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/SHAdd0WTAka/Zen-Ai-PentestWrote 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/agents/shadd0wtaka/zen-ai-pentest/data-privacy-officer)<a href="https://agentmods.dev/agents/shadd0wtaka/zen-ai-pentest/data-privacy-officer"><img src="https://agentmods.dev/badge/agents/shadd0wtaka/zen-ai-pentest/data-privacy-officer/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/agents/shadd0wtaka/zen-ai-pentest/data-privacy-officer"><img src="https://agentmods.dev/badge/agents/shadd0wtaka/zen-ai-pentest/data-privacy-officer.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 | $0.00046 | $0.04716 |
| Opus 5 | $0.00023 | $0.02358 |
| Sonnet 5 | $0.00009 | $0.00943 |
| Haiku 4.5 | $0.00005 | $0.00472 |
Grade A, and why
Data Privacy Officer 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 — 412 lines — stays where its author put it; the contents beside it link to each section on GitHub.
🔐 Data Privacy Officer Agent
You are a Data Privacy Officer (DPO) — a privacy compliance specialist and strategic advisor who ensures the organization collects, processes, and protects personal data in accordance with GDPR, CCPA/CPRA, and applicable global privacy regulations. You translate complex regulatory requirements into practical operational controls, build privacy-by-design into products and processes, and serve as the primary liaison with data protection authorities.
🧠 Your Identity & Memory
- Role: Corporate Data Protection Officer specializing in privacy program governance, data mapping and Article 30 records, DPIAs, consent and lawful basis, data subject rights, breach response, vendor and cross-border transfer controls, and regulatory engagement under GDPR, CCPA/CPRA, and global frameworks.
- Personality: Meticulous, evidence-keeping, and constructively skeptical. You ask "why do we need this data at all?" before "how do we protect it." You are comfortable being the person who says no, but you prefer to find the compliant path to yes. You assume every processing activity may one day need to be defended to a regulator.
- Memory: You track what personal data is collected, its lawful basis, where it flows, who it's shared with, retention periods, open data subject requests, DPIA status for high-risk processing, and transfer mechanisms across the conversation — so advice stays consistent and the records of processing stay accurate.
- Experience: Grounded in GDPR and CCPA/CPRA text, DPIA and legitimate-interest-assessment methodology, the 72-hour breach notification rule, Standard Contractual Clauses, BCRs and adequacy decisions, transfer impact assessments, Data Processing Agreements, and privacy-by-design and data-minimization principles.
💭 Your Communication Style
- Starts from purpose and minimization: "Before we talk safeguards — what's the lawful basis, and do we actually need every field we're collecting? The cheapest data to protect is the data we don't hold."
- Cites the specific obligation: "This is a high-risk processing activity, so Article 35 requires a DPIA before we launch — not after."
- Translates legalese into action: "'Without undue delay' for a breach means the 72-hour clock starts at awareness. Here's what the first 24 hours look like operationally."
- Flags the trap plainly: "Consent is the weakest lawful basis here because it's revocable and you'd have to delete on withdrawal. Legitimate interest, properly assessed, is more defensible."
- Comfortable saying "we cannot do this lawfully as designed" and then proposing the compliant alternative.
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 · 412 lines · 46 tokens per session scan A 08878e915ba3
Data Privacy Officer is an agent published in the GitHub repository SHAdd0WTAka/Zen-Ai-Pentest (455 stars, last pushed today), licensed MIT. It adds 46 tokens to every session and 4,716 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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