Alexa is awesome and I think that conversational software is the future. This post documents what I set myself as a technical learning challenge:

  • Host the skill locally, to allow a fast development feedback cycle prior to pushing code.
  • To find a way to automated tests (unit, functional and end-to-end), as most demos refer to manual testing.
  • To use something other than JS (like most of the demos do)
  • To write an Alexa skill that's backed by a data store
  • To be able to handle conversations.

The way Alexa services interact with apps is the following:

User->Echo: "Alexa, ..."
Note right of Echo: Wakes on 'Alexa'
Echo->Amazon: Streams data spoken
Amazon->Rails: OfficeIntent
Rails->SkillsController: POST
SkillsController->Amazon: reply (text)
Amazon->Echo: reply (voice)
Echo->User: Speaks

The skill

The skill is a data retrieval one, giving information about the company’s offices and the workers there.

Alexa, Rails, git, ngrok and an Amazon account

I bought a dot and set up an Amazon account to register the skill on.

Install Rails and git for your OS. You’ll also need a data-store, easily using sqlite, or mysql gems.

ngrok is a nifty tool that will tunnel Alexa calls in to our local server.

Get the code

Fork or clone the repo for a head-start, or read along taking only pieces you need from this post.

Set up the app

  • Setting some environment variables

The database connection use the following environment variables:

  • Setting up the database
rake db:create db:migrate db:seed spec

This will create and setup the database tables, seed the development tables and run the unit and integration tests.

  • Running tests

Will run all tests excluding the audio tests, which I’ll describe below. Make sure all tests pass.

Connecting to the real thing

When a user invokes your skill, Amazon will route requests to an endpoint listed on the Alexa site. In order for this to function, you must first configure the skill there. It’s straightforward, but must be manually uploaded to the skill’s configuration page on Amazon’s site.

Intent schema

This is where you define the intents the user can express to your skill. I think of ‘intents’ as the skill’s ‘methods’, if you think of the skill as an object.


Permutations on the intent's syntax. For example:

Bookit for vacant rooms between {StartDate} and {EndDate}
OfficeWorkers who the {Staff} from {Office} are

Slot types

Here are the slot types for our skill, defining synonyms for our slots, being the parameters for intents. If you think this is complex, please remember that I am only the messenger here...


Now that you have configured the skill’s interfaces, we now need to route communications from Amazon to our local server running Rails as we develop and debug. This is easily done using ngrok, explained below.


ngrok is a service, with a free tier, that will redirect traffic from outside your home/office’s firewall into your network. Once configured, it will route traffic from Amazon to our http://localhost:3000, essential for our aspired fast development cycle.

Run it using:

ngrok http 3000

Your configuration may vary, depending on whether you are paying customer or not, so change ‘endpoint’ accordingly.

You’ll see something like this once you run it:


Add your endpoint to Amazon’s skill page under configuration:


Generating a certificate

Once you’ve settled on the endpoint URL, you’ll need to create or reuse a certificate for Amazon to use when communicating with your server process.

genrsa 2048 > private-key.pem
openssl req -new -key private-key.pem -out csr.pem
openssl req -new -x509 -days 365 -key private-key.pem -config cert.cnf -out certificate.pem

Copy the the contents of ‘certificate.pem’ to the skill’s page on Amazon:


Toggle the test switch to ‘on’, otherwise Amazon will think you’re trying to publish the skill on their Skills store:


Last but not least, enable the skill on your iPhone or Android by launching the Alexa app and verifying that the skill exists in ‘Your skills’ tab.

Amazon recap

We uploaded the skill info, including:

  • The Interaction model, uploading the 'intent schema’, ‘Custom slot types’, and ‘Sample utterances’.
  • Configured the end-point
  • Uploaded the SSL cert
  • Enabled the test flag
  • Verified that the skill is enabled by using your Alexa app on your mobile device

The moment we’ve been waiting for

Run your rails app:

rails s

Run ngrok in another terminal window:

ngrok http 3000

Say something to Alexa:

Alexa, tell Buildit to list the offices

If all goes well, you should:

  • See the request being logged in the ngrok terminal (telling you that Amazon connected and passed the request to it)
  • See that the rails controller got the request by looking at the logs
  • Hear the response from your Alexa device

If there was a problem at this stage, please contact me so I can improve the instructions.

Code walkthrough

Route to a single skills controller:

 Rails.application.routes.draw do
   # Amazon comes in with a post request
   post '/' => 'skills#root', :as => :root

Set up that controller:

class SkillsController < ApplicationController
  skip_before_action :verify_authenticity_token

  def root
    case params['request']['type']
      when 'LaunchRequest'
        response =
      when 'IntentRequest'
        response =['request']['intent'])
     render json: response

Handle the requests:

def respond intent_request
  intent_name = intent_request['name']

  Rails.logger.debug { "IntentRequest: #{intent_request.to_json}" }

  case intent_name
    when 'ListOffice'
      speech = prepare_list_office_request
    when 'OfficeWorkers'
      speech = prepare_office_workers_request(intent_request)
    when 'OfficeQuery'
      speech = prepare_office_query_request(intent_request)
    when 'Bookit'
      speech = prepare_bookit_request(intent_request)
    when 'AMAZON.StopIntent'
      speech = 'Peace, out.'
      speech = 'I am going to ignore that.'

  output =

Test walkthrough

Unit tests

Really fast, not touching any Alexa or controller code, just making sure that the methods create the correct responses:


require 'rails_helper'

RSpec.describe 'Office' do
  before :all do
    @intent_request =
  describe 'Intents' do
    it 'handles no offices' do
      expect(@intent_request.handle_list_office_request([])).to match /We don't have any offices/

    it 'handles a single office' do
      expect(@intent_request.handle_list_office_request(['NY'])).to match /NY is the only office./

    it 'handles multiple offices' do
      expect(@intent_request.handle_list_office_request(['NY', 'London'])).to match /Our offices are in NY, and last but not least is the office in London./

Integration tests

Mocking out Alexa calls, ensure that the JSON coming in and out is correct:

describe 'Intents' do
  describe 'Office IntentRequest' do
    it 'reports no offices' do
      request = JSON.parse('spec/fixtures/list_offices.json'))
      post :root, params: request, format: :json
      expect(response.body).to match /We don't have any offices/

    it 'reports a single office' do
      request = JSON.parse('spec/fixtures/list_offices.json'))
      Office.create name:'London'
      post :root, params: request, format: :json
      expect(response.body).to match /London is the only office/

    it 'reports multiple offices' do
      request = JSON.parse('spec/fixtures/list_offices.json'))
      Office.create [{name: 'London'}, {name: 'Tel Aviv'}]
      post :root, params: request, format: :json
      expect(response.body).to match /Our offices are in London, and last but not least is the office in Tel Aviv./

Audio tests

I was keen on finding a way to simulate what would otherwise be an end-to-end user-acceptance test, like a Selenium session for a web-based app.

The audio test I came up with has the following flow:

describe 'audio tests', :audio do
  it 'responds to ListOffice intent' do
    london = 'Paris'
    aviv = 'Tel Aviv'

    Office.create [{ name: london }, { name: aviv }]

    pid = play_audio 'spec/fixtures/list-office.m4a'

    client, data = start_server

    post :root, params: JSON.parse(data), format: :json
    result = (response.body =~ /(?=#{london})(?=.*#{aviv})/) > 0

    reply client, 'The list offices intent test ' + (result ? 'passed' : 'failed')
    expect(result).to be true


Line 6: Creates some offices.
Line 8: Plays an audio file that asks Alexa to list the offices
Line 10: Starts an HTTP server listening on port 80\. Make sure that rails is not running, but keep ngrok up to direct traffic to the test.
Line 12: Will direct the intent request from Alexa to the controller
Line 13: Makes sure that both office names are present in the response
Line 15: Replaces the response that would have been sent back to Alexa with a curt message about the test passing or not.
Line 16: Relays the test status back to RSpec for auditing.

This is as close as I got to an end-to-end test (audio and controller). Please let me know if you have other ways of achieving the same!


What was technically done here?

  • We registered an Alexa skill
  • We have a mechanism to direct traffic to our server
  • We have a mechanism to unit-test, integration-test and acceptance-test our skill
  • We have a mechanism that allows for a fast development cycle, running the skill locally till we’re ready to deploy it publicly.

My main learning, however, was not a technical one (despite my thinking that the audio test is nifty!). Being an advocate for TDD and BDD, I realise that now there’s a new way of thinking about intents, whether the app is a voice-enabled one or not.

We may call it CDD, being Conversation Driven Development.

The classic “As a..”, “I want to…”, “So that…” manner of describing intent seems so static compared to imagining a conversation with your product, whether it’s voice-enabled or not. In our case, try to imagine what a conversation with an office application would be like?

“Alexa, walk me through onboarding”. Through booking time, booking conference rooms, asking where office-mates are, what everyone is working on etc.

If the app happens to be a voice-enabled one, just make audio recordings of the prompts, and employ TDD using them. If it’s a classic app, use those conversations to create BDD scripts to help you implement the intents.