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 codebygarv/Ai-skills --skill compliance-privacy-reviewergit clone --depth 1 https://github.com/codebygarv/Ai-skillsWrote 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/codebygarv/ai-skills/compliance-privacy-reviewer)<a href="https://agentmods.dev/skills/codebygarv/ai-skills/compliance-privacy-reviewer"><img src="https://agentmods.dev/badge/skills/codebygarv/ai-skills/compliance-privacy-reviewer/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/codebygarv/ai-skills/compliance-privacy-reviewer"><img src="https://agentmods.dev/badge/skills/codebygarv/ai-skills/compliance-privacy-reviewer.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 | $0.00052 | $0.00647 |
| Opus 5 | $0.00026 | $0.00324 |
| Sonnet 5 | $0.00010 | $0.00129 |
| Haiku 4.5 | $0.00005 | $0.00065 |
Grade A, and why
compliance-privacy-reviewer 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 7d 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 — 38 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Purpose
Review how a feature collects, stores, processes, and retains personal data against common privacy-framework principles — data minimization, purpose limitation, consent, retention limits, and user rights (access/deletion/export) — flagging gaps before they become compliance problems.
When to Use
- A feature collects, stores, or processes personal data (PII) — user profiles, tracking/analytics, location data, health/financial data, anything third-party-shareable.
- Reviewing a data flow or new integration for privacy implications.
- Before shipping something that touches user data in a new way.
This is not legal advice. Real compliance sign-off (GDPR, CCPA, HIPAA, etc.) requires qualified legal review — this skill flags likely gaps for a human/legal team to evaluate, not a compliance certification.
What to Analyze
- Data minimization — is every piece of personal data collected actually necessary for the stated purpose, or is more being collected "just in case"?
- Purpose limitation — is data used only for the purpose it was collected for, or silently reused elsewhere (e.g. support-ticket data later used for marketing without new consent)?
- Consent — is consent captured before data collection where required, is it specific (not bundled into an unrelated ToS acceptance), and can it be withdrawn?
- Retention — is there a defined retention period, or does data accumulate indefinitely with no deletion policy?
- User rights — can a user actually access, export, or delete their data on request, or does the current design make that operationally difficult (data scattered across systems with no way to locate/purge it all)?
- Third-party sharing — is personal data sent to third parties (analytics, ad tech, subprocessors) disclosed, and is there a data processing agreement consideration flagged for legal?
Output Format
- Findings grouped by principle (Data Minimization, Purpose Limitation, Consent, Retention, User Rights, Third-Party Sharing).
- Each finding: what's collected/done, the gap against the principle, and a concrete recommendation.
- Explicit reminder at the top and bottom of output: this is a technical/design review, not legal sign-off — flag for legal/compliance review before treating any framework as "satisfied."
What ships with it
2 files 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.
- 7d ago First seen · 38 lines · 52 tokens per session scan A a8a26924869f
compliance-privacy-reviewer is a skill published in the GitHub repository codebygarv/Ai-skills (25 stars, last pushed 22d ago), licensed MIT. It adds 52 tokens to every session and 647 once invoked, about $0.0003 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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