A modern analytics pipeline for tracking and analyzing GitHub contributions across repositories with AI-powered summaries and leaderboards.
GitHub Contributor Analytics is a modern analytics pipeline for tracking and analyzing GitHub contributions across multiple repositories. It processes contributor data, calculates activity scores, generates AI-powered summaries, and maintains interactive leaderboards with MMORPG-style progression tiers and character classes. The system provides detailed insights into pull requests, issues, reviews, and comments through a fully automated pipeline.
Open-source project maintainers and community managers who need to track, analyze, and reward contributor activity across multiple repositories. It's also suitable for developers building dashboards or tools that consume structured GitHub contribution data via its static JSON API.
Developers choose this over basic GitHub analytics because it provides gamified leaderboards with progression tiers, AI-generated summaries of contributions, and a clean separation between code and data using a specialized branch strategy. Its static JSON API makes contributor data easily consumable by external tools and dashboards without requiring a live backend.
Leaderboard of Eliza Contributors
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Implements MMORPG-style progression tiers (beginner to legend) and character classes like Builder and Hunter, making contributor tracking motivational and interactive, as shown in the leaderboard API.
Leverages OpenRouter API to generate daily, weekly, and monthly summaries for repositories and contributors, reducing manual reporting effort, though it requires an API key and incurs costs.
Exports data as consumable static endpoints like /api/leaderboard-monthly.json, enabling integration with external tools without a live backend, detailed in the API documentation.
Uses a separate _data branch and sqlite-diffable for efficient Git history, keeping code commits clean and supporting CI/CD workflows, as described in the Data Management Architecture section.
Requires multiple dependencies (Bun, GitHub token, OpenRouter API key, uv for data syncing) and detailed configuration in pipeline.config.ts, which can be overwhelming for initial deployment.
AI summary generation depends on OpenRouter API with variable expenses; the README warns lifetime summaries are 'expensive' and memory-intensive, leading to unpredictable costs.
Data is processed daily via GitHub Actions, so insights are not real-time and delays occur, making it unsuitable for live monitoring despite the automation options.