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Use Case Specification

AWS LabsOfficialPublished by AWS Labs

  • Skill
  • Content generator
  • Software development
  • Apache-2.0
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From the author

Creates a reusable use case specification file that defines the business problem, stakeholders, and measurable success criteria for model customization, as recommended by the AWS Responsible AI Lens. Use as the default first step in any model customization plan. Skip only if the user explicitly declines or already has a use case specification to reuse. Captures problem statement, primary users, and LLM-as-a-Judge success tenets.
— the description by AWS Labs

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

This skill guides users through a structured discovery process to define the core requirements for model customization. By gathering information on the business problem, primary users, and measurable success criteria, it produces a standardized Markdown specification file. It is designed to be the initial step in a model customization workflow, ensuring that projects are aligned with responsible AI practices from the outset.

What it does

  • Checks for existing use case specifications to avoid redundant work.
  • Conducts a multi-turn discovery process to identify business problems and stakeholders.
  • Defines measurable success criteria suitable for LLM-as-a-Judge evaluation.
  • Synthesizes gathered information into a structured Markdown document.
  • Supports iterative updates to the specification based on user feedback.
  • Aligns project documentation with AWS Responsible AI Lens design principles.

When to use it

  • At the very beginning of any model customization project.
  • When you need to formalize project goals and success metrics.
  • When you want to ensure your model customization plan follows responsible AI guidelines.
  • When you need to establish clear evaluation criteria for model performance.

Advantages

  • Ensures project alignment with industry-standard responsible AI practices.
  • Reduces ambiguity by forcing clear definitions of success.
  • Provides a reusable and editable artifact for project stakeholders.
  • Minimizes user effort through intelligent inference of project details.

Usage examples

“I want to fine-tune a model to help my customer support team answer technical questions.”
The agent will initiate the discovery process, asking targeted questions to define the specific technical support problem, the user context, and the metrics for success.
“Can you update the success criteria to focus more on tone adherence?”
The agent will modify the existing use case specification document to reflect the new focus on tone and request confirmation of the updated criteria.

Install

  1. Download the skill as a ZIP:

    https://autogineer.com/agent-skills/awslabs/use-case-specification/download
  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip use-case-specification.zip -d .agents/skills

Documentation by AWS Labs

Use Case Specification

Multi-turn conversation to gather use case details and produce a use case specification document.

Principles

  1. One thing at a time. Each response advances exactly one decision or collects one piece of information.

  2. Confirm before proceeding. Wait for the user to approve the spec before considering this skill complete.

  3. Infer, don't interrogate. Use what's already known from the conversation. Only ask when you truly can't infer.

  4. Do NOT ask about base model selection. Model selection is handled exclusively by the model-selection skill.

Workflow

Step 0: Check for Existing Spec

Before starting discovery, check if a *_use_case_spec.md file already exists in the project. If it does, present it to the user and ask whether they want to reuse it, modify it, or start fresh.

Phase 1: Discovery (1–3 turns)

Review what is already known from the conversation so far, then identify what is still missing. You need these three things:

  • What is the problem the user is trying to solve with model customization

  • Who will use the finetuned model and in what context

  • Which success criteria can be used to evaluate how well the custom model performs compared to the base model on a test set. Success criteria must be measurable by an LLM-as-a-Judge (e.g., response accuracy, tone adherence) — not things like latency or throughput.

Guidelines:

  • Infer as much as possible from what the user has already said

  • If the user gave examples, use them to fill gaps rather than asking again

  • Only ask clarifying questions when you cannot infer the information needed for Phase 2

  • If everything is already clear, say "You've given me a clear picture. I'll put together a use case specification now." and move to Phase 2.

⏸ Wait for user after each clarifying question.

Phase 2: Producing a Use Case Specification Document
  1. Save all generated artifacts under the project directory structure defined by the directory-management skill, if available.

  2. Synthesize the information you collected from the user into a Markdown document called [relevant_title]_use_case_spec.md containing the following fields (and only these fields):

Use case description
  - Concise problem statement + what the custom model will do
  - Field name: “Business Problem”
  - Type: String

Key stakeholders
  - Who uses the model and in what context
  - Field name: “Primary Users”
  - Type: String, comma separated if there are multiple 

Success criteria
  - A list of 3 criteria (a short name and a description) with which the user measure the success of the custom model. 
  - Field name: “Success Tenets”
  - Type: list of name-description pairs
  1. Present the use case specification in a human-readable format as follows:

I have put together a use case specification and saved it in [relevant_title]_use_case_spec.md.

A use case specification is a design principle recommended by the AWS Responsible AI Lens.

[use case in human-readable format]

Does this match your intent?

⏸ Wait for user approval.

use_case_specification Edit Protocol

  • If the user requests changes pertaining to any information covered by use_case_spec.md, you must edit it accordingly and ask for confirmation again.

  • The user can edit use_case_spec.md directly if they want to. If the user says they've updated the file directly, read it to get the latest in your context.

Files

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Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Security

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Attribution and licence

Published by
AWS Labs
Publisher
AWS Labs (Official)
Source
awslabs/agent-plugins · plugins/sagemaker-ai/skills/use-case-specification · commit fbefb37
Licence
Apache License 2.0 (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE)
  • NOTICE (added by AutoGineer from NOTICE)
  • ATTRIBUTION.md (written by AutoGineer)
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Download ZIP

The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download use-case-specification.zip