Jekyll is a mature, blog-aware static site generator written in Ruby. It transforms plain text files (Markdown, Liquid templates, HTML, Sass, CoffeeScript) into static websites, supporting collections, posts, drafts, data files, gem-based themes, and a rich plugin system. The architecture is a monolith library with a CLI interface (build, serve, new, clean, doctor, new-theme) and a built-in development server with LiveReload. Key data flows: source files are read by a Reader, processed through converters (Markdown via Kramdown, SmartyPants, etc.), rendered with Liquid templates and layouts, and written to a destination directory. Incremental builds are supported via a regenerator that tracks file modifications and dependencies. The codebase is well-structured with extensive unit and integration tests (Minitest, Cucumber), benchmarks, and profiling tools. It demonstrates strong engineering practices: a plugin architecture with hooks, converters, generators, tags, and filters; a drop-based Liquid integration; caching; and platform-aware utilities. The project is actively maintained with frequent releases, a formal release process, and community governance.
Huginn is a self-hosted, hackable automation platform built as a Ruby on Rails application. It allows users to create and chain 'agents' that monitor, scrape, transform, and act on data from hundreds of sources (web, RSS, email, Twitter, Telegram, Slack, OpenAI, etc.) via an event-driven architecture. Agents are polymorphic Ruby classes with a consistent lifecycle: scheduled checks, event reception, and event emission. The system supports Liquid templating for dynamic configuration, OAuth services, file pointers, and background job processing via DelayedJob. The codebase is extensive with 80+ agent types, reusable concerns (WebRequestConcern, LiquidInterpolatable, FormConfigurable), and a comprehensive test suite. While primarily an application, it includes a small research dataset (ANEW sentiment norms) used by a sentiment analysis agent, but the dominant character is clearly a production automation service.
lewagon/data-setup is a Ruby-based documentation build system that generates the Le Wagon Data Science bootcamp environment setup guides for macOS, Windows, Linux, VM, and remote setups. It reads OS-specific YAML build specifications, resolves localized markdown partials from `_partials/` (including Spanish translations and remote partials fetched from other lewagon repositories), renders them with Liquid templating, and writes final bilingual guides back to markdown files. The primary entry point is `build.rb`, orchestrated by `lib/builder.rb` and `lib/generator.rb`, with CI via GitHub Actions rebuilding and committing generated files on every push. Despite the data-science branding, this is not a data-research repository: there is no pipeline, notebook, or model code. The only data-related artifacts are pinned Python package requirement files under `specs/releases/` and environment verification scripts in `checks/`. The dominant character is an application — specifically a content-generation toolchain for setup documentation. It demonstrates a clean partial-composition model, locale-aware fallback, remote content fetching with image-path rewriting, and parallel partial caching. The repository is actively maintained and technically current, though its reliance on pinned Python versions and remote GitHub raw URLs creates an ongoing need for upkeep. It is a well-structured niche tool rather than a general-purpose framework, but its modular design makes parts of the build pipeline reusable in similar multi-source documentation projects.