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Contributing to PromptRefiner

Thank you for your interest in contributing! This guide will help you get started.

Table of Contents

Code of Conduct

This project follows the Contributor Covenant Code of Conduct. By participating, you are expected to uphold this code. Report unacceptable behavior via GitHub Issues.

How Can I Contribute?

Good First Issues

Look for issues labeled good first issue or help wanted. These are specifically curated for new contributors.

Areas Where Help Is Needed

  • New Model Support — Adding tokenizers and pricing for new LLM providers (Google Gemini, Mistral, etc.)
  • UI/UX Improvements — Better prompt diff views, syntax highlighting, dark mode
  • Prompt Refinement Strategies — New refinement templates and techniques
  • Testing — Expanding test coverage for edge cases
  • Documentation — Improving guides, adding examples, translating docs
  • Cost Data — Keeping model pricing up to date

Development Setup

See localsetup.md for complete environment setup instructions.

Quick version:

# Backend
cd backend
python -m venv venv
source venv/bin/activate
pip install -r requirements.txt
pip install -r requirements-dev.txt  # includes pytest, black, ruff
cp .env.example .env

# Frontend
cd frontend
npm install

Pull Request Process

  1. Fork the repository and create a branch from main:

    git checkout -b feature/your-feature-name

  2. Make your changes following the coding standards.

  3. Write tests for any new functionality:

    # Backend
cd backend && pytest

# Frontend
cd frontend && npm test

  4. Lint your code:

    # Backend
ruff check .
black --check .

# Frontend
npm run lint

  5. Commit with a clear message following Conventional Commits:

    feat: add Gemini model token counting
fix: correct GPT-4o pricing calculation
docs: update API endpoint documentation

  6. Push and open a Pull Request against main.

  7. Describe your changes in the PR body:

    • What does this PR do?
    • How was it tested?
    • Any breaking changes?

  8. A maintainer will review your PR. Address any feedback, and it will be merged once approved.

Coding Standards

Python (Backend)

  • Formatter: Black (line length 100)
  • Linter: Ruff
  • Type Hints: Required for all function signatures
  • Docstrings: Google style for public functions
  • Testing: pytest with at least 80% coverage for new code
# Good
def count_tokens(text: str, model: str = "gpt-4o") -> int:
    """Count the number of tokens in text for a given model."""
    ...

# Bad
def count_tokens(text, model="gpt-4o"):
    ...

JavaScript/React (Frontend)

  • Formatter: Prettier
  • Linter: ESLint
  • Components: Functional components with hooks
  • Naming: PascalCase for components, camelCase for functions/variables

General

  • No commented-out code in PRs
  • No console.log or print statements in production code
  • Environment variables for all secrets — never hardcode API keys

Reporting Bugs

Open a GitHub Issue with:

  1. Summary — One-line description
  2. Steps to Reproduce — Numbered steps to trigger the bug
  3. Expected Behavior — What should happen
  4. Actual Behavior — What actually happens
  5. Environment — OS, Python version, Node version, browser
  6. Screenshots/Logs — If applicable

Suggesting Features

Open a GitHub Issue with the enhancement label:

  1. Problem Statement — What problem does this solve?
  2. Proposed Solution — How should it work?
  3. Alternatives Considered — Other approaches you thought of
  4. Additional Context — Mockups, references, examples

Questions?

Open a Discussion on GitHub or comment on a relevant Issue. We're happy to help!