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Webex Assistant Skills
Full API Reference

Webex Assistant Skills SDK Guide

The Webex Skills SDK is designed to simplify the process of creating a Webex Assistant Skill. It provides some tools that help to easily set up a template skill, deal with encryption and test the skill locally, among other things.

In this document we'll go through some examples of how to use this SDK to create different types of skills, and we'll also show how to use the different tools available.

anchorRequirements
anchor

In order to follow the examples in this guide, we'll need to install the SDK and its dependencies. Right now the SDK works with Python 3.7 and above. Note that if you want to build a MindMeld Skill as shown later in this guide you will have to use Python 3.7, since that is the higher version supported by the MindMeld Library.

Installing the SDK

For this guide, we assume you are familiar and have installed:

We'll start by creating a virtual environment:

pyenv install 3.7.5
pyenv virtualenv 3.7.5 webex-skills
pyenv local webex-skills

We can now install the SDK using pip:

pip install webex-skills

We should be all set, we'll use the SDK later in this guide. You will need to work inside the webex-skills virtual environment we just created.

anchorSimple Skills vs MindMeld Skills
anchor

In a nutshell, a skill is a web service that takes a request containing some text and some context, analyzes that information and responds accordingly. The level of analysis done on that information will depend greatly on the tasks the skills has to accomplish. Some skills will simply need to look for keywords in the text, while others will perform complex NLP in order to understand what the user is requesting.

This SDK has tooling for creating 2 types of skills: Simple Skills and MindMeld Skills. These should serve as templates for basic and complex skills. Let's now take a look at these templates in detail.

Simple Skills

Simple Skills do not perform any type of ML or NLP analysis on the requests. These skills are a good starting point for developers to start tinkering with, and they are usually good enough for performing trivial non-complicated tasks. Most developers would start with a Simple Skill and then migrate to a MindMed Skill if needed.

Most of the time all you need is to recognize a few keywords in the text. Imagine a skill which only task is to turn on and off the lights in the office. Some typical queries would be:

  • "Turn on the lights"
  • "Turn off the lights"
  • "Turn on the lights please"
  • "Turn the lights off"

In this particular case, it will probably be good enough to just look for the words on and off in the text received. If on is present, the skill turns on the lights and responds accordingly and vice versa.

As you can imagine, we don't really need any complex NLP for this skill. A simple regex would be more than enough. Simple Skills do just that: they provide a template where you can specify the regexes you care about and have them map to specific handlers (turn_on_lights and turn_off_lights in our example).

We'll build a simple skill in this section

MindMeld Skills

MindMeld Skills perform NLP analysis on the requests. These skills are a good template for cases where the queries will have a lot of variation and contain a lot of information.

Let's take the case of a skill for ordering food. Queries for a skill like this might look like the following:

  • "Order a pepperoni pizza from Super Pizzas"
  • "Order a pad thai from Thailand Cafe"
  • "I want a hamburger with fries and soda from Hyper Burgers"

As we can see, using regexes for these cases can get out of hand really fast. We would need to be able to recognize every single dish from every single restaurant, which might account for hundreds or thousands of regexes. As we add more dishes and restaurants, updating the codebase becomes a real problem.

For cases like this, we leverage the open source MindMeld Library. This library makes it really easy to perform NLP on any text query and identify entities like dishes, restaurants and quantities. With that, performing the required actions becomes a much easier job.

We'll build a MindMeld skill in this section

anchorBuilding a Simple Skill
anchor

Let's now use the SDK to build a Simple Skill. As in the example above, we'll build a skill to turn lights on and off according to what the user is asking. We are going to call this skill Switch.

Create the Skill Template

In the pyenv environment we created before, run the following command:

webex-skills project init switch

This will create a template for a simple skill. You should see the following file structure:

File Structure

As you can see, the project section init command creates a template of a skill. As usual, you can use the --help option to see the documentation for this command:

$ webex-skills project init --help
Usage: webex-skills project init [OPTIONS] SKILL_NAME

  Create a new skill project from a template

Arguments:
  SKILL_NAME  The name of the skill you want to create  [required]

Options:
  --skill-path DIRECTORY      Directory in which to initialize a skill project
                              [default: .]

  --secret TEXT               A secret for encryption. If not provided, one
                              will be generated automatically.

  --mindmeld / --no-mindmeld  If flag set, a MindMeld app will be created,
                              otherwise it defaults to a simple app  [default:
                              False]

  --help                      Show this message and exit.
Running the Template

We can now run our skill and start testing it. There are a couple ways you can run it.

First this SDK has a run command, you can run it as follows:

webex-skills skills run switch

You should see an output similar to:

INFO:     Started server process [86661]
INFO:     Waiting for application startup.
INFO:     Application startup complete.
INFO:     Uvicorn running on http://127.0.0.1:8080 (Press CTRL+C to quit)

As usual, you can use the --help option to see the documentation for this command:

$ webex-skills skills run --help
Usage: webex-skills skills run [OPTIONS] SKILL_NAME

Arguments:
  SKILL_NAME  The name of the skill to run.  [required]

Options:
  --help  Show this message and exit.

The second option to run a skill is to use uvicorn. After all, the skill created is an asgi application based on FastAPI:

uvicorn switch.main:api --port 8080 --reload

You should see an output similar to the following:

INFO:     Will watch for changes in these directories: ['<PATH_TO_SKILL>']
INFO:     Uvicorn running on http://127.0.0.1:8080 (Press CTRL+C to quit)
INFO:     Started reloader process [86234] using statreload
INFO:     Started server process [86253]
INFO:     Waiting for application startup.
INFO:     Application startup complete.

Now that we have the skill actually run it, we can test it.

Checking the Skill

One quick thing we can do before sending actual requests to the skill is to make sure we have everything correctly setup. The sdk provides a tool for that. We can call it as:

webex-skills skills check switch

In your skill output, you should see something like this:

INFO:     127.0.0.1:58112 - "GET /check?signature=<SIGNATURE%3D&message=<MESSAGE>%3D HTTP/1.1" 200 OK

In the SDK output you should see:

switch appears to be working correctly

That means that your skill is running and the check request was successfully processed.

Invoking the Skill

The SDK skills section has an invoke command which is used for sending requests to the skill. With the skill running, we can invoke it as follows:

webex-skills skills invoke switch

We can now enter a command and see a response:

$ webex-skills skills invoke switch
Enter commands below (Ctl+C to exit)
>> hi
{ 'challenge': 'a129d633075c9c227cc4bdcd1653b063b6dfe613ca50355fa84e852dde4b198f',
  'directives': [ {'name': 'reply', 'payload': {'text': 'Hello I am a super simple skill'}, 'type': 'action'},
                  {'name': 'speak', 'payload': {'text': 'Hello I am a super simple skill'}, 'type': 'action'},
                  {'name': 'sleep', 'payload': {'delay': 10}, 'type': 'action'}],
  'frame': {},
  'history': [ { 'context': {},
                 'directives': [],
                 'frame': {},
                 'history': [],
                 'params': { 'allowed_intents': [],
                             'dynamic_resource': {},
                             'language': 'en',
                             'locale': None,
                             'target_dialogue_state': None,
                             'time_zone': 'UTC',
                             'timestamp': 1634881359},
                 'text': 'hi'}],
  'params': { 'allowed_intents': [],
              'dynamic_resource': {},
              'language': 'en',
              'locale': None,
              'target_dialogue_state': None,
              'time_zone': 'UTC',
              'timestamp': 1634881359}}

We can see that we got all the directives back. The template skill will simply repeat or echo everything we send to it.

As usual, you can use the --help option to see the documentation for this command:

$ webex-skills skills invoke --help
Usage: webex-skills skills invoke [OPTIONS] [NAME]

  Invoke a skill running locally or remotely

Arguments:
  [NAME]  The name of the skill to invoke. If none specified, you would need
          to at least provide the `public_key_path` and `secret`. If
          specified, all following configuration (keys, secret, url, ect.)
          will be extracted from the skill.


Options:
  -s, --secret TEXT         The secret for the skill. If none provided you
                            will be asked for it.

  -k, --key PATH            The path of the public key for the skill.
  -u TEXT                   The public url for the skill.
  -v                        Set this flag to get a more verbose output.
  --encrypt / --no-encrypt  Flag to specify if the skill is using encryption.
                            [default: True]

  --help                    Show this message and exit.
Updating the Skill

Let's now modify our skill so it does what we want: remember we want this skill to turn on and off the office lights.

Simply update the main.py file with the following 2 handlers:

@api.handle(pattern=r'.*\son\s?.*')
async def turn_on(current_state: DialogueState) -> DialogueState:
    new_state = current_state.copy()

    text = 'Ok, turning lights on.'

      # Call lights API to turn on your light here.

    new_state.directives = [
        responses.Reply(text),
        responses.Speak(text),
        responses.Sleep(10),
    ]

    return new_state


@api.handle(pattern=r'.*\soff\s?.*')
async def turn_off(current_state: DialogueState) -> DialogueState:
    new_state = current_state.copy()

    text = 'Ok, turning lights off.'

    # Call lights API to turn off your light here.

    new_state.directives = [
        responses.Reply(text),
        responses.Speak(text),
        responses.Sleep(10),
    ]

    return new_state

The SDK provides the @api.handle decorator, and as you can see it can take a pattern parameter which is then applied to the query to determine if the handler applies to the query being processed. This way, we can add a few handlers by simple creating regexes that match the type of queries we want to support.

In the example above, we have added regexes to identify the on and off keywords. Which mostly tell what the user wants to do.

By using the skill invoke command we can run a few tests:

>> turn on the lights
{ 'challenge': '56094568e18c66cb89eca8eb092cc3bbddcd64b4c0442300cfbe9af67183e260',
  'directives': [ {'name': 'reply', 'payload': {'text': 'Ok, turning lights on.'}, 'type': 'action'},
                  {'name': 'speak', 'payload': {'text': 'Ok, turning lights on.'}, 'type': 'action'},
                  {'name': 'sleep', 'payload': {'delay': 10}, 'type': 'action'}],
  'frame': {},
  'history': [ { 'context': {},
                 'directives': [],
                 'frame': {},
                 'history': [],
                 'params': { 'allowed_intents': [],
                             'dynamic_resource': {},
                             'language': 'en',
                             'locale': None,
                             'target_dialogue_state': None,
                             'time_zone': 'UTC',
                             'timestamp': 1634881502},
                 'text': 'turn on the lights'}],
  'params': { 'allowed_intents': [],
              'dynamic_resource': {},
              'language': 'en',
              'locale': None,
              'target_dialogue_state': None,
              'time_zone': 'UTC',
              'timestamp': 1634881502}}
>> turn off the lights
{ 'challenge': '2587110a9c97ebf9ce435412c0ea6154eaef80f384ac829cbdc679db483e5beb',
  'directives': [ {'name': 'reply', 'payload': {'text': 'Ok, turning lights off.'}, 'type': 'action'},
                  {'name': 'speak', 'payload': {'text': 'Ok, turning lights off.'}, 'type': 'action'},
                  {'name': 'sleep', 'payload': {'delay': 10}, 'type': 'action'}],
  'frame': {},
  'history': [ { 'context': {},
                 'directives': [],
                 'frame': {},
                 'history': [],
                 'params': { 'allowed_intents': [],
                             'dynamic_resource': {},
                             'language': 'en',
                             'locale': None,
                             'target_dialogue_state': None,
                             'time_zone': 'UTC',
                             'timestamp': 1634881502},
                 'text': 'turn on the lights'},
               { 'context': {},
                 'directives': [],
                 'frame': {},
                 'history': [],
                 'params': { 'allowed_intents': [],
                             'dynamic_resource': {},
                             'language': 'en',
                             'locale': None,
                             'target_dialogue_state': None,
                             'time_zone': 'UTC',
                             'timestamp': 1634881502},
                 'text': 'turn off the lights'}],
  'params': { 'allowed_intents': [],
              'dynamic_resource': {},
              'language': 'en',
              'locale': None,
              'target_dialogue_state': None,
              'time_zone': 'UTC',
              'timestamp': 1634881502}}
>> turn the lights on
{ 'challenge': 'ce24a510f6a7025ac5e4cc51b082483bdbcda31836e2c3567780c231e5674c59',
  'directives': [ {'name': 'reply', 'payload': {'text': 'Ok, turning lights on.'}, 'type': 'action'},
                  {'name': 'speak', 'payload': {'text': 'Ok, turning lights on.'}, 'type': 'action'},
                  {'name': 'sleep', 'payload': {'delay': 10}, 'type': 'action'}],
  'frame': {},
  'history': [ { 'context': {},
                 'directives': [],
                 'frame': {},
                 'history': [],
                 'params': { 'allowed_intents': [],
                             'dynamic_resource': {},
                             'language': 'en',
                             'locale': None,
                             'target_dialogue_state': None,
                             'time_zone': 'UTC',
                             'timestamp': 1634881502},
                 'text': 'turn on the lights'},
               { 'context': {},
                 'directives': [],
                 'frame': {},
                 'history': [],
                 'params': { 'allowed_intents': [],
                             'dynamic_resource': {},
                             'language': 'en',
                             'locale': None,
                             'target_dialogue_state': None,
                             'time_zone': 'UTC',
                             'timestamp': 1634881502},
                 'text': 'turn off the lights'},
               { 'context': {},
                 'directives': [],
                 'frame': {},
                 'history': [],
                 'params': { 'allowed_intents': [],
                             'dynamic_resource': {},
                             'language': 'en',
                             'locale': None,
                             'target_dialogue_state': None,
                             'time_zone': 'UTC',
                             'timestamp': 1634881502},
                 'text': 'turn the lights on'}],
  'params': { 'allowed_intents': [],
              'dynamic_resource': {},
              'language': 'en',
              'locale': None,
              'target_dialogue_state': None,
              'time_zone': 'UTC',
              'timestamp': 1634881502}}

In the examples above, we can see the skill responding with the correct message. In a real skill, we would also call an API to actually perform the action the user wants.

More About the Simple Skill Handler Signature

When we created our Simple Skill, our handler had the following siganture:

async def greet(current_state: DialogueState) -> DialogueState:

You can also update the handler to give you another parameter:

async def greet(current_state: DialogueState, query: str) -> DialogueState:

The query string parameter will give you the query that was sent to the skill, this can be used in cases where you need to further analyze the text.

anchorBuilding a MindMeld Skill
anchor

When a skill is very complex and needs to handle many commands, usually the best approach is to create a MindMeld based skill. This will allow us to use NLP to better classify the request and extract important information we need from it. We are not going to go very deep into how a MindMeld application works, but there are a lot of resources in the official MindMeld library site.

This SDK also has tooling in place for setting up a MindMeld based skill. For that, we can use the project init command with the --mindmeld flag set. But first, we need to add the extra dependency mindmeld:

pip install "webex-skills[mindmeld]"

Let's create a skill called greeter:

webex-skills project init greeter --mindmeld

The folder structure should look like this:

File Structure

Invoking the MindMeld Skill

With the greeter skill running, let's try invoking it using the SDK skills invoke command:

$ webex-skills skills invoke greeter

Enter commands below (Ctl+C to exit)
>> hi
{ 'challenge': 'a31ced06481293abd8cbbcffe72d712e996cf0ddfb56d981cd1ff9c1d9a46bfd',
  'directives': [ {'name': 'reply', 'payload': {'text': 'Hello I am a super simple skill using NLP'}, 'type': 'action'},
                  {'name': 'speak', 'payload': {'text': 'Hello I am a super simple skill using NLP'}, 'type': 'action'},
                  {'name': 'sleep', 'payload': {'delay': 10}, 'type': 'action'}],
  'frame': {},
  'history': [ { 'context': {},
                 'directives': [],
                 'frame': {},
                 'history': [],
                 'params': { 'allowed_intents': [],
                             'dynamic_resource': {},
                             'language': 'en',
                             'locale': None,
                             'target_dialogue_state': None,
                             'time_zone': 'UTC',
                             'timestamp': 1634880182},
                 'text': 'hi'}],
  'params': { 'allowed_intents': [],
              'dynamic_resource': {},
              'language': 'en',
              'locale': None,
              'target_dialogue_state': None,
              'time_zone': 'UTC',
              'timestamp': 1634880182}}

As you can see, the response of a MindMeld Skill has the same shape as a Simple Skill, it's really just the internals of the skill that change.

Building New Models

Since MindMeld Skills use NLP, we need to retrain the ML models each time we modify the training data. The SDK provides a the nlp build command for this purpose:

webex-skills nlp build greeter

After running this, the models will be refreshed with the latest training data.

As usual, you can use the --help option to see the documentation for this command:

$ webex-skills nlp build --help
Usage: webex-skills nlp build [OPTIONS] [NAME]

  Build nlp models associated with this skill

Arguments:
  [NAME]  The name of the skill to build.

Options:
  --help  Show this message and exit.
Testing the Models

Anothoer useful command in this SDK is the nlp process command. It's similar to the skill invoke command, in the sense that it will send a query to the running skill. However, the query will only be run through the NLP pipeline so we can see how it was categorized. Let's look at an example:

$ webex-skills nlp process greeter

Enter a query below (Ctl+C to exit)
>> hi
{'domain': 'greeting', 'entities': [], 'intent': 'greet', 'text': 'hi'}
>>

You can see that now the response only contains the extracted ANLP pieces. This command is very useful for testing your models as you work on improving them.

As usual, you can use the --help option to see the documentation for this command:

$ webex-skills nlp process --help
Usage: webex-skills nlp process [OPTIONS] [NAME]

  Run a query through NLP processing

Arguments:
  [NAME]  The name of the skill to send the query to.

Options:
  --help  Show this message and exit.
More About the MindMeld Skill Handler Signature

When we created our Simple Skill, our handler had the following siganture:

async def greet(current_state: DialogueState) -> DialogueState:

You can also update the handler to give you another parameter:

async def greet(current_state: DialogueState, , processed_query: ProcessedQuery) -> DialogueState:

The processed_query parameter will give you the text that was sent to the skill, the domain and intent identified as well as the entities extracted from the query. This can be useful in cases where you want to use the entities as part of the skill logic. We'll show an example of this in the Converting a Simple Skill into a MindMeld Skill section.

anchorConverting a Simple Skill into a MindMeld Skill
anchor

In many cases, when you start creating a new skill, you would probably start with a Simple Skill. As your skill grows in complexity, you might need to convert it into a Mindmeld Skill. This SDK makes that conversion very easy, we are going to take a look into that next.

Consider the Switch Skill we built above. It really just recognizes if we want to turn something on or off. But what if we have multiple lights? We could in principle look at the query and try to extract the light we want to switch with another regex, but that can be very brittle. Instead, let's try turning it into a MindMeld Skill.

Adding the Training Data

Let's start by creating our domains, intents and entities. We still want to just turn on and off lights, but we want to be able to identify which light to switch. On the switch app, create the following folder structure:

File Structure

As you can see, we have created a greeting and smart_lights domains, the intents greet, turn_lights_on and turn_lights_off, and the entities all and location. We now need to add training data to make this setup work.

Normally, you would need to collect training data manually to create your domain, intents and entities. But, for this guide, we are going to take a shortcut. If you are familiar with the MindMeld library, you have probably seen that it comes with Blueprint applications, so we are going to borrow some data from one of them.

Go to this repo and copy the train.txt files from the corresponding intents into our folders. Do the same for entities, from here copy the corresponding gazetteer.txt and mapping.json files into our folders. Our directory should now look like this:

File Structure

Updating the Handlers

The next thing we'll do is to convert our logic to turn it into a MindMeld Skill. The steps are very simple, let's start. We'll make all the following changes in main.py:

Instead of making the variable app a SimpleApi, make it a MindMeldAPI:

api = MindmeldAPI()

In the @api.handle decorators, add the intent you want to handle instead of the pattern:

@api.handle(intent='turn_lights_on')
async def turn_on(current_state: DialogueState, processed_query: ProcessedQuery) -> DialogueState:
...

@api.handle(intent='turn_lights_off')
async def turn_off(current_state: DialogueState, processed_query: ProcessedQuery) -> DialogueState:

Finally, add some logic to complement the response with the location entity if available. In the turn_on handler, replace the line:

    text = 'Ok, turning lights on.'

With the following logic:

    if len(processed_query.entities) > 0:
        entity = processed_query.entities[0]
        if entity['type'] == 'location':
            text = f'Ok, turning the {entity["text"]} lights on.'
        else:
            text = 'Ok, turning all lights on.'
    else:
        text = 'Ok, turning all lights on.'

Do the corresponding change to the turn_off handler.

You will also need to import the new classes you are using: MindmeldAPI and ProcessedQuery.

That's it! We now have NLP support in our skill.

All in all your main.py should look like this:

from webex_skills.api import MindmeldAPI
from webex_skills.dialogue import responses
from webex_skills.models.mindmeld import DialogueState, ProcessedQuery

api = MindmeldAPI()


@api.handle(default=True)
async def greet(current_state: DialogueState) -> DialogueState:
    text = 'Hello I am a super simple skill'
    new_state = current_state.copy()

    new_state.directives = [
        responses.Reply(text),
        responses.Speak(text),
        responses.Sleep(10),
    ]

    return new_state


@api.handle(intent='turn_lights_on')
async def turn_on(current_state: DialogueState, processed_query: ProcessedQuery) -> DialogueState:
    new_state = current_state.copy()

    if len(processed_query.entities) > 0:
        entity = processed_query.entities[0]
        if entity['type'] == 'location':
            text = f'Ok, turning the {entity["text"]} lights on.'
        else:
            text = 'Ok, turning all lights on.'
    else:
        text = 'Ok, turning all lights on.'

    # Call lights API to turn on your light here.

    new_state.directives = [
        responses.Reply(text),
        responses.Speak(text),
        responses.Sleep(10),
    ]

    return new_state


@api.handle(intent='turn_lights_off')
async def turn_off(current_state: DialogueState, processed_query: ProcessedQuery) -> DialogueState:
    new_state = current_state.copy()

    if len(processed_query.entities) > 0:
        entity = processed_query.entities[0]
        if entity['type'] == 'location':
            text = f'Ok, turning the {entity["text"]} lights off.'
        else:
            text = 'Ok, turning all lights off.'
    else:
        text = 'Ok, turning all lights off.'

    # Call lights API to turn off your light here.

    new_state.directives = [
        responses.Reply(text),
        responses.Speak(text),
        responses.Sleep(10),
    ]

    return new_state
Adding the Parsers Configuration File

One last step we need is to add a config file needed by some parsers. This is added automatically when directly creating a MindMeld Skill, so we'll need to add it manually in this case.

In the switch module directory add a config.py file with the following content:

NLP_CONFIG = {'system_entity_recognizer': {}}
ENTITY_RESOLVER_CONFIG = {'model_type': 'exact_match'}
Testing the Skill

We can now test the skill to make sure it works as intended. Since we just added our training data, we need to build the models first. Since we have entities defined, we will need to have Elasticsearch running for the nlp build command to work properly. You can refer to the MindMeld Getting Started Documentation for a guide on how to install and run Elasticsearch.

Remember that in order to build MindMeld skills we need the extra mindmeld dependency:

pip install "webex-skills[mindmeld]"

With that out of the way, you can build the models:

webex-skills nlp build switch

We can now run our new skill:

webex-skills skills run switch

Finally, we can use the invoke method to send a couple commands:

$ webex-skills skills invoke switch
Enter commands below (Ctl+C to exit)
>> hi
{ 'challenge': 'bd57973f82227c37fdaed9404f86be521ecdbefc684e15319f0d51bbecbb456e',
  'directives': [ {'name': 'reply', 'payload': {'text': 'Hello I am a super simple skill'}, 'type': 'action'},
                  {'name': 'speak', 'payload': {'text': 'Hello I am a super simple skill'}, 'type': 'action'},
                  {'name': 'sleep', 'payload': {'delay': 10}, 'type': 'action'}],
  'frame': {},
  'history': [ { 'context': {},
                 'directives': [],
                 'frame': {},
                 'history': [],
                 'params': { 'allowed_intents': [],
                             'dynamic_resource': {},
                             'language': 'en',
                             'locale': None,
                             'target_dialogue_state': None,
                             'time_zone': 'UTC',
                             'timestamp': 1634934720},
                 'text': 'hi'}],
  'params': { 'allowed_intents': [],
              'dynamic_resource': {},
              'language': 'en',
              'locale': None,
              'target_dialogue_state': None,
              'time_zone': 'UTC',
              'timestamp': 1634934720}}
>> turn on the lights
{ 'challenge': 'ff8e57bcb94b90c736e11dd79ae5fe3b269dcc450d7bb07b083de73d9a22d5e8',
  'directives': [ {'name': 'reply', 'payload': {'text': 'Ok, turning all lights on.'}, 'type': 'action'},
                  {'name': 'speak', 'payload': {'text': 'Ok, turning all lights on.'}, 'type': 'action'},
                  {'name': 'sleep', 'payload': {'delay': 10}, 'type': 'action'}],
  'frame': {},
  'history': [ { 'context': {},
                 'directives': [],
                 'frame': {},
                 'history': [],
                 'params': { 'allowed_intents': [],
                             'dynamic_resource': {},
                             'language': 'en',
                             'locale': None,
                             'target_dialogue_state': None,
                             'time_zone': 'UTC',
                             'timestamp': 1634934720},
                 'text': 'hi'},
               { 'context': {},
                 'directives': [],
                 'frame': {},
                 'history': [],
                 'params': { 'allowed_intents': [],
                             'dynamic_resource': {},
                             'language': 'en',
                             'locale': None,
                             'target_dialogue_state': None,
                             'time_zone': 'UTC',
                             'timestamp': 1634934720},
                 'text': 'turn on the lights'}],
  'params': { 'allowed_intents': [],
              'dynamic_resource': {},
              'language': 'en',
              'locale': None,
              'target_dialogue_state': None,
              'time_zone': 'UTC',
              'timestamp': 1634934720}}
>> turn off the kitchen lights
{ 'challenge': '300e7adfe2f199998f152793b944bc597c5145e991dda621e1495e2e06cebb6e',
  'directives': [ {'name': 'reply', 'payload': {'text': 'Ok, turning the kitchen lights off.'}, 'type': 'action'},
                  {'name': 'speak', 'payload': {'text': 'Ok, turning the kitchen lights off.'}, 'type': 'action'},
                  {'name': 'sleep', 'payload': {'delay': 10}, 'type': 'action'}],
  'frame': {},
  'history': [ { 'context': {},
                 'directives': [],
                 'frame': {},
                 'history': [],
                 'params': { 'allowed_intents': [],
                             'dynamic_resource': {},
                             'language': 'en',
                             'locale': None,
                             'target_dialogue_state': None,
                             'time_zone': 'UTC',
                             'timestamp': 1634934720},
                 'text': 'hi'},
               { 'context': {},
                 'directives': [],
                 'frame': {},
                 'history': [],
                 'params': { 'allowed_intents': [],
                             'dynamic_resource': {},
                             'language': 'en',
                             'locale': None,
                             'target_dialogue_state': None,
                             'time_zone': 'UTC',
                             'timestamp': 1634934720},
                 'text': 'turn on the lights'},
               { 'context': {},
                 'directives': [],
                 'frame': {},
                 'history': [],
                 'params': { 'allowed_intents': [],
                             'dynamic_resource': {},
                             'language': 'en',
                             'locale': None,
                             'target_dialogue_state': None,
                             'time_zone': 'UTC',
                             'timestamp': 1634934720},
                 'text': 'turn off the kitchen lights'}],
  'params': { 'allowed_intents': [],
              'dynamic_resource': {},
              'language': 'en',
              'locale': None,
              'target_dialogue_state': None,
              'time_zone': 'UTC',
              'timestamp': 1634934720}}
>> turn off all the lights
{ 'challenge': 'e92a90c304c9ef96a3edf31c6ffb10606739cdf98ad34cfd57314b20138ad59b',
  'directives': [ {'name': 'reply', 'payload': {'text': 'Ok, turning all lights off.'}, 'type': 'action'},
                  {'name': 'speak', 'payload': {'text': 'Ok, turning all lights off.'}, 'type': 'action'},
                  {'name': 'sleep', 'payload': {'delay': 10}, 'type': 'action'}],
  'frame': {},
  'history': [ { 'context': {},
                 'directives': [],
                 'frame': {},
                 'history': [],
                 'params': { 'allowed_intents': [],
                             'dynamic_resource': {},
                             'language': 'en',
                             'locale': None,
                             'target_dialogue_state': None,
                             'time_zone': 'UTC',
                             'timestamp': 1634934720},
                 'text': 'hi'},
               { 'context': {},
                 'directives': [],
                 'frame': {},
                 'history': [],
                 'params': { 'allowed_intents': [],
                             'dynamic_resource': {},
                             'language': 'en',
                             'locale': None,
                             'target_dialogue_state': None,
                             'time_zone': 'UTC',
                             'timestamp': 1634934720},
                 'text': 'turn on the lights'},
               { 'context': {},
                 'directives': [],
                 'frame': {},
                 'history': [],
                 'params': { 'allowed_intents': [],
                             'dynamic_resource': {},
                             'language': 'en',
                             'locale': None,
                             'target_dialogue_state': None,
                             'time_zone': 'UTC',
                             'timestamp': 1634934720},
                 'text': 'turn off the kitchen lights'},
               { 'context': {},
                 'directives': [],
                 'frame': {},
                 'history': [],
                 'params': { 'allowed_intents': [],
                             'dynamic_resource': {},
                             'language': 'en',
                             'locale': None,
                             'target_dialogue_state': None,
                             'time_zone': 'UTC',
                             'timestamp': 1634934720},
                 'text': 'turn off all the lights'}],
  'params': { 'allowed_intents': [],
              'dynamic_resource': {},
              'language': 'en',
              'locale': None,
              'target_dialogue_state': None,
              'time_zone': 'UTC',
              'timestamp': 1634934720}}
>>

We have now converted a Simple Skill into a MindMeld Skill.

anchorEncryption
anchor

Skills require encryption in order to safely send and receive requests. For the encryption to work properly, we need to provide a key pair and a secret for our skills. As we saw in the Simple Skill and MindMeld Skill examples above, the keys and secret will be automatically created for us when we use the SDK to create a template. However, the SDK also has tools to create these manually if we need to.

These tools are under the crypto section which we'll try next.

Generating Secrets

Generating a secret is very simple, simply use the generate-secret command:

webex-skills crypto generate-secret

A secret will be logged to the terminal, which then you can add to your app.

Generating Keys

Generating a key pair is very simple, simply use the generate-keys command:

webex-skills crypto generate-keys

A key pair will be created.

As usual, you can use the --help option to see the documentation for this command:

$ webex-skills crypto generate-keys --help
Usage: webex-skills crypto generate-keys [OPTIONS] [FILEPATH]

  Generate an RSA keypair

Arguments:
  [FILEPATH]  The path where to save the keys created. By default, they get
              created in the current directory.


Options:
  --name TEXT  The name to use for the keys created.  [default: id_rsa]
  --help       Show this message and exit.
anchorRemotes
anchor

This SDK also has the notion of remotes. That is, skills that are already running and even deployed somewhere, but we still want to be able to test them using the SDK.

The process for using remotes is very simple, we're going to look into that now. We'll use the remote section of the SDK.

Remotes are automatically added for skills created with this SDK.

Creating a Remote

You can create a remote by using the create command. Let's recreate a remote for a skill called echo:

webex-skills remote create echo

Follow the prompts to create a new remote:

$ webex-skills remote create echo

Secret: <SECRET>
Public key path [id_rsa.pub]: <KEY_PATH>
URL to invoke the skill [http://localhost:8080/parse]: <URL>

As usual, you can use the --help option to see the documentation for this command:

$ webex-skills remote create --help

Usage: webex-skills remote create [OPTIONS] NAME

  Add configuration for a new remote skill to the cli config file

Arguments:
  NAME  The name to give to the remote.  [required]

Options:
  -u TEXT            URL of the remote. If not provided it will be requested.
  -s, --secret TEXT  The skill secret. If not provided it will be requested.
  -k, --key PATH     The path to the public key. If not provided it will be
                     requested.

  --help             Show this message and exit.
Listing Remotes

You can also list the remotes you currently have set up. For that you can use the list command:

webex-skills remote list

You will get something like:

{'echo': {'name': 'echo',
          'public_key_path': '<KEY_PATH>',
          'secret': '<SECRET>',
          'url': 'http://localhost:8080/parse'},
 'greeter': {'app_dir': '<APP_DIR>>',
             'name': 'greeter',
             'private_key_path': '<KEY_PATH>',
             'project_path': '<PROJECT_PATH>',
             'public_key_path': '<KEY_PATH>',
             'secret': '<SECRET>',
             'url': 'http://localhost:8080/parse'},
 'switch': {'app_dir': '<APP_DIR>>',
            'name': 'switch',
            'private_key_path': '<KEY_PATH>',
            'project_path': '<PROJECT_PATH>',
            'public_key_path': '<KEY_PATH>',
            'secret': '<SECRET>',
            'url': 'http://localhost:8080/parse'}}

As usual, you can use the --help option to see the documentation for this command:

$ run webex-skills remote list --help

Usage: webex-skills remote list [OPTIONS]

  List configured remote skills

Options:
  --name TEXT  The name of a particular skill to display.
  --help       Show this message and exit.
anchorFurther Development and Deployment of your Skill
anchor

You might have noticed that when you create a new skill, there is a pyproject.toml file that gets added to the project. This file already contains the dependencies needed to run the skill independently (without using the SDK's skills run command.)

In order to run the skill independently, you can use Poetry to manage your dependencies, or you can also replace the pyproject.toml with a requirements.txt file if you want to use pip instead.

For deployment, the SDK builds the skill based on FastAPI, which offers multiple options for running an app in production. You can find more information in their Deployment Documentation.