From sagemaker-ai
Creates a reusable use case specification file defining business problem, stakeholders, and measurable success criteria for model customization, following AWS Responsible AI Lens. Default first step in any model customization plan.
How this skill is triggered — by the user, by Claude, or both
Slash command
/sagemaker-ai:use-case-specificationThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
Multi-turn conversation to gather use case details and produce a use case specification document.
Multi-turn conversation to gather use case details and produce a use case specification document.
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.
Review what is already known from the conversation so far, then identify what is still missing. You need these three things:
Guidelines:
⏸ Wait for user after each clarifying question.
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
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.
npx claudepluginhub awslabs/agent-plugins --plugin sagemaker-aiDiscovers user intent and generates structured plans for SageMaker AI model customization workflows: fine-tuning, data preparation, evaluation, deployment. Handles plan iteration and mid-execution alterations.
Refines product specs through iterative LLM debates (OpenAI, Anthropic, Gemini, etc.) until consensus, with Claude participating. Use for adversarial spec writing or refinement.
Authors structured specifications (NLSpec) via multi-AI research and consensus gathering. Useful for complex requirement definition.