GitHub - rubyonai/rails-llm-integration: 🔥🔥 A Claude Skill that teaches Claude Code how to write LLM features

# Raw API call in a controller. No retries, no cost tracking, blocks the request.
def create
  client = OpenAI::Client.new(access_token: ENV["OPENAI_API_KEY"])
  response = client.chat(parameters: {
    model: "gpt-4o",
    messages: [{ role: "user", content: "Describe #{@product.name}" }]
  })
  @product.update!(description: response.dig("choices", 0, "message", "content"))
end

Important

This skill gives Claude Code a set of Rails conventions for LLM calls so it generates consistent, structured code whenever you ask it to build AI features.

LLM::GenerateDescriptionJob.perform_later(product_id: @product.id)

app/
  services/llm/
    product_description_service.rb    # Business logic, validation, parsing
    base_service.rb                   # Tracing, retries, cost tracking (inherited)
  jobs/llm/
    generate_description_job.rb       # Async with typed retry rules
  prompts/
    product_descriptions/
      generate.system.erb             # Versioned in git, tested on its own
      generate.text.erb
config/
  llm.yml                            # Model routing + budget caps (like database.yml)

module LLM
  class ProductDescriptionService < BaseService
    self.task_type = :generation    # Routes to the right model automatically

    def validate_params!(params)
      raise ArgumentError, "product required" unless params[:product]
    end

    def prompt_template
      "product_descriptions/generate"
    end

    def parse_response(response)
      content = response.dig(:choices, 0, :message, :content)
      { description: content.strip }
    end
  end
end

cp -r rails-llm-integration/ your-rails-app/.claude/skills/rails-llm-integration/

> Add AI-powered product descriptions to my app
> Set up LLM service objects with cost tracking
> Create a ticket classification service using ruby_llm

Note

Claude reads the skill's reference docs and generates code following these conventions.