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Contributing to AI Agents Reality Check

Thank you for your interest in contributing to AI Agents Reality Check! This project aims to provide rigorous, mathematically sound benchmarking of AI agent architectures.

Ways to Contribute

Bug Reports

  • Use GitHub Issues with the "bug" label
  • Include system information and reproduction steps
  • Attach benchmark output logs when relevant

Feature Requests

  • Use GitHub Issues with the "enhancement" label
  • Explain the use case and expected behavior
  • Consider mathematical rigor and architectural focus

Research Contributions

  • New agent architectures for benchmarking
  • Additional statistical analysis methods
  • Academic validation studies
  • Performance optimization improvements

Development Setup

git clone https://github.com/Cre4T3Tiv3/ai-agents-reality-check
cd ai-agents-reality-check
make install-dev

Code Standards

Python Code Quality

  • Use black for formatting: make format
  • Type hints required for all functions
  • Docstrings for all public methods
  • 90%+ test coverage for new features

Mathematical Rigor

  • Statistical methods must be academically sound
  • Include confidence intervals for performance metrics
  • Document assumptions and limitations
  • Provide references for statistical techniques

Testing Requirements

  • Unit tests for all new functionality
  • Integration tests for benchmark pipelines
  • Schema validation for data structures
  • Performance regression tests

Pull Request Process

  1. Fork and Branch: Create feature branch from main
  2. Implement: Follow code standards and testing requirements
  3. Test: Run make check and ensure all tests pass
  4. Document: Update relevant documentation
  5. Submit: Create PR with clear description and context

Review Criteria

  • Mathematical accuracy and statistical validity
  • Code quality and test coverage
  • Documentation completeness
  • Backward compatibility
  • Performance impact assessment

Community Guidelines

  • Be respectful and constructive
  • Focus on technical merit and empirical evidence
  • Welcome newcomers and provide helpful feedback
  • Maintain the ethos: challenge assumptions with data

Questions?

  • GitHub Discussions for general questions
  • GitHub Issues for specific bugs or features
  • Email ByteStack Labs for research collaborations