Zen-Ai-Pentest: Agent for Claude Code

.opencode/agents/data-privacy-officer.md

Data Privacy Officer is an agent for Claude Code, OpenCode from SHAdd0WTAka/Zen-Ai-Pentest. It costs 46 tokens per session (4,716 once invoked), scanned A, original, MIT.

A privacy-compliance specialist who helps organizations handle personal data under rules such as GDPR and CCPA.

In plain words
What is it for?
Use it for data mapping, privacy impact assessments, consent management, data-subject requests, breach response, vendor checks, and cross-border transfer reviews.
Why use it?
It helps turn legal privacy requirements into practical controls for collecting, using, storing, sharing, and deleting personal data.

Agent for Claude CodeOpenCode

Written for OpenCode and Claude Code: installed under .opencode/, but also a Claude Code subagent (agents/*.md). Also seen: mentions subagents.

This is SHAdd0WTAka/Zen-Ai-Pentest's own configuration. It tells Claude Code and OpenCode how to work on Zen-Ai-Pentest itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything Zen-Ai-Pentest configures →

Reuse

Borrowing it

Nothing to install: this file belongs to SHAdd0WTAka/Zen-Ai-Pentest. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/SHAdd0WTAka/Zen-Ai-Pentest/main/.opencode/agents/data-privacy-officer.md
Clone the repo
git clone --depth 1 https://github.com/SHAdd0WTAka/Zen-Ai-Pentest

Made for: Claude Code, OpenCode.

Wrote 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.

agentmods badge for Data Privacy Officer

README.md
[![agentmods](https://agentmods.dev/badge/agents/shadd0wtaka/zen-ai-pentest/data-privacy-officer/github.svg)](https://agentmods.dev/agents/shadd0wtaka/zen-ai-pentest/data-privacy-officer)
Your own site
<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.

agentmods 80×15 button for Data Privacy Officer

Your own site · 80×15
<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>
Per session 46 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 4,716 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00046 $0.04716
Opus 5.5 $0.00018 $0.01886
Sonnet 5.5 $0.00009 $0.00943
Haiku 4.5 $0.00005 $0.00472

Measured 1mo ago against content hash 08878e915ba3, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

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 1mo 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.

.opencode/agents/data-privacy-officer.md · 412 lines

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.

Read the full file on GitHub · 412 lines

Changes

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.

  1. 1mo ago First seen · 412 lines · 46 tokens per session scan A 08878e915ba3

Subscribe to this mod's changes

Data Privacy Officer is an agent published in the GitHub repository SHAdd0WTAka/Zen-Ai-Pentest (499 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 5.5. 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.

Related

Other agents, from other repositories

terms

Drafts GDPR-compliant privacy policies, Terms of Service, cookie notices, and DPAs sized to company stage. Use when you need a privacy policy, ToS, or data processing agreement written or audited. Trigger with "draft my privacy policy", "review my terms of service".

jeremylongshore/tons-of-skills-marketplace · 59 tokens

playwright-test-generator

Use this agent when you need to create automated browser tests using Playwright Examples: Context: User wants to generate a test for the test plan item.

microsoft/playwright · 151 tokens

.NET-Notebook-Migration-Agent

Expert .NET and documentation transformation agent that migrates Polyglot Jupyter notebooks into clean Markdown and companion .NET sample code.

microsoft/ai-agents-for-beginners · 33 tokens

AVM Owner Triage

Triage open GitHub issues across the Azure Verified Modules (AVM) repos an owner maintains. Splits the backlog into a Copilot-delegatable pile and a human pile, produces a report with a delegation ratio, and never comments or assigns without explicit user approval.

github/awesome-copilot · 61 tokens

code-reviewer

Performs thorough code reviews for the Notebooks in the Cookbook repo, focusing on Python/Jupyter best practices, and project-specific standards. Use this agent proactively after writing any significant code changes, especially when modifying notebooks, Github Actions, and scripts.

anthropics/claude-cookbooks · 52 tokens

Ultimate Transparent Thinking Beast Mode

Agent "Ultimate Transparent Thinking Beast Mode" from github/awesome-copilot, covering quantum cognitive architecture, phase 2: adversarial intelligence & red-team analysis, phase 3: implementation & iterative refinement and phase 4: comprehensive verification & completion.

github/awesome-copilot · 11 tokens