Official agent skill

Use Case Specification

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.

OfficialApache-2.0Auto-check passedAI & LLM Engineering

Install Use Case Specification

skills CLI

$ npx skills add awslabs/agent-plugins --skill use-case-specification -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI

$ gh skill install awslabs/agent-plugins use-case-specification --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy

$ git clone --depth 1 https://github.com/awslabs/agent-plugins.git skills-src && mkdir -p .claude/skills && cp -r skills-src/plugins/sagemaker-ai/skills/use-case-specification .claude/skills/use-case-specification && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Paste into Claude Code

Install the "use-case-specification" agent skill from https://github.com/awslabs/agent-plugins/tree/main/plugins/sagemaker-ai/skills/use-case-specification into .claude/skills/use-case-specification/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "use-case-specification", then confirm the skill loads.

Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

$skill-installer

$skill-installer install https://github.com/awslabs/agent-plugins/tree/main/plugins/sagemaker-ai/skills/use-case-specification

Type this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.

skills CLI

$ npx skills add awslabs/agent-plugins --skill use-case-specification -a codex

Project install goes to .agents/skills/; add -g for ~/.codex/skills/.

GitHub CLI

$ gh skill install awslabs/agent-plugins use-case-specification --agent codex

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/awslabs/agent-plugins.git skills-src && mkdir -p .agents/skills && cp -r skills-src/plugins/sagemaker-ai/skills/use-case-specification .agents/skills/use-case-specification && rm -rf skills-src

Use ~/.agents/skills/ instead of .agents/skills for a personal install.

Codex skills documentation · loads skills from .agents/skills/

Paste into Codex

Install the "use-case-specification" agent skill from https://github.com/awslabs/agent-plugins/tree/main/plugins/sagemaker-ai/skills/use-case-specification into .agents/skills/use-case-specification/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "use-case-specification", then confirm the skill loads.

Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add awslabs/agent-plugins --skill use-case-specification -a cursor

Project install goes to .agents/skills/; add -g for ~/.cursor/skills/.

GitHub CLI

$ gh skill install awslabs/agent-plugins use-case-specification --agent cursor

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/awslabs/agent-plugins.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/plugins/sagemaker-ai/skills/use-case-specification .cursor/skills/use-case-specification && rm -rf skills-src

Use ~/.cursor/skills/ instead of .cursor/skills for a personal install.

Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/

Paste into Cursor

Install the "use-case-specification" agent skill from https://github.com/awslabs/agent-plugins/tree/main/plugins/sagemaker-ai/skills/use-case-specification into .cursor/skills/use-case-specification/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "use-case-specification", then confirm the skill loads.

Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

gemini skills install

$ gemini skills install https://github.com/awslabs/agent-plugins.git --path plugins/sagemaker-ai/skills/use-case-specification

--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.

skills CLI

$ npx skills add awslabs/agent-plugins --skill use-case-specification -a gemini-cli

Project install goes to .agents/skills/; add -g for ~/.gemini/skills/.

GitHub CLI

$ gh skill install awslabs/agent-plugins use-case-specification --agent gemini-cli

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/awslabs/agent-plugins.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/plugins/sagemaker-ai/skills/use-case-specification .gemini/skills/use-case-specification && rm -rf skills-src

Use ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.

Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/

Paste into Gemini CLI

Install the "use-case-specification" agent skill from https://github.com/awslabs/agent-plugins/tree/main/plugins/sagemaker-ai/skills/use-case-specification into .gemini/skills/use-case-specification/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "use-case-specification", then confirm the skill loads.

Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

GitHub CLI

$ gh skill install awslabs/agent-plugins use-case-specification

Installs for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).

skills CLI

$ npx skills add awslabs/agent-plugins --skill use-case-specification -a github-copilot

Project install goes to .agents/skills/; add -g for ~/.copilot/skills/.

Manual copy

$ git clone --depth 1 https://github.com/awslabs/agent-plugins.git skills-src && mkdir -p .github/skills && cp -r skills-src/plugins/sagemaker-ai/skills/use-case-specification .github/skills/use-case-specification && rm -rf skills-src

Use ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.

GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/

Paste into GitHub Copilot

Install the "use-case-specification" agent skill from https://github.com/awslabs/agent-plugins/tree/main/plugins/sagemaker-ai/skills/use-case-specification into .github/skills/use-case-specification/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "use-case-specification", then confirm the skill loads.

GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add awslabs/agent-plugins --skill use-case-specification -a opencode

OpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.

GitHub CLI

$ gh skill install awslabs/agent-plugins use-case-specification --agent opencode

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/awslabs/agent-plugins.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/plugins/sagemaker-ai/skills/use-case-specification .opencode/skills/use-case-specification && rm -rf skills-src

Use ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.

OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/

Paste into OpenCode

Install the "use-case-specification" agent skill from https://github.com/awslabs/agent-plugins/tree/main/plugins/sagemaker-ai/skills/use-case-specification into .opencode/skills/use-case-specification/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "use-case-specification", then confirm the skill loads.

OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

Facts

Skill name: use-case-specification
GitHub stars: 915
Token cost: ~1k tokens
SKILL.md length: 448 words
Files: 1
Skills in repo: 33
Repo updated: Oct 5, 2026
First seen: Oct 7, 2026
Licence: Apache-2.0

At a glance

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.

Works in 3 steps: Check for Existing Spec → Discovery (1–3 turns) → Producing a Use Case Specification…
Tasks that involve LLM guardrails
SKILL.md covers Principles, Workflow and use_case_specification Edit…
Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Use Case Specification is an agent skill from awslabs/agent-plugins, published by the product's own GitHub organization. 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.

Its SKILL.md is about 1k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in AI & LLM Engineering, covering LLM guardrails. It works with Amazon Web Services. The repository describes itself as: Agent Plugins for AWS equip AI coding agents with the skills to help you architect, deploy, and operate on AWS. The licence is Apache-2.0.

When your agent uses it

Tasks that involve LLM guardrails

Example prompts

“Use the use-case-specification skill to create a reusable use case specification file that defines the business problem, stakeholders, and…”
“/use-case-specification”

Workflow steps

3 steps, taken from the step headings in SKILL.md.

Check for Existing Spec
Discovery (1–3 turns)
Producing a Use Case Specification Document

What it can do on your machine

Read from SKILL.md and the folder at commit da51970. It shows what the files ask for, not the result of running them.

Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
No scripts in the folder and no shell commands in SKILL.md.
From the folder's file list and the shell code blocks in SKILL.md.
Network
Links to these hosts (documentation or services it may open):
- docs.aws.amazon.com
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Use Case Specification loads about 1k tokens when it runs. Until then it costs about 114 tokens; SKILL.md has 448 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it: ~114
When it runs · the whole SKILL.md, loaded when a task matches: ~1k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from awslabs/agent-plugins at commit da51970, republished under its Apache-2.0 licence (© awslabs). 448 words, ~1,012 tokens.

Download SKILL.mdSave it as .claude/skills/use-case-specification/SKILL.md (or your agent's skills folder).

name: use-case-specification
description: 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.
metadata.version: 1.0.0

Use Case Specification

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

Principles

One thing at a time. Each response advances exactly one decision or collects one piece of information.
Confirm before proceeding. Wait for the user to approve the spec before considering this skill complete.
Infer, don't interrogate. Use what's already known from the conversation. Only ask when you truly can't infer.
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.

Show full SKILL.md (155 more words)Show less

Phase 2: Producing a Use Case Specification Document

Save all generated artifacts under the project directory structure defined by the directory-management skill, if available.
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

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

Just SKILL.md in plugins/sagemaker-ai/skills/use-case-specification of awslabs/agent-plugins.

Open the folder on GitHubat commit da51970

Compare with similar skills

Use Case Specification next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Use Case Specification compared with similar skills
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Use Case Specification this skillawslabs/agent-plugins	915	—	~1k	Automated safety check: Pass	Apache-2.0	2 days ago
Wa Guardrailsaws-samples/sample-well-architected-skills-and-steering	273	—	~2.8k	Automated safety check: Pass	MIT-0	2 days ago
Amazon Bedrockaws/agent-toolkit-for-aws	2.8k	—	~8.6k	Automated safety check: Pass	Apache-2.0	today
Investigation Cost Guardrailaws/tools-for-devops-agent	100	—	~4.5k	Automated safety check: Pass	Apache-2.0	today
Hardening Cloud Posturetrilwu/secskills	156	—	~1.9k	Automated safety check: Pass	MIT	1 mo ago
Ak Add Capabilitiesyaalalabs/agent-kernel	191	—	~12k	Automated safety check: Pass	Apache-2.0	today

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Questions about Use Case Specification

What does Use Case Specification do?

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 Case Specification is an agent skill from awslabs/agent-plugins, published by the product's own GitHub organization. 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.

When should I use Use Case Specification?

Use Case Specification fits situations like: tasks that involve LLM guardrails.

How do I install Use Case Specification in Claude Code?

Run `npx skills add awslabs/agent-plugins --skill use-case-specification -a claude-code`. Or copy the skill folder (plugins/sagemaker-ai/skills/use-case-specification in awslabs/agent-plugins) into .claude/skills/use-case-specification in your project. Claude Code loads it when a task matches its description.

How do I install Use Case Specification in Codex?

Run `npx skills add awslabs/agent-plugins --skill use-case-specification -a codex`. Or copy the skill folder (plugins/sagemaker-ai/skills/use-case-specification in awslabs/agent-plugins) into .agents/skills/use-case-specification in your project. Codex loads it when a task matches its description.

Can I use Use Case Specification in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add awslabs/agent-plugins --skill use-case-specification -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/use-case-specification, .gemini/skills/use-case-specification, .github/skills/use-case-specification and .opencode/skills/use-case-specification in your project.

What does Use Case Specification need to run?

SKILL.md names no scripts, command-line tools or credentials: Use Case Specification is instructions for the agent only.

Does Use Case Specification access the network?

SKILL.md names 1 domain. As links in the text: docs.aws.amazon.com. This is read from the text; nothing was executed.

Is Use Case Specification safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Use Case Specification use?

Use Case Specification is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Use Case Specification use?

About 1k tokens (SKILL.md is roughly 4k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Use Case Specification?

Skills that share tags, products or a category with Use Case Specification: Wa Guardrails (aws-samples/sample-well-architected-skills-and-steering, 273 stars), Amazon Bedrock (aws/agent-toolkit-for-aws, 2.8k stars), Investigation Cost Guardrail (aws/tools-for-devops-agent, 100 stars) and Hardening Cloud Posture (trilwu/secskills, 156 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Use Case Specification?

awslabs (a GitHub organization, an official publisher) maintains it in awslabs/agent-plugins, which has 915 GitHub stars. The repository holds 33 skills in this directory. The repository was last updated on October 5, 2026.

Source: awslabs/agent-plugins on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.

Use Case Specification

Install Use Case Specification

Facts

At a glance

What it does

When your agent uses it

Example prompts

Workflow steps

What it can do on your machine

Tool permissions

Runs code

Network

Credentials

Context cost

Safety

SKILL.md

Use Case Specification

Principles

Workflow

Step 0: Check for Existing Spec

Phase 1: Discovery (1–3 turns)

Phase 2: Producing a Use Case Specification Document

use_case_specification Edit Protocol

Files

Compare with similar skills

Similar skills

Wa Guardrails

Amazon Bedrock

Investigation Cost Guardrail

Hardening Cloud Posture

Ak Add Capabilities

SageMaker Serving Image Selection

More from awslabs/agent-plugins

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Hyperpod Ssm

Questions about Use Case Specification

Install Use Case Specification

Facts

At a glance

What it does

When your agent uses it

Example prompts

Workflow steps

What it can do on your machine

Tool permissions

Runs code

Network

Credentials

Context cost

Safety

SKILL.md

Use Case Specification

Principles

Workflow

Step 0: Check for Existing Spec

Phase 1: Discovery (1–3 turns)

Phase 2: Producing a Use Case Specification Document

use_case_specification Edit Protocol

Files

Compare with similar skills

Similar skills

More from awslabs/agent-plugins

Related

Questions about Use Case Specification