$ show patrickkfkan/patreon-dl
This repository implements a comprehensive CLI tool and library for downloading content from Patreon. It supports posts, products, media (images, videos, audio, attachments), and metadata like campaign info, comments, and collections. The downloader uses cookie-based authentication to access patron-only content and handles various embedded media providers (YouTube, Vimeo, SproutVideo) via internal tasks or external downloader hooks. The architecture is event-driven and modular: a base Downloader class orchestrates fetchers, parsers, and download task batches, with typed entities and extensive configuration options (proxy, concurrency, file existence actions, naming formats). A built-in web server with a React frontend allows browsing downloaded content via a local SQLite database with full-text search. The code demonstrates strong engineering practices: class mixins for database and API layers, a task hierarchy with retry and abort support, a chainable logger abstraction, and a utility-heavy core that is largely decoupled from the Patreon-specific logic. However, the tight coupling to Patreon's undocumented API structure, lack of automated tests, and some deprecated options are notable weaknesses.
The LLM identified 2 files worth highlighting for reuse:
A logger implementation that composes multiple Logger instances (e.g., ConsoleLogger and FileLogger) and delegates log calls to each in order. This allows flexible, runtime-configurable logging without modifying the logger interface. The pattern is cleanly isolated (imports only the Logger interface) and can be extracted into any Node.js project that needs multi-sink logging.
A generic batch download task manager that queues individual download tasks, enforces concurrency limits via the Bottleneck library, and emits granular progress events (taskStart, taskProgress, taskEnd, etc.) with typed payloads. It handles retries, file conflict resolution, and abort signals. The component is self-contained (depends only on the task interface and common event types) and demonstrates a robust, reusable pattern for any batch processing pipeline.