Which AI agents are compatible with the Ruby Engineering skill?

This skill is designed to work with any LLM-based agent that has file-system access or code-editing capabilities, such as AutoGPT, BabyAGI, or custom developer agents built on OpenAI's GPT-4.

How does this skill handle 'Fat Models' in legacy Rails applications?

It specializes in decoupling logic from bloated models and controllers into Service Objects, Form Objects, and Plain Old Ruby Objects (POROs) to eliminate technical debt.

Can this skill work with older versions of Ruby or specific Rails releases?

Yes, the skill is designed to analyze your Gemfile and lockfile to ensure all refactored code and suggestions remain compatible with your specific Ruby and Rails versions.

What exactly is included in the purchase of this skill?

You receive the full skill definition, optimized logic for Ruby architecture, and a set of specialized guidelines that dictate how the agent should handle RSpec testing and performance auditing.

Does this skill provide deep server-level performance optimization?

The skill focuses on application-layer performance, such as resolving N+1 queries through ActiveRecord and reducing object allocation, rather than low-level C-extension or memory management tuning.

Can I use this skill for API-only Ruby applications?

While it specializes in backend Ruby, it is fully capable of refactoring Grape APIs and building robust RESTful endpoints that follow industry-standard serialization patterns.

RUBY Engineering

Example session with this skill installed

Refactor this UserRegistration service. It currently handles file uploads, email sending, and database writes all in one method. I want it to be more modular and easier to test.

Read your context and instructions
Compiled the ruby engineering

I have refactored the UserRegistration logic. The orchestration is now handled by a dedicated service object, with file handling and notifications extracted into focused modules.

Refactored User Registration Service


# app/services/user_registration_service.rb
class UserRegistrationService
  include Wisper::Publisher

  attr_reader :params, :user

  def initialize(params)
    @params = params
    @user = User.new(params.slice(:email, :password, :name))
  end

  def call
    return broadcast(:invalid, user) unless user.valid?

    ActiveRecord::Base.transaction do
      user.save!
      process_avatar if params[:avatar]
    end

    send_welcome_email
    broadcast(:success, user)
  rescue ActiveRecord::RecordInvalid => e
    broadcast(:error, e.message)
  end

  private

  def process_avatar
    AvatarProcessor.call(user, params[:avatar])
  end

  def send_welcome_email
    UserMailer.welcome(user).deliver_later
  end
end

Next steps

Update the UsersController to subscribe to the :success and :invalid events from this service.
Add a unit test in spec/services/user_registration_service_spec.rb to verify the transaction rollbacks.
Verify that AvatarProcessor handles storage provider errors without crashing the registration flow.

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About this skill

Legacy Ruby codebases often suffer from callback hell, bloated models, and opaque metaprogramming that makes debugging a nightmare. This skill enforces idiomatic Ruby and clean Rails architecture to ensure your services and gems remain maintainable as they scale.

What it does

Architectural refactoring extracts business logic into focused service objects and plain Ruby modules.
Deep debugging isolates failures across routing, model validations, background jobs, and serialization layers.
Performance auditing identifies N+1 queries, object churn, and eager loading issues before they hit production.
Convention enforcement maintains consistency with existing codebase patterns while applying modern Ruby best practices.

How it works

Context analysis identifies whether the environment is a Rails app, a standalone gem, or a background worker.
Dependency mapping inspects the Gemfile and framework versions to ensure compatible syntax and patterns.
Logic isolation breaks down complex orchestration into readable, testable units of code.
Validation runs existing test suites and linters to verify the integrity of the proposed changes.

Frameworks & tools

This skill works with Ruby, Ruby on Rails, RSpec, Minitest, and standard linting tools like RuboCop. It handles diverse environments including Sidekiq workers, Grape APIs, and CLI tools.

Why this beats prompting it yourself

Generic AI often suggests "magic" Rails helpers or heavy metaprogramming that obscures logic. This skill prioritizes explicit dependencies and small objects, preventing technical debt from accumulating in your models and controllers.

Use cases

Refactor a 500-line Rails model into testable service objects.
Debug a silent failure in a complex Sidekiq job or callback chain.
Optimize a slow controller action by resolving deep N+1 query patterns.
Modernize a legacy Ruby gem to follow current community standards.

Known limitations

Does not provide specialized frontend JavaScript support. Performance tuning focuses on application-level Ruby/SQL rather than low-level C extension optimization.

How to use

Skills on Agensi run inside your AI agent. Copy the prompt, paste it into your agent, and it connects to Agensi and installs this skill for you. If a payment is needed, your agent shows it to you first.

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RUBY Engineering