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 employee-ai-policygit 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/employee-ai-policy)<a href="https://agentmods.dev/skills/zgbrenner/agentcounsel/employee-ai-policy"><img src="https://agentmods.dev/badge/skills/zgbrenner/agentcounsel/employee-ai-policy/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/employee-ai-policy"><img src="https://agentmods.dev/badge/skills/zgbrenner/agentcounsel/employee-ai-policy.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 | $0.00040 | $0.03168 |
| Opus 5 | $0.00020 | $0.01584 |
| Sonnet 5 | $0.00008 | $0.00634 |
| Haiku 4.5 | $0.00004 | $0.00317 |
Grade A, and why
Employee AI Policy 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 — 176 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Employee AI Policy
Purpose
Produce a structured review of an organization's internal employee AI-use policy (or, if no policy exists, a structured gap analysis based on the topics a policy should address). The output is a gap-and-issues table with prioritized recommendations for attorney and HR review.
This skill does not certify legal compliance, render an opinion on what any law requires, or produce a final policy. It identifies what is missing, inconsistent, or ambiguous and routes open questions to the right specialists.
Use When
- An organization has a draft AI-use policy for employees and wants it reviewed before publication.
- Legal, HR, or compliance has been asked "do we have what we need in our AI policy?" or "what should our AI policy cover?"
- An existing AI policy needs to be updated because new AI tools have been adopted or applicable law has changed.
- An incident (data leak, confidentiality breach via AI tool, IP dispute) has prompted a policy review.
- A user asks "what should employees be allowed to do with AI tools?" or "how do we handle employees using generative AI tools for work?"
Required Inputs
- Policy text (if one exists): The full text of the current or draft employee AI-use policy, uploaded or pasted.
- Organization context: A brief description of the organization's industry, approximate size, and the types of AI tools employees are currently using or are likely to use.
- Jurisdictions: The countries and states or provinces where employees are located — employment law is jurisdiction-specific and the review will flag where jurisdiction-specific legal input is needed.
- 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 · 176 lines · 40 tokens per session scan A 0d8ae2f58b38
Employee AI Policy is a skill published in the GitHub repository zgbrenner/agentcounsel (19 stars, last pushed 1mo ago), licensed MIT. It adds 40 tokens to every session and 3,168 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.
Other skills, from other repositories
runtime-admissibility-review
Determines whether a specific AI-agent action, output, recommendation, or proposed commitment remains admissible for execution or institutional reliance under current authority, delegated scope, evidence, facts, policy, risk, escalation, and revocation conditions. Use this Skill before an enterprise or regulated AI…
agent-authority-charter-builder-arkadiy-miteiko
Creates an Agent Authority Charter for enterprise or regulated AI agents before deployment. Use this Skill when a user needs to define what an AI agent is allowed to do, who delegated authority to it, what actions are permitted or prohibited, when human approval is required, what evidence must be preserved, and how…
legal-research
A legal research skill for finding and analysing laws and court cases, including similar cases. It requires access to a reliable legal research database before producing a formal report.
document-fill
A document-filling assistant that uses a supplied template and case files to fill in forms, contracts, or legal documents. It also reports where each filled value came from and which details are missing.
contract-review
A contract-review workflow for agreements governed by Chinese law, United States law, or both. It examines clauses one by one and produces a list of legal-risk questions, with an optional marked-up Word document.
fedramp
Expert guidance for FedRAMP certification and compliance under CR26 (FedRAMP Consolidated Rules for 2026). Use this skill whenever a user asks about FedRAMP authorization, ATO (Authority to Operate), cloud security for federal government, NIST SP 800-53 controls, CSP compliance, or any of the core FedRAMP document…