Laravel Algos Laravel Package

Getting Started

• Install with composer require foxws/laravel-algos. The package auto-discovers its service provider.
• Publish any config (if available) with php artisan vendor:publish --tag="laravel-algos-config". Check config/algos.php for registered algorithm mappings.
• First place to look: the package README and src/ directory. Algorithm classes are typically under Foxws\Algos\Algorithms or exposed via an Algos facade.
• First use case: inject an algorithm into a controller or service and run it on a collection or array. For example:

  use Foxws\Algos\Facades\Algos;
  
  $sorted = Algos::sort($items, 'quick');
  

  Or resolve a concrete algorithm class via the container:

  use Foxws\Algos\Algorithms\QuickSort;
  
  public function index(QuickSort $sorter) {
      return $sorter->sort($data);
  }
  

• If you use Laravel Boost, version 0.3.2 ships a Boost skill and tip. Look for a boost or resources/boost directory (or check the package’s composer.json extra section) to load the skill into your AI assistant context.

Implementation Patterns

• Dependency injection over facades for testability. Type-hint algorithm interfaces or concrete classes in constructors, jobs, and actions.
• Facade for quick scripts / tinker. Use Algos:: for ad-hoc runs, but prefer DI in application code.
• Pipeline composition. Chain algorithm calls or wrap them in Laravel’s Pipeline when you need multiple processing steps (e.g. filter → sort → paginate).
• Eloquent integration. Use algorithms on Collection results or inside query scopes when you need custom ordering/grouping that SQL cannot express efficiently:

  $users = User::all();
  $ranked = Algos::rank($users, 'relevance');
  

• Custom algorithms. Extend the package’s base algorithm class (if provided), implement the required interface, and register it in config/algos.php under the appropriate key. This is the main extension point.
• Queue heavy work. For large datasets, dispatch a job that runs the algorithm and stores results, rather than blocking the request.
• Testing. Bind a fake or mock implementation in the container for unit tests. If the package provides a facade, use Algos::shouldReceive(...).
• Laravel Boost skill. The new skill is a development aid, not runtime code. Commit it to your repo’s AI context (e.g. .boost/skills/) so assistants generate correct usage examples.

Gotchas and Tips

• Memory usage. Many algorithms load data into memory. Avoid passing large Eloquent result sets directly; chunk or use database-native operations where possible.
• Config publishing is not automatic. If you need to override algorithm mappings or defaults, publish the config first; otherwise the package uses its internal defaults.
• Version pinning. The package is pre-1.0 (0.3.x). Pin to a specific minor version in composer.json ("foxws/laravel-algos": "^0.3") and review changelogs before upgrading.
• Laravel Boost skill is optional. It only works if you have Laravel Boost installed. It adds no runtime dependencies, but if your AI tooling does not read Boost skills, ignore it.
• Debugging. Use dd() or Laravel Telescope on algorithm inputs/outputs. If a facade call fails, check that the service provider is registered (it should be auto-discovered) and that the algorithm key exists in config.
• Extension points. The config file and base algorithm class are the intended customisation points. Avoid modifying vendor files; register your own implementations instead.
• Performance profiling. For CPU-bound algorithms, benchmark with realistic data sizes. Consider opcache/JIT settings in production.