
Gdpr Data Handling
- 12.1k installs
- 38.3k repo stars
- Updated July 22, 2026
- wshobson/agents
GDPR-compliant data handling means lawfully processing EU personal data under Article 6 bases, honoring all six data subject rights within 30 days, encrypting PII, implementing consent (opt-in, specific, documented), cat
About
This skill provides a practical guide for building GDPR-compliant data systems covering consent management, data subject rights (access, erasure, portability), and privacy-by-design patterns. Developers use it when building systems that process EU personal data, implementing privacy controls, or conducting compliance reviews. Key workflows include documenting processing activities and legal bases under Article 6, implementing the six data subject rights with 30-day response SLAs, categorizing personal data by protection level (basic, sensitive Art. 9, criminal Art. 10, children's), and enforcing encryption and access controls. The skill emphasizes data minimization, continuous audits, and proper consent implementation (opt-in, non-bundled, separately documented).
- Categorize personal data into four protection tiers (basic, sensitive Art. 9, criminal Art. 10, children's) with aligned
- Implement six data subject rights (access, rectification, erasure, restriction, portability, objection) with mandatory 1
- Document lawful processing bases from Article 6 (consent, contract, legal obligation, vital interest, public interest, l
- Enforce encryption, access controls, and data minimization; prohibit indefinite retention and bundled consent.
- Conduct regular compliance audits and create data processing agreements before EU personal data processing begins.
Gdpr Data Handling by the numbers
- 12,081 all-time installs (skills.sh)
- +195 installs in the week ending Jul 28, 2026 (Skillselion tracking)
- Ranked #38 of 2,209 Security skills by installs in the Skillselion catalog
- Security screen: MEDIUM risk (skills.sh audit)
- Data as of Jul 28, 2026 (Skillselion catalog sync)
gdpr-data-handling capabilities & compatibility
- Capabilities
- categorize personal data by protection level and · implement data subject rights workflows (access, · document lawful processing bases and consent flo · design encryption and access control architectur · create compliance audit checklists and data proc
- Use cases
- security audit · documentation
- Platforms
- macOS · Windows · Linux
- Runs
- Runs locally
- Pricing
- Free
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| Installs | 12.1k |
|---|---|
| repo stars | ★ 38.3k |
| Security audit | 3 / 3 scanners passed |
| Last updated | July 22, 2026 |
| Repository | wshobson/agents ↗ |
What it does
Implement GDPR-compliant data handling with consent management and data subject rights in EU-facing systems.
Who is it for?
SaaS platforms, APIs, and services with EU users or customers; systems handling health, financial, or identity data; organizations seeking privacy-first architecture.
Skip if: Systems with no EU personal data; non-regulated environments; systems not subject to GDPR jurisdiction.
When should I use this skill?
Starting a new system that will process EU data; adding privacy controls to existing products; preparing for compliance audit; designing data retention and consent flows.
What you get
Teams can build and operate systems that process EU personal data with documented consent, automated DSR workflows, categorized data protection, and continuous compliance verification.
- Consent schema
- Audit log model
- Subject-rights handler stubs
By the numbers
- 6 data subject rights must be honored within 1 month
- 4 personal data categories defined by protection level (basic, sensitive, criminal, children's)
- Article 6 specifies 6 lawful bases for processing
Files
GDPR Data Handling
Practical implementation guide for GDPR-compliant data processing, consent management, and privacy controls.
When to Use This Skill
- Building systems that process EU personal data
- Implementing consent management
- Handling data subject requests (DSRs)
- Conducting GDPR compliance reviews
- Designing privacy-first architectures
- Creating data processing agreements
Core Concepts
1. Personal Data Categories
| Category | Examples | Protection Level |
|---|---|---|
| Basic | Name, email, phone | Standard |
| Sensitive (Art. 9) | Health, religion, ethnicity | Explicit consent |
| Criminal (Art. 10) | Convictions, offenses | Official authority |
| Children's | Under 16 data | Parental consent |
2. Legal Bases for Processing
Article 6 - Lawful Bases:
├── Consent: Freely given, specific, informed
├── Contract: Necessary for contract performance
├── Legal Obligation: Required by law
├── Vital Interests: Protecting someone's life
├── Public Interest: Official functions
└── Legitimate Interest: Balanced against rights3. Data Subject Rights
Right to Access (Art. 15) ─┐
Right to Rectification (Art. 16) │
Right to Erasure (Art. 17) │ Must respond
Right to Restrict (Art. 18) │ within 1 month
Right to Portability (Art. 20) │
Right to Object (Art. 21) ─┘Detailed worked examples and patterns
Detailed sections (starting with ## Implementation Patterns) live in references/details.md. Read that file when the navigation summary above is insufficient.
Best Practices
Do's
- Minimize data collection - Only collect what's needed
- Document everything - Processing activities, legal bases
- Encrypt PII - At rest and in transit
- Implement access controls - Need-to-know basis
- Regular audits - Verify compliance continuously
Don'ts
- Don't pre-check consent boxes - Must be opt-in
- Don't bundle consent - Separate purposes separately
- Don't retain indefinitely - Define and enforce retention
- Don't ignore DSARs - 30-day response required
- Don't transfer without safeguards - SCCs or adequacy decisions
gdpr-data-handling — detailed worked examples
Implementation Patterns
Pattern 1: Consent Management
// Consent data model
const consentSchema = {
userId: String,
consents: [
{
purpose: String, // 'marketing', 'analytics', etc.
granted: Boolean,
timestamp: Date,
source: String, // 'web_form', 'api', etc.
version: String, // Privacy policy version
ipAddress: String, // For proof
userAgent: String, // For proof
},
],
auditLog: [
{
action: String, // 'granted', 'withdrawn', 'updated'
purpose: String,
timestamp: Date,
source: String,
},
],
};
// Consent service
class ConsentManager {
async recordConsent(userId, purpose, granted, metadata) {
const consent = {
purpose,
granted,
timestamp: new Date(),
source: metadata.source,
version: await this.getCurrentPolicyVersion(),
ipAddress: metadata.ipAddress,
userAgent: metadata.userAgent,
};
// Store consent
await this.db.consents.updateOne(
{ userId },
{
$push: {
consents: consent,
auditLog: {
action: granted ? "granted" : "withdrawn",
purpose,
timestamp: consent.timestamp,
source: metadata.source,
},
},
},
{ upsert: true },
);
// Emit event for downstream systems
await this.eventBus.emit("consent.changed", {
userId,
purpose,
granted,
timestamp: consent.timestamp,
});
}
async hasConsent(userId, purpose) {
const record = await this.db.consents.findOne({ userId });
if (!record) return false;
const latestConsent = record.consents
.filter((c) => c.purpose === purpose)
.sort((a, b) => b.timestamp - a.timestamp)[0];
return latestConsent?.granted === true;
}
async getConsentHistory(userId) {
const record = await this.db.consents.findOne({ userId });
return record?.auditLog || [];
}
}<!-- GDPR-compliant consent UI -->
<div class="consent-banner" role="dialog" aria-labelledby="consent-title">
<h2 id="consent-title">Cookie Preferences</h2>
<p>
We use cookies to improve your experience. Select your preferences below.
</p>
<form id="consent-form">
<!-- Necessary - always on, no consent needed -->
<div class="consent-category">
<input type="checkbox" id="necessary" checked disabled />
<label for="necessary">
<strong>Necessary</strong>
<span>Required for the website to function. Cannot be disabled.</span>
</label>
</div>
<!-- Analytics - requires consent -->
<div class="consent-category">
<input type="checkbox" id="analytics" name="analytics" />
<label for="analytics">
<strong>Analytics</strong>
<span>Help us understand how you use our site.</span>
</label>
</div>
<!-- Marketing - requires consent -->
<div class="consent-category">
<input type="checkbox" id="marketing" name="marketing" />
<label for="marketing">
<strong>Marketing</strong>
<span>Personalized ads based on your interests.</span>
</label>
</div>
<div class="consent-actions">
<button type="button" id="accept-all">Accept All</button>
<button type="button" id="reject-all">Reject All</button>
<button type="submit">Save Preferences</button>
</div>
<p class="consent-links">
<a href="/privacy-policy">Privacy Policy</a> |
<a href="/cookie-policy">Cookie Policy</a>
</p>
</form>
</div>Pattern 2: Data Subject Access Request (DSAR)
from datetime import datetime, timedelta
from typing import Dict, List, Optional
import json
class DSARHandler:
"""Handle Data Subject Access Requests."""
RESPONSE_DEADLINE_DAYS = 30
EXTENSION_ALLOWED_DAYS = 60 # For complex requests
def __init__(self, data_sources: List['DataSource']):
self.data_sources = data_sources
async def submit_request(
self,
request_type: str, # 'access', 'erasure', 'rectification', 'portability'
user_id: str,
verified: bool,
details: Optional[Dict] = None
) -> str:
"""Submit a new DSAR."""
request = {
'id': self.generate_request_id(),
'type': request_type,
'user_id': user_id,
'status': 'pending_verification' if not verified else 'processing',
'submitted_at': datetime.utcnow(),
'deadline': datetime.utcnow() + timedelta(days=self.RESPONSE_DEADLINE_DAYS),
'details': details or {},
'audit_log': [{
'action': 'submitted',
'timestamp': datetime.utcnow(),
'details': 'Request received'
}]
}
await self.db.dsar_requests.insert_one(request)
await self.notify_dpo(request)
return request['id']
async def process_access_request(self, request_id: str) -> Dict:
"""Process a data access request."""
request = await self.get_request(request_id)
if request['type'] != 'access':
raise ValueError("Not an access request")
# Collect data from all sources
user_data = {}
for source in self.data_sources:
try:
data = await source.get_user_data(request['user_id'])
user_data[source.name] = data
except Exception as e:
user_data[source.name] = {'error': str(e)}
# Format response
response = {
'request_id': request_id,
'generated_at': datetime.utcnow().isoformat(),
'data_categories': list(user_data.keys()),
'data': user_data,
'retention_info': await self.get_retention_info(),
'processing_purposes': await self.get_processing_purposes(),
'third_party_recipients': await self.get_recipients()
}
# Update request status
await self.update_request(request_id, 'completed', response)
return response
async def process_erasure_request(self, request_id: str) -> Dict:
"""Process a right to erasure request."""
request = await self.get_request(request_id)
if request['type'] != 'erasure':
raise ValueError("Not an erasure request")
results = {}
exceptions = []
for source in self.data_sources:
try:
# Check for legal exceptions
can_delete, reason = await source.can_delete(request['user_id'])
if can_delete:
await source.delete_user_data(request['user_id'])
results[source.name] = 'deleted'
else:
exceptions.append({
'source': source.name,
'reason': reason # e.g., 'legal retention requirement'
})
results[source.name] = f'retained: {reason}'
except Exception as e:
results[source.name] = f'error: {str(e)}'
response = {
'request_id': request_id,
'completed_at': datetime.utcnow().isoformat(),
'results': results,
'exceptions': exceptions
}
await self.update_request(request_id, 'completed', response)
return response
async def process_portability_request(self, request_id: str) -> bytes:
"""Generate portable data export."""
request = await self.get_request(request_id)
user_data = await self.process_access_request(request_id)
# Convert to machine-readable format (JSON)
portable_data = {
'export_date': datetime.utcnow().isoformat(),
'format_version': '1.0',
'data': user_data['data']
}
return json.dumps(portable_data, indent=2, default=str).encode()Pattern 3: Data Retention
from datetime import datetime, timedelta
from enum import Enum
class RetentionBasis(Enum):
CONSENT = "consent"
CONTRACT = "contract"
LEGAL_OBLIGATION = "legal_obligation"
LEGITIMATE_INTEREST = "legitimate_interest"
class DataRetentionPolicy:
"""Define and enforce data retention policies."""
POLICIES = {
'user_account': {
'retention_period_days': 365 * 3, # 3 years after last activity
'basis': RetentionBasis.CONTRACT,
'trigger': 'last_activity_date',
'archive_before_delete': True
},
'transaction_records': {
'retention_period_days': 365 * 7, # 7 years for tax
'basis': RetentionBasis.LEGAL_OBLIGATION,
'trigger': 'transaction_date',
'archive_before_delete': True,
'legal_reference': 'Tax regulations require 7 year retention'
},
'marketing_consent': {
'retention_period_days': 365 * 2, # 2 years
'basis': RetentionBasis.CONSENT,
'trigger': 'consent_date',
'archive_before_delete': False
},
'support_tickets': {
'retention_period_days': 365 * 2,
'basis': RetentionBasis.LEGITIMATE_INTEREST,
'trigger': 'ticket_closed_date',
'archive_before_delete': True
},
'analytics_data': {
'retention_period_days': 365, # 1 year
'basis': RetentionBasis.CONSENT,
'trigger': 'collection_date',
'archive_before_delete': False,
'anonymize_instead': True
}
}
async def apply_retention_policies(self):
"""Run retention policy enforcement."""
for data_type, policy in self.POLICIES.items():
cutoff_date = datetime.utcnow() - timedelta(
days=policy['retention_period_days']
)
if policy.get('anonymize_instead'):
await self.anonymize_old_data(data_type, cutoff_date)
else:
if policy.get('archive_before_delete'):
await self.archive_data(data_type, cutoff_date)
await self.delete_old_data(data_type, cutoff_date)
await self.log_retention_action(data_type, cutoff_date)
async def anonymize_old_data(self, data_type: str, before_date: datetime):
"""Anonymize data instead of deleting."""
# Example: Replace identifying fields with hashes
if data_type == 'analytics_data':
await self.db.analytics.update_many(
{'collection_date': {'$lt': before_date}},
{'$set': {
'user_id': None,
'ip_address': None,
'device_id': None,
'anonymized': True,
'anonymized_date': datetime.utcnow()
}}
)Pattern 4: Privacy by Design
class PrivacyFirstDataModel:
"""Example of privacy-by-design data model."""
# Separate PII from behavioral data
user_profile_schema = {
'user_id': str, # UUID, not sequential
'email_hash': str, # Hashed for lookups
'created_at': datetime,
# Minimal data collection
'preferences': {
'language': str,
'timezone': str
}
}
# Encrypted at rest
user_pii_schema = {
'user_id': str,
'email': str, # Encrypted
'name': str, # Encrypted
'phone': str, # Encrypted (optional)
'address': dict, # Encrypted (optional)
'encryption_key_id': str
}
# Pseudonymized behavioral data
analytics_schema = {
'session_id': str, # Not linked to user_id
'pseudonym_id': str, # Rotating pseudonym
'events': list,
'device_category': str, # Generalized, not specific
'country': str, # Not city-level
}
class DataMinimization:
"""Implement data minimization principles."""
@staticmethod
def collect_only_needed(form_data: dict, purpose: str) -> dict:
"""Filter form data to only fields needed for purpose."""
REQUIRED_FIELDS = {
'account_creation': ['email', 'password'],
'newsletter': ['email'],
'purchase': ['email', 'name', 'address', 'payment'],
'support': ['email', 'message']
}
allowed = REQUIRED_FIELDS.get(purpose, [])
return {k: v for k, v in form_data.items() if k in allowed}
@staticmethod
def generalize_location(ip_address: str) -> str:
"""Generalize IP to country level only."""
import geoip2.database
reader = geoip2.database.Reader('GeoLite2-Country.mmdb')
try:
response = reader.country(ip_address)
return response.country.iso_code
except:
return 'UNKNOWN'Pattern 5: Breach Notification
from datetime import datetime
from enum import Enum
class BreachSeverity(Enum):
LOW = "low"
MEDIUM = "medium"
HIGH = "high"
CRITICAL = "critical"
class BreachNotificationHandler:
"""Handle GDPR breach notification requirements."""
AUTHORITY_NOTIFICATION_HOURS = 72
AFFECTED_NOTIFICATION_REQUIRED_SEVERITY = BreachSeverity.HIGH
async def report_breach(
self,
description: str,
data_types: List[str],
affected_count: int,
severity: BreachSeverity
) -> dict:
"""Report and handle a data breach."""
breach = {
'id': self.generate_breach_id(),
'reported_at': datetime.utcnow(),
'description': description,
'data_types_affected': data_types,
'affected_individuals_count': affected_count,
'severity': severity.value,
'status': 'investigating',
'timeline': [{
'event': 'breach_reported',
'timestamp': datetime.utcnow(),
'details': description
}]
}
await self.db.breaches.insert_one(breach)
# Immediate notifications
await self.notify_dpo(breach)
await self.notify_security_team(breach)
# Authority notification required within 72 hours
if self.requires_authority_notification(severity, data_types):
breach['authority_notification_deadline'] = (
datetime.utcnow() + timedelta(hours=self.AUTHORITY_NOTIFICATION_HOURS)
)
await self.schedule_authority_notification(breach)
# Affected individuals notification
if severity.value in [BreachSeverity.HIGH.value, BreachSeverity.CRITICAL.value]:
await self.schedule_individual_notifications(breach)
return breach
def requires_authority_notification(
self,
severity: BreachSeverity,
data_types: List[str]
) -> bool:
"""Determine if supervisory authority must be notified."""
# Always notify for sensitive data
sensitive_types = ['health', 'financial', 'credentials', 'biometric']
if any(t in sensitive_types for t in data_types):
return True
# Notify for medium+ severity
return severity in [BreachSeverity.MEDIUM, BreachSeverity.HIGH, BreachSeverity.CRITICAL]
async def generate_authority_report(self, breach_id: str) -> dict:
"""Generate report for supervisory authority."""
breach = await self.get_breach(breach_id)
return {
'organization': {
'name': self.config.org_name,
'contact': self.config.dpo_contact,
'registration': self.config.registration_number
},
'breach': {
'nature': breach['description'],
'categories_affected': breach['data_types_affected'],
'approximate_number_affected': breach['affected_individuals_count'],
'likely_consequences': self.assess_consequences(breach),
'measures_taken': await self.get_remediation_measures(breach_id),
'measures_proposed': await self.get_proposed_measures(breach_id)
},
'timeline': breach['timeline'],
'submitted_at': datetime.utcnow().isoformat()
}Compliance Checklist
## GDPR Implementation Checklist
### Legal Basis
- [ ] Documented legal basis for each processing activity
- [ ] Consent mechanisms meet GDPR requirements
- [ ] Legitimate interest assessments completed
### Transparency
- [ ] Privacy policy is clear and accessible
- [ ] Processing purposes clearly stated
- [ ] Data retention periods documented
### Data Subject Rights
- [ ] Access request process implemented
- [ ] Erasure request process implemented
- [ ] Portability export available
- [ ] Rectification process available
- [ ] Response within 30-day deadline
### Security
- [ ] Encryption at rest implemented
- [ ] Encryption in transit (TLS)
- [ ] Access controls in place
- [ ] Audit logging enabled
### Breach Response
- [ ] Breach detection mechanisms
- [ ] 72-hour notification process
- [ ] Breach documentation system
### Documentation
- [ ] Records of processing activities (Art. 30)
- [ ] Data protection impact assessments
- [ ] Data processing agreements with vendorsRelated skills
How it compares
Use gdpr-data-handling for code-level consent and audit implementation; pair with legal review for policy wording and jurisdictional scope.
FAQ
What is the response deadline for data subject requests?
30 days from request receipt (extendable to 90 days for complex cases). Document all requests and responses in audit logs.
Can I pre-check consent boxes?
No. Article 7 requires freely given, specific, informed consent. Consent must be opt-in and granular; never bundle separate purposes.
What data needs explicit consent?
Article 9 sensitive categories (health, religion, ethnicity, biometrics) and Article 10 criminal data require explicit consent and official authority respectively.
Is Gdpr Data Handling safe to install?
skills.sh reports 3 of 3 security scanners passed. Review the Security Audits panel on this page before installing in production.