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-vendor-terms-reviewgit 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-vendor-terms-review)<a href="https://agentmods.dev/skills/zgbrenner/agentcounsel/ai-vendor-terms-review"><img src="https://agentmods.dev/badge/skills/zgbrenner/agentcounsel/ai-vendor-terms-review/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-vendor-terms-review"><img src="https://agentmods.dev/badge/skills/zgbrenner/agentcounsel/ai-vendor-terms-review.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 | $0.04430 |
| Opus 5 | $0.00021 | $0.02215 |
| Sonnet 5 | $0.00008 | $0.00886 |
| Haiku 4.5 | $0.00004 | $0.00443 |
Grade A, and why
AI Vendor Terms Review 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 12d 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 — 199 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Vendor Terms Review
Purpose
Produce a structured, attorney-ready review of an AI vendor's terms of service, API agreement, enterprise license, or acceptable use policy. The review focuses on the provisions that are materially different in AI contracts — rights to inputs and prompts, training data use, output ownership, IP indemnity, accuracy disclaimers, and model-change rights — while also flagging standard contract risk areas that apply with particular force in AI contexts.
This skill produces draft legal work product for attorney review. It does not render a legal conclusion about whether any term is enforceable, whether the contract should be signed, or what any specific law requires.
Use When
- A team wants to adopt a new AI API, AI platform, or AI-enabled SaaS product and needs the legal terms reviewed.
- An existing AI vendor agreement is up for renewal or the vendor has pushed updated terms.
- A user asks "what are we giving up by signing this?" or "does this vendor own what we generate?"
- Legal has received a vendor-side paper AI agreement and needs a structured risk review.
- A vendor's acceptable use policy or model card terms need review before integration.
Required Inputs
- Vendor agreement text: The full text of the terms of service, API agreement, data processing agreement, and/or acceptable use policy — uploaded or pasted. Identify which document(s) are provided.
- Client's intended use: A plain-language description of what the organization plans to do with the vendor's AI system (e.g., generate customer-facing marketing copy, process employee HR queries, analyze legal documents).
- Client's role: Whether the organization is an API consumer, enterprise licensee, reseller, or end user.
- Data the client will input: The types of data the client will send to the vendor's system — including whether it includes personal data, confidential business information, or privileged material.
- Privileged or work-product material: Whether the client intends to input or has historically input material protected by attorney-client privilege or attorney work-product doctrine (drafts of legal memos, analyses for counsel, litigation strategy, etc.). If yes, note the matter context and the governing privilege jurisdiction.
- Optional: the practice group's
practice-profiles/ai-governance.mdif it has been populated and is loaded alongside this skill. If present, the skill uses its Standard Positions and Escalation Thresholds tables to benchmark the output and to gate escalation. If absent, the skill proceeds without practice-profile benchmarking and asks the user to supply standing positions inline if needed.
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.
- 12d ago First seen · 199 lines · 42 tokens per session scan A 10c4a7e9f891
AI Vendor Terms Review 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 4,430 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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