---
sourceDocument: Brazil Enable AI
sourceDocumentLink: https://www.servicenow.com/docs/r/intelligent-experiences

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    - brazil

ft:locale :

    - en-US

ft:publication_title :

    - Brazil Enable AI

ft:clusterId :

    - platai

bundleId :

    - platai

workflow :

    - Platform


---

# Skill scoping guidelines

# Skill scoping guidelines {#ariaid-title1}

Release version: Brazil  
Updated September 10, 2026  
![](https://www.servicenow.com/docs/portal-asset/ico-clock) 1 minute to read  
Scope a custom skill before you build it to define its requirements and expected outcomes.

## Before you begin {#scoping-the-skill__section_b1c_nph_ccc}

Before you start to build a custom skill with AI Skill Kit, review the base system skills that are available in the AI Admin Hub console. Use these pre-existing skills whenever possible.

## User prerequisites {#scoping-the-skill__section_k1d_5ph_ccc}

AI Skill Kit is intended for developers and people with generative AI experience. Because you must write the initial prompt template, you should:

1. Be knowledgeable about prompt engineering, including:
   * Having familiarity with the development and testing of machine learning systems.
   * Having informed expectations about the behavior and capabilities of a prompted large language model (LLM). You should understand:
     * The fundamentally probabilistic nature of LLMs.
     * That performance can vary greatly across different LLMs and different target tasks.
   * Having experience or training with writing and evaluating prompts for LLMs.
   {#scoping-the-skill__ul_wkb_gpj_ccc}
2. Have an in-depth understanding of the use case that you're trying to solve and the persona that you're building the skill for.
{#scoping-the-skill__ol_j54_bpj_ccc}

## Design {#scoping-the-skill__section_tln_vph_ccc}

Before you begin building a custom skill, you must think about the overall design and document the requirements. Consider the following:

1. What precisely do you want the skill to do?
   * What are the inputs to the model?
   * What should be the outputs of the model? (Both content and format)
   * Who uses the skill?
   * How can you characterize success?
2. What don't you want the model to do?
   * It's useful to think about the possible risks and downsides of using generative AI outputs so that you can try to guard against them during development and testing.
   * It's worth listing any specific model behaviors (LLM outputs) that would be detrimental or harmful in your specific use case.