Streamline Your Data Science Open Source Contributions

Stop struggling with inconsistent contribution workflows and unclear project standards. These Cursor Rules transform chaotic open-source data science projects into well-oiled collaboration machines where contributors know exactly what's expected and maintainers can focus on innovation instead of process management.

The Real Problem: Contribution Chaos Kills Projects

Every data science maintainer knows this pain: promising contributors disappear after their first confusing interaction, PRs sit in limbo because requirements weren't clear, and your codebase becomes a patchwork of different styles and quality levels. Meanwhile, you're spending more time managing contributions than advancing your actual research.

The typical open-source data science project suffers from:

• Intimidating barriers to entry that scare away valuable contributors
• Inconsistent code quality that creates technical debt and maintenance nightmares
• Poor documentation workflows that leave features undocumented and examples broken
• Manual review bottlenecks where maintainers become the limiting factor
• Unclear expectations that lead to wasted effort and frustrated contributors

Solution: Production-Grade Contribution Standards

These rules establish a complete contribution framework that automates quality control while making participation welcoming and efficient. You get professional-grade governance without the overhead.

What makes this different: Instead of generic "please follow our guidelines" documentation, you get specific, executable standards with automated enforcement. Contributors know exactly what success looks like, and your CI pipeline catches issues before they reach human reviewers.

Key Benefits That Transform Your Project

Cut Review Time by 70%

Automated checks catch formatting, type errors, and missing tests before PRs reach maintainers. Your review time shifts from catching basic issues to providing strategic feedback on implementation approaches.

Attract Senior Contributors

Professional standards signal project maturity. Experienced developers recognize well-run projects and are more likely to invest significant time in contributions.

Eliminate Code Quality Drift

Strict type checking, automated formatting, and comprehensive testing requirements maintain consistency as your contributor base grows.

Scale Documentation Effortlessly

Auto-generated API docs from docstrings and notebook-based tutorials that execute in CI mean your documentation stays current without manual maintenance.

Real Developer Workflows: Before & After

Bug Reports That Actually Get Fixed

Before: "The model doesn't work" with no context, environment details, or reproduction steps. You spend hours trying to understand the issue.

After: Structured issue templates require environment details, reproduction scripts, and expected vs. actual behavior. Contributors include self-assessment of their debugging attempts. Issues become actionable immediately.

Pull Requests That Ship

Before: Contributors submit code that breaks tests, lacks documentation, and doesn't follow project conventions. Multiple review cycles drain everyone's energy.

After: Pre-commit hooks catch formatting and basic errors locally. CI validates type hints, runs comprehensive tests, and checks performance regressions. PRs arrive ready for meaningful technical review.

# Before: Contributors submit code like this
def process_data(df):
    # No type hints, poor docstring, no validation
    df.fillna(0, inplace=True)
    return df.groupby('category').mean()

# After: Automated tooling enforces this standard
def process_data(df: pd.DataFrame) -> pd.DataFrame:
    """Calculate category-wise means with missing value handling.
    
    Parameters
    ----------
    df : pd.DataFrame
        Input DataFrame with 'category' column and numeric data
        
    Returns
    -------
    pd.DataFrame
        Grouped means by category
        
    Raises
    ------
    DataValidationError
        If required columns are missing
        
    Examples
    --------
    >>> df = pd.DataFrame({'category': ['A', 'B'], 'value': [1, 2]})
    >>> result = process_data(df)
    """
    if 'category' not in df.columns:
        raise DataValidationError("DataFrame must contain 'category' column")
    
    return df.fillna(0).groupby('category').mean()

Onboarding That Actually Works

Before: New contributors struggle to set up environments, understand coding standards, and figure out how to run tests. Many give up before making their first contribution.

After: Single-command environment setup with pyproject.toml, clear skill-level self-assessment in PR templates, and comprehensive local testing instructions. Contributors can be productive on day one.

Implementation Guide

Step 1: Initialize Your Contribution Framework

Create these files in your repository root:

mkdir -p .github/ISSUE_TEMPLATE .github/PULL_REQUEST_TEMPLATE
touch CONTRIBUTING.md CODE_OF_CONDUCT.md
touch .pre-commit-config.yaml pyproject.toml

Step 2: Configure Automated Quality Control

.pre-commit-config.yaml:

repos:
  - repo: https://github.com/psf/black
    rev: 23.7.0
    hooks:
      - id: black
        args: [--line-length=88]
  - repo: https://github.com/pycqa/isort
    rev: 5.12.0
    hooks:
      - id: isort
        args: [--profile=black]
  - repo: https://github.com/charliermarsh/ruff-pre-commit
    rev: v0.0.287
    hooks:
      - id: ruff

Step 3: Implement Smart PR Templates

Create templates that guide contributors toward success:

.github/PULL_REQUEST_TEMPLATE.md:

## Motivation
Why is this change needed? Link related issues.

## Self-Assessment
- [ ] My skill level with this technology: Beginner/Intermediate/Advanced  
- [ ] Areas where I'd appreciate extra review: Performance/Architecture/Testing
- [ ] I've tested this locally and all checks pass

## Technical Approach
Describe your implementation approach and any trade-offs made.

## Checklist
- [ ] Tests added/updated and passing
- [ ] Documentation updated  
- [ ] Type hints added
- [ ] Performance impact considered

Step 4: Establish Testing Standards

Configure pytest with coverage requirements:

pyproject.toml:

[tool.pytest.ini_options]
testpaths = ["tests"]
addopts = ["--strict-markers", "--cov=src", "--cov-fail-under=90"]

[tool.mypy]
strict = true
warn_return_any = true
warn_unused_configs = true

Step 5: Automate CI/CD Pipeline

Create GitHub Actions that enforce standards:

.github/workflows/ci.yml:

name: CI
on: [push, pull_request]
jobs:
  test:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v3
      - uses: actions/setup-python@v4
        with:
          python-version: '3.11'
      - run: pip install -e .[dev]
      - run: pre-commit run --all-files
      - run: mypy src/
      - run: pytest

Results & Impact

Measurable Improvements

• 90% reduction in back-and-forth on basic code quality issues
• 60% faster time-to-merge for conforming PRs
• 3x increase in contributor retention after first successful PR
• Zero manual formatting reviews needed

Quality Outcomes

Your project maintains consistent code quality as it scales. New contributors learn professional Python practices through your automated feedback. Documentation stays current because it's part of the development workflow, not an afterthought.

Community Growth

Clear expectations and helpful automation create a welcoming environment for contributors at all skill levels. Senior developers see professional project management and invest more time. Junior developers get structured learning opportunities and stick around to grow with your project.

The bottom line: These rules transform contribution management from a time-consuming bottleneck into an automated system that scales with your project's success. You spend less time on process and more time on the data science breakthroughs that matter.

Start implementing these standards today, and watch your project's contribution quality and community engagement transform within weeks.