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Contributing to TNH Scholar (Prototype Phase)

TNH Scholar is currently in rapid prototype phase, focusing on core functionality and basic usability. We welcome contributions that help validate and improve the prototype implementation.

We Need Testers and Experimenters

You don't need coding experience to contribute meaningfully! The TNH Scholar project is actively seeking community members to:

  • Explore the software - Try the CLI tools with real dharma talk content and see what works (and what doesn't)
  • Report your experience - Share what you discover: pain points, confusing behavior, missing features, or delightful surprises
  • Experiment with workflows - Test different command pipelines and patterns to process your materials
  • Identify needs - Help us understand what practitioners and scholars actually need from these tools

Your perspective as a practitioner, translator, or researcher using the tools is invaluable during this prototype phase.

Current Focus Areas

  1. TNH-FAB Command Line Tool

    • Basic functionality testing
    • Error case identification
    • Command pipeline testing
    • Pattern system integration

  2. Pattern System

    • Pattern usage testing
    • Pattern creation testing
    • Version control functionality
    • Concurrent access testing

  3. AUDIO-TRANSCRIBE Command Line Tool

    • Basic functionality testing
    • Error case identification

How to Help

Getting Started as a Tester

  1. Install TNH Scholar
pip install tnh-scholar
  1. Try the Quick Start Guide

Follow the Quick Start Guide to get familiar with basic operations

  1. Test with Your Own Materials

Experiment with real dharma talk content using commands like:

# Test basic commands
tnh-fab punctuate input.txt
tnh-fab section input.txt
tnh-fab translate input.txt
tnh-fab process -p pattern_name input.txt

# Test pipeline operations
cat input.txt | tnh-fab punctuate | tnh-fab section

  1. Explore the Pattern System

  2. Create test patterns in ~/.config/tnh-scholar/patterns/

  3. Test pattern loading and application

  4. Try custom workflow combinations

  5. Report What You Find

Share your discoveries via GitHub Issues: - Clear description of the problem or observation - Steps to reproduce (include the command used) - Expected vs actual behavior - Your environment (OS, Python version) - Example files (if helpful)

Code Contributions

At this prototype stage:

  • Start with bug fixes
  • Keep changes focused
  • Include tests for new functionality
  • Follow existing code style
  • See style guide and design principles for coding standards and architectural patterns.

Questions and Discussion

  • Use GitHub Issues for feature discussions
  • Tag issues with 'question' or 'discussion'
  • Focus on prototype phase functionality

This is a project in rapid prototype - we're looking for practical feedback on core functionality as well as possible new feature additions and new tools.