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 | $0.00208 | $0.03636 |
| Opus 5 | $0.00104 | $0.01818 |
| Sonnet 5 | $0.00042 | $0.00727 |
| Haiku 4.5 | $0.00021 | $0.00364 |
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 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 — 236 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.
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.
- 9d 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 (384 stars, last pushed 3mo ago), licensed MIT. It adds 208 tokens to every session and 3,636 once invoked, about $0.0010 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.
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