Discover and rank Seattle's top GitHub projects and open source repositories
A comprehensive system that collects, validates, and ranks open source projects from Seattle-based GitHub users. Features three-stage API collection strategy, intelligent multi-factor scoring, secondary validation workflow, distributed processing with Celery/Redis, PyPI integration, and automated weekly updates via GitHub Actions.
Live Website: https://thomas0829.github.io/Seattle-Source-Ranker/
- 458,693 projects tracked across Seattle's developer community
- 3,051,941 total stars accumulated by Seattle projects
- 29,504 users collected in latest run
- 1,204 Python projects published on PyPI (1.99% of Python projects)
- 28 Python projects in global Top 15,000 PyPI packages (0.07% of Python projects)
- Last updated: 2026-07-27 03:39:43 PDT
- Discover Quality Projects: Find high-quality Seattle-based open source projects to learn from and contribute to
- Find Libraries & Tools: Search for well-maintained local packages and tools to integrate into your projects
- Track Technology Trends: Explore what technologies and frameworks are trending in Seattle's tech community
- Network & Collaborate: Identify active maintainers and projects aligned with your interests
- Talent Discovery: Find skilled developers based on their open source contributions and project quality
- Evaluate Technical Skills: Assess a developer's expertise through their project portfolio and activity metrics
- Market Research: Understand the local tech landscape and identify companies with strong open source presence
- Community Insights: Track emerging technologies and popular frameworks in Seattle's developer ecosystem
- Three-Stage API Collection - GraphQL user discovery → REST repo data → GraphQL validation
- Secondary Validation - Removes ~2% invalid repos, fixes incorrect watchers count
- Distributed Processing - 16 parallel tasks via Celery workers for efficient collection
- Multi-Token Support - Intelligent rotation system (6 tokens recommended)
- Multi-Factor Scoring - SSR algorithm balancing popularity, quality, and maintenance
- PyPI Integration - Python project detection with tiered bonus scoring
- Interactive Web UI - React-based with real-time search and smooth pagination
- Weekly Auto-Updates - Automated collection and deployment via GitHub Actions
- 10 Languages Supported - Separate rankings for major programming languages
# Clone and install
git clone https://github.com/thomas0829/Seattle-Source-Ranker.git
cd Seattle-Source-Ranker
pip install -e .The package can be imported and used as a Python library:
from seattle_source_ranker.tokens import TokenManager
from seattle_source_ranker.pypi import PyPIChecker
from seattle_source_ranker.scoring import calculate_ssr_score
# Load GitHub tokens
token_mgr = TokenManager()
token = token_mgr.get_best_token()
# Check if a project is on PyPI
checker = PyPIChecker()
result = checker.check_project("requests", "requests")
print(f"On PyPI: {result.on_pypi}")
# Calculate SSR score for a project
score = calculate_ssr_score(
stars=5000,
forks=1000,
watchers=500,
created_at="2015-01-01",
pushed_at="2024-12-01",
open_issues=50
)
print(f"SSR Score: {score:,.2f}")For more details, see the source code and documentation.
./run_local.shRuns the complete pipeline automatically - from data collection to frontend deployment.
Prerequisites:
- Python 3.11+
- Redis server
- 6 GitHub tokens in
.env.tokens(setup guide)
For step-by-step control:
# 1. Install dependencies
conda env create -f environment.yml && conda activate ssr
# OR: pip install -e .
# 2. Configure tokens (see docs/MULTI_TOKEN_GUIDE.md)
# Edit .env.tokens with your GitHub tokens
# 3. Start Redis
redis-server --daemonize yes
# 4. Run collection (~60-90 min)
python main.py --max-users 30000 --workers 8
# 5. Secondary validation (~45 min)
bash scripts/start_workers.sh
python scripts/secondary_update.py
bash scripts/stop_workers.sh
# 6. PyPI integration
python scripts/update_pypi_official_index.py
python scripts/generate_pypi_projects.py
python scripts/update_top_pypi_packages.py
# 7. Generate frontend
python scripts/generate_frontend_data.py
python scripts/update_readme.py
# 8. Start dev server
cd frontend && npm startProjects are ranked using a comprehensive multi-factor scoring system that balances popularity with quality and maintenance signals.
Stars × 40% - Primary popularity indicator
Forks × 20% - Engagement and derivative work
Watchers × 10% - Ongoing interest and monitoring
Age × 10% - Project maturity (peak at 3-5 years)
Activity × 10% - Recent maintenance (last push time)
Health × 10% - Issue management (issues relative to popularity)
Score = (
log₁₀(stars + 1) / log₁₀(100000) × 0.40 +
log₁₀(forks + 1) / log₁₀(10000) × 0.20 +
log₁₀(watchers + 1) / log₁₀(10000) × 0.10 +
age_factor() × 0.10 +
activity_factor() × 0.10 +
health_factor() × 0.10
) × 1000000
Python projects published on PyPI receive tiered scoring enhancements:
Base Score Range: 0-1,000,000 points
Tier 1 - Any PyPI Package: Base Score × 1.05
Tier 2 - Top 15K Global PyPI: Base Score × 1.05 × 1.10 = × 1.155
Examples:
Not on PyPI: 756,000 points → 756,000 final
Regular PyPI: 756,000 points → 793,800 final (+5%)
Top 15K PyPI: 756,000 points → 873,180 final (+15.5%)
Tier 1 - Any PyPI (5% bonus):
- Applies to ~1K packages (~2.7% of Python projects)
- Distribution Commitment - Package is ready for
pip install - Ecosystem Integration - Can be used as a dependency in other projects
Tier 2 - Top 15K Global (additional 10% bonus):
- Applies to ~30 packages (~0.07% of Python projects)
- Global Impact - Millions of downloads worldwide
- Production Scale - Among most-used Python packages
- Combined bonus: ×1.155 total (+15.5%)
The tiered system rewards both PyPI publication and global ecosystem impact while maintaining fairness to development-focused repositories.
- Logarithmic Scaling - Better distribution across projects of different sizes
- Age Maturity - Rewards established projects (2-8 years), penalizes too new/old
- Recent Activity - Prefers actively maintained projects
- Health Metrics - Considers issue management quality relative to popularity
- Tiered PyPI Bonuses - Recognizes both production-ready and globally-impactful Python packages
- Backend Calculation - All scoring done in backend, frontend displays final scores
Projects are ranked both overall and by programming language (10 major categories: JavaScript, Python, HTML, Java, TypeScript, C#, Ruby, CSS, C++, Jupyter Notebook).
For detailed factor calculations and examples, visit the Scoring Methodology page on the live website.
Having issues? Check the Troubleshooting Guide for:
- Common errors and solutions (Redis, rate limits, collection failures)
- Frontend build issues
- Frequently Asked Questions (watchers, tokens, file management)
- Troubleshooting Guide - Common issues, solutions, and FAQs
- Multi-Token Setup - GitHub token configuration and optimization
- Changelog - Version history and release notes
- Contributing - How to contribute to this project
This project is licensed under the MIT License - see the LICENSE file for details.
- GitHub API (GraphQL v4 + REST v3) for comprehensive data access
- PyPI API for Python package ecosystem data
- Top 15K PyPI packages ranking from hugovk/top-pypi-packages
- Seattle's developer community for creating amazing open source projects
- Celery & Redis for enabling distributed processing and task queuing
- React for powering the interactive web interface
- GitHub Actions for automated weekly workflows and deployment
Seattle Source Ranker 1.0.0 - Production-ready ranking system with advanced search and PyPI integration
Statistics automatically updated weekly by GitHub Actions every Monday at midnight Seattle time.
Made with love for Seattle's tech community