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.
git clone --depth 1 https://github.com/briiirussell/cybersecurity-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/rules/briiirussell/cybersecurity-skills/privacy-engineering)<a href="https://agentmods.dev/rules/briiirussell/cybersecurity-skills/privacy-engineering"><img src="https://agentmods.dev/badge/rules/briiirussell/cybersecurity-skills/privacy-engineering/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/rules/briiirussell/cybersecurity-skills/privacy-engineering"><img src="https://agentmods.dev/badge/rules/briiirussell/cybersecurity-skills/privacy-engineering.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 | $0.00204 | $0.03710 |
| Opus 5.5 | $0.00082 | $0.01484 |
| Sonnet 5.5 | $0.00041 | $0.00742 |
| Haiku 4.5 | $0.00020 | $0.00371 |
Grade A, and why
privacy-engineering 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 today.
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 — 240 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Privacy Engineering — GDPR / CCPA Technical Implementation
Implement privacy controls at the code, data, and infrastructure layers. This skill is not legal compliance theater — it is the engineering work that turns the legal requirements into systems that actually do what they claim.
Privacy and security overlap but are not the same. Security protects against unauthorized access; privacy protects against authorized-but-improper use. A perfectly secure system that logs every keystroke and shares the log with vendors is a privacy disaster. This skill covers the privacy half of that distinction.
Cross-references: owasp-audit for the security side, iam-audit for access control to personal data, secrets-audit for credential handling, incident-triage for the response side of a privacy breach (72-hour GDPR notification clock starts when you find out, not when you finish investigating), security-comms for the customer-disclosure draft.
Authorization
Review systems the user's organization owns or has engaged them to review. Trace data flows through schemas, code, configs and synthetic records, not by pulling real personal data. If you find personal data where it shouldn't be, record the location and category without copying the values. Legal conclusions (lawful basis, whether a breach is notifiable) belong to the DPO and counsel.
Regulatory landscape (engineering-relevant subset)
The skill produces compliant technical implementations. Final compliance determinations stay with counsel; this skill is the technical execution layer.
| Regulation | Scope | Key engineering hooks |
|---|---|---|
| GDPR (EU) | Any processing of personal data of EU/EEA residents | Articles 5 (principles), 6 (lawful basis), 7 (consent), 15-22 (data subject rights), 25 (privacy by design), 30 (records of processing), 32 (security), 33 (breach notification — 72 hours), 35 (DPIA) |
| CCPA / CPRA (California) | Businesses processing CA resident data above thresholds | Right to know, delete, correct, opt out of sale / share. Sensitive PI category. Annual privacy notice. Service-provider contracts |
| LGPD (Brazil) | Brazilian residents | Similar shape to GDPR with local twists |
| PIPEDA (Canada) | Federal commercial | Consent-based with reasonable expectation, breach notification |
| State laws (US) | Varies — VA, CO, CT, UT, etc. | Roughly CCPA-shaped; engineering practices that meet GDPR + CCPA usually cover state laws |
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.
- today Changed · +4 lines · -4 tokens per session 99b618d0190a
- 1mo ago First seen · 236 lines · 208 tokens per session scan A e49cbdfab664
privacy-engineering is a cursor rule published in the GitHub repository briiirussell/cybersecurity-skills (435 stars, last pushed today), licensed MIT. It adds 204 tokens to every session and 3,710 once invoked, about $0.0008 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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