Dotter is a dotfile manager and templater written in Rust. It manages symbolic links and Handlebars-based templates for configuration files, supporting conditional deployment based on variables, packages, and host-specific configurations. The architecture is a single CLI binary with subcommands for deploy, undeploy, init, watch, and shell completion generation. It reads TOML configuration files (global.toml, local.toml) that define packages, files, and variables, then merges them with optional includes and patches. The deployment logic uses a trait-based Filesystem abstraction to support real operations and dry-run simulation, with platform-specific handling for Unix (sudo elevation) and Windows (symlink detection). The codebase demonstrates strong Rust practices: extensive use of traits for testability, mockall for unit testing, comprehensive error handling with anyhow, and a modular structure separating concerns (config, actions, filesystem, handlebars helpers, diff). It is actively maintained with recent commits, CI/CD pipelines, and a well-documented wiki.
dotdrop is a command-line tool for managing dotfiles across multiple hosts using profiles, Jinja2 templating, actions, and transformations. It stores dotfiles in a Git repository and deploys them to target systems, allowing different configurations per host or environment. The architecture is modular: separate Python modules handle each operation (install, compare, update, import, remove, uninstall), with a central dispatcher in `dotdrop.py`. Configuration is defined in YAML (or optionally TOML) files that specify dotfiles, profiles, variables, actions, and transformations. Key entry points are the CLI subcommands invoked via `dotdrop <command>`. Data flows involve parsing the config, aggregating imported configs and variables, then executing file operations (copy, symlink, diff) with optional template rendering and shell commands. External dependencies include Jinja2, docopt, PyYAML, and optional libraries for TOML support and system detection. The codebase is well-tested with both Python unit tests and a large suite of Bash integration tests, and it is distributed via PyPI, Homebrew, AUR, Snap, and other channels.