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 privacy-policy-gap-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/privacy-policy-gap-review)<a href="https://agentmods.dev/skills/zgbrenner/agentcounsel/privacy-policy-gap-review"><img src="https://agentmods.dev/badge/skills/zgbrenner/agentcounsel/privacy-policy-gap-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/privacy-policy-gap-review"><img src="https://agentmods.dev/badge/skills/zgbrenner/agentcounsel/privacy-policy-gap-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 | $0.00038 | $0.03608 |
| Opus 5 | $0.00019 | $0.01804 |
| Sonnet 5 | $0.00008 | $0.00722 |
| Haiku 4.5 | $0.00004 | $0.00361 |
Grade A, and why
Privacy Policy Gap 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 9d 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 — 179 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Privacy Policy Gap Review
Purpose
Produce a structured, attorney-ready gap review of a published privacy policy or privacy notice. The review identifies: missing standard disclosure topics, vague or boilerplate language that may not reflect actual practice, internal inconsistencies within the policy, and discrepancies between the policy's representations and the organization's actual data practices as described by the user. It produces draft legal work product for attorney review — not legal advice.
This skill provides structural and drafting analysis only. It does not certify compliance with any specific privacy law, regulation, or jurisdiction. Whether any identified gap constitutes a legal violation — and what remediation is legally required — are attorney-verification items. The applicable law, the organization's compliance posture, and the jurisdictional scope of the policy must be determined by counsel.
Use When
- A user asks to "review our privacy policy," "find gaps in this privacy notice," or "does our policy match what we actually do."
- An organization is updating its privacy policy and wants a first-pass review before attorney review and redrafting.
- A privacy audit or assessment requires a document review of the current privacy notice.
- An organization has changed its data practices (new vendor, new product feature, new data collection) and needs to identify whether the policy needs to be updated.
- Due diligence on a transaction requires review of the target's public-facing privacy representations.
- A regulator or counterparty has raised concerns about the organization's privacy policy and the legal team needs a structured analysis.
- The organization operates in multiple jurisdictions and wants to identify disclosure topics that may need to be addressed for different audiences
[CONFIRM: applicable requirements with counsel].
Required Inputs
- The privacy policy or privacy notice text — uploaded, pasted, or linked. If not provided, stop and request it. Do not fabricate or assume policy terms.
- A description of the organization's actual data practices — what personal data is collected, from whom, for what purposes, who it is shared with, how it is processed, and how long it is retained. This description must come from the user; do not invent practices. If this description is not provided, the practice-versus-policy comparison step cannot be completed — note the gap and proceed with a structural review only, flagging the comparison as an open item.
- Optional: the organization's industry or sector (e.g., healthcare, financial services, children's services, e-commerce) — relevant for identifying sector-specific disclosure topics to flag, though applicable law is always
[CONFIRM]. - Optional: the audience or jurisdictions the policy is intended to serve (e.g., EU residents, California residents, global) — used to identify disclosure topics commonly associated with those audiences, not to assert applicable law.
- Optional: a prior version of the policy, if this is a revision review.
- Optional: the practice group's
practice-profiles/privacy.mdif it has been populated and is loaded alongside this skill. If present, the skill uses its Standard Positions and Source-of-Truth Documents tables to benchmark the policy against the group's baseline policy template. 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.
- 9d ago First seen · 179 lines · 38 tokens per session scan A 37892c6ab7c1
Privacy Policy Gap Review is a skill published in the GitHub repository zgbrenner/agentcounsel (19 stars, last pushed 1mo ago), licensed MIT. It adds 38 tokens to every session and 3,608 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 2026-09-03.
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