---
title: Skill scoping guidelines
description: Scope a custom skill before you build it to define its requirements and expected outcomes.
locale: en-US
canonical_url: https://www.servicenow.com/docs/r/australia/intelligent-experiences/now-assist-skill-kit/scoping-the-skill.html
release: australia
product: Now Assist Skill Kit
classification: now-assist-skill-kit
topic_type: reference
last_updated: "2026-09-10"
reading_time_minutes: 1
breadcrumb: [General guidelines for AI Skill Kit, Exploring AI Skill Kit, AI Skill Kit, Enable AI experiences]
---

# Skill scoping guidelines

Scope a custom skill before you build it to define its requirements and expected outcomes.

## Before you begin

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

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.
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.

## Design

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.