Test Case | Command | Expected Result |
|---|---|---|
| JSON auto-creation | akira suggest |
Creates ~/.akira_aliases.json |
| Suggest nested group | akira suggest gh |
Lists only git commands |
| Run alias resolution | akira run-alias gh status |
Prints Resolved to: git status |
| GUI launches | akira gui |
Opens Fyne window |
| Shell completions work | akira [TAB] |
Shows completions |
- User aliases are stored in:
| OS | File |
|---|---|
| Linux/Mac | ~/.akira_aliases.json |
| Windows | C:\Users\<You>\.akira_aliases.json |
Created with defaults on first run.
PRs and feature suggestions are welcome!
MIT