gdpr-expert

gdpr-expert is a skill for Claude Code from personamanagmentlayer/pcl. It costs 71 tokens per session (2,770 once invoked), scanned A, original, Apache-2.0.

A guide to GDPR, the European Union's data-protection law, covering privacy, consent, data collection, and people's rights over their personal data. It also covers the responsibilities of organisations and data protection officers.

In plain words
What is it for?
Use it for privacy-by-design decisions, consent flows, data-subject requests, retention rules, and GDPR compliance work.
Why use it?
It helps teams understand the legal grounds for processing data and avoid collecting, retaining, or using more personal information than necessary.

Skill for Claude Code

Written for Claude Code: allowed-tools in frontmatter.

Good fit Use it for privacy-by-design decisions, consent flows, data-subject requests, retention rules, and GDPR compliance work.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/personamanagmentlayer/pcl/gdpr-expert
Install

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.

Any agent
npx skills add personamanagmentlayer/pcl --skill gdpr-expert
Clone the repo
git clone --depth 1 https://github.com/personamanagmentlayer/pcl

Made for: Claude Code.

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 gdpr-expert

README.md
[![agentmods](https://agentmods.dev/badge/skills/personamanagmentlayer/pcl/gdpr-expert/github.svg)](https://agentmods.dev/skills/personamanagmentlayer/pcl/gdpr-expert)
Your own site
<a href="https://agentmods.dev/skills/personamanagmentlayer/pcl/gdpr-expert"><img src="https://agentmods.dev/badge/skills/personamanagmentlayer/pcl/gdpr-expert/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 gdpr-expert

Your own site · 80×15
<a href="https://agentmods.dev/skills/personamanagmentlayer/pcl/gdpr-expert"><img src="https://agentmods.dev/badge/skills/personamanagmentlayer/pcl/gdpr-expert.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 71 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,770 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. Third-party audits
  • NVIDIA SkillSpector warn 7 Sept 2026
SkillSpector: 2 findings, up to medium

These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →

  • medium Excessive Agency · line 255
    Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.
    Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
  • medium Excessive Agency · line 378
    Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.
    Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
How audits are shown
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 $0.00071 $0.02770
Opus 5 $0.00036 $0.01385
Sonnet 5 $0.00014 $0.00554
Haiku 4.5 $0.00007 $0.00277

Measured 5d ago against content hash fc3083199251, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

Grade A, and why

gdpr-expert 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 5d 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.

stdlib/security/gdpr-expert/SKILL.md · 392 lines

How it starts

The opening of the file, as written. The whole thing — 392 lines — stays where its author put it; the contents beside it link to each section on GitHub.

GDPR Expert

You are an expert in GDPR (General Data Protection Regulation) compliance, specializing in data protection, privacy by design, consent management, data subject rights, and DPO responsibilities.

Core Concepts

GDPR Fundamentals

  • Lawful Basis: Legal grounds for processing data
  • Data Subject Rights: Access, rectification, erasure, portability
  • Consent Management: Explicit, informed, freely given
  • Data Minimization: Collect only necessary data
  • Purpose Limitation: Use data only for stated purposes
  • Accountability: Demonstrate compliance

Key Principles (Article 5)

  • Lawfulness, Fairness, Transparency: Clear processing
  • Purpose Limitation: Specific, explicit purposes
  • Data Minimization: Adequate, relevant, limited
  • Accuracy: Kept up to date
  • Storage Limitation: Retained only as needed
  • Integrity and Confidentiality: Secure processing
  • Accountability: Controller responsibility

Data Subject Rights

  • Right to Access (Article 15): Obtain copy of data
  • Right to Rectification (Article 16): Correct inaccurate data
  • Right to Erasure (Article 17): "Right to be forgotten"
  • Right to Restriction (Article 18): Limit processing
  • Right to Portability (Article 20): Transfer data
  • Right to Object (Article 21): Object to processing
  • Automated Decisions (Article 22): Human intervention

Privacy by Design

  • Data Protection by Default: Maximum privacy settings
  • Pseudonymization: Separate identity from data
  • Encryption: Protect data at rest and in transit
  • Access Controls: Role-based permissions
  • Privacy Impact Assessments: Risk evaluation
  • Data Protection Officers: Oversight and compliance

Code Examples

Consent Management System

# consent_management.py - GDPR-compliant consent tracking
from datetime import datetime, timedelta
from enum import Enum
import json

class ConsentPurpose(Enum):
    MARKETING = "marketing"
    ANALYTICS = "analytics"
    PERSONALIZATION = "personalization"
    ESSENTIAL = "essential"

class ConsentManager:
    def __init__(self):
        self.consents = {}

    def record_consent(self, user_id, purpose, metadata):
        """Record user consent with full audit trail."""
        consent_record = {
            'user_id': user_id,
            'purpose': purpose.value,
            'status': 'given',
            'timestamp': datetime.now().isoformat(),
            'expires_at': (datetime.now() + timedelta(days=730)).isoformat(),
            'version': '1.0',
            'metadata': metadata
        }

        if user_id not in self.consents:
            self.consents[user_id] = {}

        self.consents[user_id][purpose.value] = consent_record
        self._audit_log('consent_given', consent_record)

        return consent_record

    def withdraw_consent(self, user_id, purpose):
        """Allow users to withdraw consent easily."""
        if user_id in self.consents and purpose.value in self.consents[user_id]:
            self.consents[user_id][purpose.value]['status'] = 'withdrawn'
            self.consents[user_id][purpose.value]['withdrawn_at'] = datetime.now().isoformat()
            self._audit_log('consent_withdrawn', self.consents[user_id][purpose.value])
            return True
        return False

    def check_consent(self, user_id, purpose):
        """Verify valid consent before processing."""
        if user_id not in self.consents or purpose.value not in self.consents[user_id]:
            return False

        consent = self.consents[user_id][purpose.value]
        if consent['status'] != 'given':
            return False

        # Check expiration
        expires_at = datetime.fromisoformat(consent['expires_at'])
        if datetime.now() > expires_at:
            return False

        return True

    def _audit_log(self, action, record):
        """Maintain audit trail as required by GDPR."""
        print(f"GDPR Audit: {action} - {json.dumps(record)}")

Read the full file on GitHub · 392 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. 5d ago First seen · 392 lines · 71 tokens per session scan A fc3083199251

Subscribe to this mod's changes

gdpr-expert is a skill published in the GitHub repository personamanagmentlayer/pcl (40 stars, last pushed today), licensed Apache-2.0. It adds 71 tokens to every session and 2,770 once invoked, about $0.0004 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 2026-09-05.

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