PiGa (Task Keeper)

PiGa (Task Keeper)

Keyboard-centric list editor and task manager backed by a self-hosted server

90stars
1forks
Last commit: 15d ago
Repo age: 8y old

PiGa (Task Keeper) is a keyboard-centric list editor and lightweight task manager designed for power users. It provides a web-based, self-hosted server backend with a focus on fast editing, nested lists, and productivity shortcuts.

Key Features

  • Keyboard-first editor with numerous productivity shortcuts for editing, navigation, formatting and task manipulation
  • Nested/collapsible tasks allowing hierarchical lists and the ability to collapse children under a parent task
  • Per-task tagging and simple metadata to organize items
  • Multiple documents with quick switching between lists
  • Copying/exporting of tasks (including as Markdown) and clipboard-oriented power-user actions
  • Server backend implemented in Scala/Play with a relational database (configured for MariaDB/MySQL via Slick)
  • Docker and docker-compose support plus command-line helpers for DB initialization and admin user creation

Use Cases

  • Maintain fast, hierarchical TODO lists and project checklists for power users who prefer keyboard workflows
  • Capture structured meeting notes or action items with tags and collapsible sections
  • Manage recurring lists such as meal plans, shopping lists, or inventories with quick navigation and edits

Limitations and Considerations

  • Requires a Java 11 runtime and a relational database (MariaDB/MySQL); initial setup requires DB configuration and table creation
  • No official mobile app or built-in multi-device sync beyond the hosted server; experience is primarily web/keyboard-focused
  • Defaults include a created admin account at setup that should have its password changed immediately

PiGa is suitable for users who want a fast, keyboard-driven list editor with server-backed storage and control over deployment. It favors power-user workflows over mobile-first interfaces and integrates into standard self-hosted stacks via Docker and a SQL database.

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