Skills for Real Engineers. Straight from my .agents directory.
use-case-specification
Agent skill fromawslabs/agent-plugins
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.
How to install use-case-specification
npx skills add awslabs/agent-plugins# or add only this skill to Claude Code: copy the folder
plugins/sagemaker-ai/skills/use-case-specification/
# into
~/.claude/skills/use-case-specification/ (all projects)
.claude/skills/use-case-specification/ (one project)
1# Use Case Specification23Multi-turn conversation to gather use case details and produce a use case specification document.45## Principles671. **One thing at a time.** Each response advances exactly one decision or collects one piece of information.82. **Confirm before proceeding.** Wait for the user to approve the spec before considering this skill complete.93. **Infer, don't interrogate.** Use what's already known from the conversation. Only ask when you truly can't infer.104. **Do NOT ask about base model selection.** Model selection is handled exclusively by the model-selection skill.1112## Workflow1314### Step 0: Check for Existing Spec1516Before 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.1718### Phase 1: Discovery (1–3 turns)1920Review what is already known from the conversation so far, then identify what is still missing. You need these three things:2122- **What** is the problem the user is trying to solve with model customization23- **Who** will use the finetuned model and in what context24- **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.2526**Guidelines**:2728- Infer as much as possible from what the user has already said29…
Excerpt shown under the repository's Apache-2.0 license.
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