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-feature-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-feature-review)<a href="https://agentmods.dev/skills/zgbrenner/agentcounsel/ai-feature-review"><img src="https://agentmods.dev/badge/skills/zgbrenner/agentcounsel/ai-feature-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-feature-review"><img src="https://agentmods.dev/badge/skills/zgbrenner/agentcounsel/ai-feature-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 | $0.00036 | $0.02961 |
| Opus 5.5 | $0.00014 | $0.01184 |
| Sonnet 5.5 | $0.00007 | $0.00592 |
| Haiku 4.5 | $0.00004 | $0.00296 |
Grade A, and why
AI Feature 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 1mo 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 — 151 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Feature Review
Purpose
Produce a structured, attorney-ready legal issues register for a product feature that uses artificial intelligence or machine learning. This skill spots legal exposure across training-data rights, output ownership and infringement, transparency and disclosure obligations, privacy and data use, automated-decision concerns, vendor terms, and high-risk use cases. It routes AI-vendor contract questions to ai-vendor-terms-review and broader AI risk triage to model-risk-triage. It produces draft legal work product for attorney review — not legal advice, not a regulatory clearance, and not a determination that the feature is lawful.
Use When
- A team is building, shipping, or updating a feature that uses an AI model, machine learning system, or algorithmic decision-making component.
- A product manager, engineer, or counsel asks to "review the legal risks of this AI feature," "check if we can use this model," or "what do we need to disclose?"
- A launch review (see
launch-review) has flagged an AI or algorithmic component for deeper analysis. - An existing AI feature is being modified in a material way: new model, new data inputs, new output use case, or new user population.
- The feature involves a vendor-supplied AI model and the team needs a legal issues overview before completing vendor contracting (route contract detail to
ai-vendor-terms-review). - The feature involves automated decisions that affect users in consequential ways (credit, employment, health, housing, content moderation, pricing).
Required Inputs
- Feature description: what the feature does, how users interact with it, and what is new or changed.
- Model(s) used: whether the model is in-house (trained or fine-tuned internally) or vendor-supplied (API, embedded, or licensed). Include model name or vendor name if known.
- Training and input data: what data was used to train or fine-tune the model (if in-house), and what data the model receives at inference time (user inputs, uploaded files, third-party data, etc.).
- Outputs and how they are used: what the model produces (text, images, scores, classifications, recommendations, decisions), and how those outputs are displayed to or acted upon by users or internal systems.
- User-facing disclosures: what, if anything, the product currently discloses to users about AI use, automated decision-making, or the nature of the outputs.
- Human-oversight design: whether and how a human reviews, approves, or can override AI outputs before they affect users.
- Target markets and users: geographies, user demographics, and any vulnerable populations (minors, patients, financial consumers, job seekers).
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.
- 1mo ago First seen · 151 lines · 36 tokens per session scan A b36fb50f119f
AI Feature Review is a skill published in the GitHub repository zgbrenner/agentcounsel (21 stars, last pushed 2mo ago), licensed MIT. It adds 36 tokens to every session and 2,961 once invoked, about $0.0001 per session on Opus 5.5. 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 2026-09-03.
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