$ show anuraghazra/github-readme-stats
This repository is a serverless application that dynamically generates SVG cards (stats, top languages, pinned repos, gist stats, WakaTime stats) for GitHub profile READMEs. It is designed to be deployed on Vercel and uses the GitHub GraphQL API and WakaTime REST API as data sources. The architecture consists of stateless API endpoints that fetch data, apply caching and access control, and render SVG images using a custom Card class and layout system. The codebase is well-structured with extensive unit tests, benchmarks, and a theme system supporting 60+ color themes. However, the project is explicitly deprecated and no longer maintained, with the README pointing to a successor. Despite this, the code demonstrates solid engineering practices, including a reusable retry mechanism with token rotation, an internationalization module, and a flexible SVG layout engine. The main entry points are the API handlers in `api/` and the card renderers in `src/cards/`.
The LLM identified 4 files worth highlighting for reuse:
A base SVG card class that encapsulates rendering of styled, animated, and accessible SVG cards. It handles gradients, animations, CSS injection, and accessibility attributes. This component is self-contained and could be extracted to generate SVG cards for any data source with minimal modification.
A generic retry function that handles GitHub API rate limits by rotating through multiple personal access tokens (PAT_1, PAT_2, ...). It retries on specific error messages and supports configurable max retries. This pattern is reusable for any API client that needs token rotation and retry logic.
A lightweight internationalization class that loads locale-specific translations and provides a `text()` method with fallback to a default locale. It supports dynamic string interpolation. This component is self-contained and can be reused in any project needing simple i18n without heavy frameworks.
A rank calculation module that uses a log-normal cumulative distribution function to compute a user's rank level (S, A+, A, B+, B, C) and percentile based on total stars, commits, PRs, issues, and contributions. The mathematical approach is non-obvious and demonstrates statistical thinking. It is isolated and could be reused for any ranking system.