Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/awslabs/agent-plugins/use-case-specificationnpx skills add awslabs/agent-plugins --skill use-case-specificationgit clone --depth 1 https://github.com/awslabs/agent-pluginsWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00085 | $0.00869 |
| Opus 5 | $0.00043 | $0.00434 |
| Sonnet 5 | $0.00017 | $0.00174 |
| Haiku 4.5 | $0.00009 | $0.00087 |
Grade A, and why
use-case-specification scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 80 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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.
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):
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago First seen · 80 lines · 85 tokens per session scan A 6eb2f6426e64
use-case-specification is a skill published in the GitHub repository awslabs/agent-plugins (876 stars, last pushed 5d ago), licensed Apache-2.0. It adds 85 tokens to every session and 869 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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