Adding skills to your assistant

Actions skill

An actions skill contains actions that represent the tasks you want your assistant to help your customers with.

Each action contains a series of steps that represent individual exchanges with a customer. Building the conversation that your assistant has with your customers is fundamentally about deciding which steps, or which user interactions, are required to complete an action. After you identify the list of steps, you can then focus on writing engaging content to turn each interaction into a positive experience for your customer.

Dialog skill

A dialog skill contains the training data and logic that enables an assistant to help your customers. It contains the following types of artifacts:

• Intents: An intent represents the purpose of a user's input, such as a question about business locations or a bill payment. You define an intent for each type of user request you want your application to support. The name of an intent is always prefixed with the # character. To train the dialog skill to recognize your intents, you supply lots of examples of user input and indicate which intents they map to.

  A content catalog is provided that contains prebuilt common intents you can add to your application rather than building your own. For example, most applications require a greeting intent that starts a dialog with the user. You can add the General content catalog to add an intent that greets the user and does other useful things, like end the conversation.

• Dialog: A dialog is a branching conversation flow that defines how your application responds when it recognizes the defined intents and entities. You use the dialog editor to create conversations with users, providing responses based on the intents and entities that you recognize in their input.

  

To enable your dialog skill to handle more nuanced questions, define entities and reference them from your dialog.

• Entities; An entity represents a term or object that is relevant to your intents and that provides a specific context for an intent. For example, an entity might represent a city where the user wants to find a business location, or the amount of a bill payment. The name of an entity is always prefixed with the @ character.

  You can train the skill to recognize your entities by providing entity term values and synonyms, entity patterns, or by identifying the context in which an entity is typically used in a sentence. To fine tune your dialog, go back and add nodes that check for entity mentions in user input in addition to intents.

As you add information, the skill uses this unique data to build a machine learning model that can recognize these and similar user inputs. Each time you add or change the training data, the training process is triggered to ensure that the underlying model stays up-to-date as your customer needs and the topics they want to discuss change.

Search skill

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When Watson Assistant doesn't have an explicit solution to a problem, it routes the user question to a search skill to find an answer from across your disparate sources of self-service content. The search skill interacts with the IBM Watson® Discovery service to extract this information from a configured data collection.

If you already use the Discovery service, you can mine your existing data collections for source material that you can share with customers to address their questions.

The following diagram illustrates how user input is processed when both a dialog skill and a search skill are added to an assistant. Any questions that the dialog is not designed to answer are sent to the search skill, which finds a relevant response from a Discovery data collection.

Skill limits

The number of skills you can create depends on your Watson Assistant plan type. Any sample dialog skills that are available for you to use do not count toward your limit unless you use them. A skill version does not count as a skill.

After 30 days of inactivity, an unused skill in a Lite plan service instance might be deleted to free up space.