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 skills add zgbrenner/agentcounsel --skill ai-use-case-intakegit clone --depth 1 https://github.com/zgbrenner/agentcounselWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/zgbrenner/agentcounsel/ai-use-case-intake)<a href="https://agentmods.dev/skills/zgbrenner/agentcounsel/ai-use-case-intake"><img src="https://agentmods.dev/badge/skills/zgbrenner/agentcounsel/ai-use-case-intake/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/zgbrenner/agentcounsel/ai-use-case-intake"><img src="https://agentmods.dev/badge/skills/zgbrenner/agentcounsel/ai-use-case-intake.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What 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 5.1 | $0.00042 | $0.02556 |
| Opus 5 | $0.00021 | $0.01278 |
| Sonnet 5 | $0.00008 | $0.00511 |
| Haiku 4.5 | $0.00004 | $0.00256 |
Grade A, and why
AI Use Case Intake 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 11d 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 — 175 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Use Case Intake
Purpose
Produce a structured, attorney-ready intake record for a proposed or in-flight AI/ML use case. The record captures the information legal and compliance teams need to triage, route, and assess the use case — it does not render a legal conclusion about lawfulness or compliance. The preliminary risk tier is a triage signal only.
Use When
- A product, engineering, or business team is proposing a new AI or ML feature, product, or workflow and needs legal sign-off or triage.
- An existing AI use case is being materially changed (new model, new data, new affected population, new market).
- Legal or compliance has received a question like "is this AI thing okay?" and needs a structured starting point.
- A governance or AI review committee requires a standardized intake record before approving a use case.
- A vendor is proposing an AI-enabled product or service and the organization must assess it before contracting.
Required Inputs
- Use case description: A plain-language description of what the AI system does and why, provided by the requester.
- AI system or model details: The specific model(s), provider(s), or platform(s) involved (e.g., OpenAI GPT-4o via API, AWS SageMaker custom model, Microsoft Copilot).
- Input data description: What data goes into the model — sources, types, whether it includes personal data, and any sensitivity classifications.
- Output description: What the system produces and how outputs are used or acted upon.
- Affected persons: Who is impacted by the system's outputs (employees, consumers, job applicants, patients, students, etc.).
- Deployment markets: Countries and states or regions where the use case will operate.
If any required input is missing, stop and request it from the requester. Do not fabricate or assume facts about the system, data, or affected individuals.
Do Not Use When
- You have a vendor AI contract to review — use
ai-vendor-terms-reviewfor that. - You need to triage the risk of a specific model already under consideration — use
model-risk-triage. - The question is primarily about employee use of AI tools — use
employee-ai-policy. - The primary legal question is a data privacy assessment — route to the relevant privacy skill after completing this intake.
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.
- 11d ago First seen · 175 lines · 42 tokens per session scan A e5eeb336439f
AI Use Case Intake is a skill published in the GitHub repository zgbrenner/agentcounsel (19 stars, last pushed 1mo ago), licensed MIT. It adds 42 tokens to every session and 2,556 once invoked, about $0.0002 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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