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IssueScout is an open source issue discovery platform that helps find GitHub issues labeled as 'good first issue'. It features community health scores, AI-driven difficulty estimates, and personalized recommendations, making it easier to connect with welcoming projects that align with individual skills.
IssueScout: Discover Your First Open Source Contribution
IssueScout is a comprehensive open source issue discovery platform designed to assist users in finding suitable "good first issue" opportunities on GitHub. It not only surfaces relevant issues but also enriches them with community health scores, AI-driven difficulty estimations, and personalized recommendations tailored to individual skills and preferences. This enables contributors to explore welcoming projects and avoid time-consuming searches through abandoned repositories.
Community Health Score: Each repository receives a score ranging from 0 to 100 based on seven critical factors including the presence of contributing guidelines, licenses, code of conduct, recent activity, popularity (stars), issues response time, and pull request merge rate. This score helps contributors gauge whether a repository is open to new contributors.
AI Difficulty Estimation: The platform employs a dual-layer system for assessing issue difficulty, starting with a fast rule-based analysis and utilizing the GPT-4o-mini model for cases where confidence in the initial assessment is low. This ensures transparency, as indicated by a badge when AI assistance is utilized.
Personalized Recommendations: By signing in with GitHub, users can access personalized recommendations based on their programming languages and interests, with the "For You" tab displaying tailored issues that match their skill set.
Progressive Loading: Issues are displayed instantly, with health scores and difficulty badges enriching asynchronously, which eliminates loading delays and enhances the user experience.
Smart Filtering: Users can filter issues by 18 labels across four categories, including language and difficulty filters, along with sort options and a claimed/unclaimed toggle, allowing for precise search capabilities.
Bookmarks: Users can save issues for future reference and archive completed issues, with complete snapshots preserved, even if the original issue undergoes changes.
Two-Level Caching: Issue difficulty is cached for 24 hours, whereas repository health data is cached permanently using a stale-while-revalidate approach, enhancing efficiency and performance.
User-Specific API Tokens: Each user’s searches are powered by their GitHub OAuth tokens, allowing for 5,000 requests per hour without encountering shared rate limit bottlenecks.
Dark Mode: The application detects user system preferences and provides a manual toggle for dark mode.
A live demonstration of IssueScout is available at the following link: Live Demo.
Contributions are appreciated and encouraged. For information on how to contribute, refer to the CONTRIBUTING.md file.
Usage analytics are collected only after obtaining explicit cookie consent, ensuring compliance with privacy standards. Users who self-host can modify or remove the analytics components as needed.
Would love to get some feedback, contributions, forks, stars :)
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