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 model-risk-triagegit 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/model-risk-triage)<a href="https://agentmods.dev/skills/zgbrenner/agentcounsel/model-risk-triage"><img src="https://agentmods.dev/badge/skills/zgbrenner/agentcounsel/model-risk-triage/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/model-risk-triage"><img src="https://agentmods.dev/badge/skills/zgbrenner/agentcounsel/model-risk-triage.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 | $0.02795 |
| Opus 5.5 | $0.00017 | $0.01118 |
| Sonnet 5.5 | $0.00008 | $0.00559 |
| Haiku 4.5 | $0.00004 | $0.00280 |
Grade A, and why
Model Risk Triage 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 — 172 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Model Risk Triage
Purpose
Produce a structured triage of the legal, governance, and operational risk dimensions of a specific AI model or AI system. The output is a risk register with ownership assignments and recommended controls — not a legal opinion on whether the model may be deployed. It equips attorneys and governance reviewers with the structured information they need to make deployment decisions.
This skill is model-level and technical in focus. It complements ai-use-case-intake (which covers the business use case) and ai-vendor-terms-review (which covers vendor contractual terms).
Use When
- Engineering, product, or legal teams need to assess a specific model before integration or deployment.
- A governance committee requires a pre-deployment risk assessment for an AI model.
- A model is being upgraded, retrained, or replaced and a delta risk assessment is needed.
- An audit, incident, or regulatory inquiry requires documentation of what risk assessment was performed before deployment.
- A user asks "what are the risks of using this model?" or "what do we need to have in place before we deploy this?"
Required Inputs
- Model identification: Name, version, provider or source (vendor API, open-source repository, internally trained), and a link to model documentation or model card if available.
- Intended use description: How the organization plans to use the model — inputs, outputs, and the decision or action outputs will inform or automate.
- Affected individuals: Who will be impacted by the model's outputs (employees, consumers, patients, job applicants, etc.) and the estimated population size or scope.
- Deployment context: The application, product, or system the model will be embedded in, and whether outputs will be directly user-facing.
- Available technical documentation: Model card, data sheet, evaluation reports, accuracy benchmarks, bias evaluation results — whatever is available. Note what is not available.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 172 lines · 42 tokens per session scan A 30364c45c8a7
Model Risk Triage is a skill published in the GitHub repository zgbrenner/agentcounsel (21 stars, last pushed 2mo ago), licensed MIT. It adds 42 tokens to every session and 2,795 once invoked, about $0.0002 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 2026-08-30.
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