Agent skill

Gdpr Compliance

by sickn33 in sickn33/agentic-awesome-skills

Implement GDPR data protection requirements. An agent skill from sickn33/agentic-awesome-skills.

MITAuto-check passedLegal & Compliance

Install Gdpr Compliance

skills CLI
$ npx skills add sickn33/agentic-awesome-skills --skill gdpr-compliance -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install sickn33/agentic-awesome-skills gdpr-compliance --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/sickn33/agentic-awesome-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/gdpr-compliance .claude/skills/gdpr-compliance && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
gdpr-compliance
GitHub stars
47k
Used in
2 other repos
Token cost
~4.2k tokens
SKILL.md length
161 words
Files
2 (incl. references)
Skills in repo
1,497
Repo updated
First seen
Licence
MIT

At a glance

Implement GDPR data protection requirements. An agent skill from sickn33/agentic-awesome-skills.

  • Processing EU personal data
  • SKILL.md covers Key Principles and Legal Bases, Data Mapping Template (Records…, Consent Management… and Data Subject Access Request…, plus 4 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
  • Tasks that involve Privacy and GDPR

What it does

Gdpr Compliance is an agent skill from sickn33/agentic-awesome-skills. Implement GDPR data protection requirements. Configure consent management, data subject rights, and privacy by design. Use when processing EU personal data.

Its SKILL.md is about 4.2k tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files, including reference files (for example `references/details.md`). Compatibility notes: Checklist and framework guidance; no privileged tooling required. Apply controls through your own change process.

It sits in Legal & Compliance, covering Privacy and GDPR. The repository describes itself as: AAS Core is the local, agent-first control plane for complete catalog discovery, agent-owned selection, stack validation, and planning, backed by 2,400+ agentic skills. Includes… The licence is MIT.

When your agent uses it

  • Processing EU personal data
  • Tasks that involve Privacy and GDPR

Example prompts

  • “/gdpr-compliance”

Requirements

  • Python 3
  • Compatibility (from SKILL.md): Checklist and framework guidance; no privileged tooling required. Apply controls through your own change process.

What it can do on your machine

Read from SKILL.md and the folder at commit b84d35a. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are yaml, python and markdown).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Links to these hosts (documentation or services it may open):

    • github.com

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

  • Compatibility

    Checklist and framework guidance; no privileged tooling required. Apply controls through your own change process.

    From compatibility in the SKILL.md frontmatter.

Context cost

Gdpr Compliance loads about 4.2k tokens when it runs, and up to ~5.6k if it reads all its reference files. Until then it costs about 43 tokens; SKILL.md has 161 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~43
When it runs · the whole SKILL.md, loaded when a task matches
~4.2k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~5.6k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from sickn33/agentic-awesome-skills at commit b84d35a, republished under its MIT licence (© sickn33). 161 words, ~4,162 tokens.

Download SKILL.mdSave it as .claude/skills/gdpr-compliance/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
gdpr-compliance
description
Implement GDPR data protection requirements. Configure consent management, data subject rights, and privacy by design. Use when processing EU personal data.
compatibility
Checklist and framework guidance; no privileged tooling required. Apply controls through your own change process.
category
security
risk
safe
source
https://github.com/BagelHole/DevOps-Security-Agent-Skills
source_repo
BagelHole/DevOps-Security-Agent-Skills
source_type
community
date_added
2026-09-20
license
MIT
license_source
https://github.com/BagelHole/DevOps-Security-Agent-Skills/blob/main/LICENSE
metadata.author
devops-skills
metadata.version
1.0

GDPR Compliance

Implement General Data Protection Regulation requirements for organizations that process personal data of EU/EEA residents, covering lawful processing, data subject rights, and technical safeguards.

yaml
gdpr_principles:
  article_5:
    lawfulness_fairness_transparency:
      description: "Process data lawfully, fairly, and transparently"
      implementation:
        - Document legal basis for every processing activity
        - Provide clear privacy notices
        - No hidden or deceptive data collection

    purpose_limitation:
      description: "Collect for specified, explicit, and legitimate purposes"
      implementation:
        - Define purpose before collection
        - Do not repurpose data without new legal basis
        - Document all processing purposes in ROPA

    data_minimization:
      description: "Adequate, relevant, and limited to what is necessary"
      implementation:
        - Collect only required fields
        - Review data models for unnecessary fields
        - Remove optional fields that are not used

    accuracy:
      description: "Accurate and kept up to date"
      implementation:
        - Provide self-service profile editing
        - Implement data validation at point of entry
        - Schedule regular data quality reviews

    storage_limitation:
      description: "Kept no longer than necessary"
      implementation:
        - Define retention periods per data category
        - Automate deletion when retention expires
        - Document retention schedule

    integrity_and_confidentiality:
      description: "Appropriate security measures"
      implementation:
        - Encryption at rest and in transit
        - Access controls and audit logging
        - Pseudonymization where appropriate

    accountability:
      description: "Demonstrate compliance"
      implementation:
        - Maintain Records of Processing Activities
        - Conduct DPIAs for high-risk processing
        - Appoint DPO if required

legal_bases:
  article_6:
    consent: "Freely given, specific, informed, unambiguous"
    contract: "Necessary for performance of a contract"
    legal_obligation: "Required by EU or member state law"
    vital_interests: "Protect life of data subject or another person"
    public_interest: "Task carried out in public interest"
    legitimate_interest: "Legitimate interest not overridden by data subject rights"

Data Mapping Template (Records of Processing Activities)

yaml
# Record of Processing Activities (ROPA) - Article 30
processing_activity:
  name: "Customer Account Management"
  controller: "Example Corp, 123 Main St, Dublin, Ireland"
  dpo_contact: "dpo@example.com"
  purpose: "Manage customer accounts, provide services, handle billing"
  legal_basis: "Contract (Art. 6(1)(b))"
  categories_of_data_subjects:
    - Customers
    - Prospective customers
  categories_of_personal_data:
    - Name, email, phone number
    - Billing address
    - Payment information (tokenized)
    - Service usage data
    - Support ticket history
  special_categories: "None"
  recipients:
    - Payment processor (Stripe) - processor
    - Email service (SendGrid) - processor
    - Cloud hosting (AWS) - processor
  international_transfers:
    - Destination: United States
      Safeguard: "Standard Contractual Clauses (SCCs)"
      TIA_completed: true
  retention_period: "Account data retained for duration of contract + 7 years for legal obligations"
  security_measures:
    - AES-256 encryption at rest
    - TLS 1.3 in transit
    - Role-based access control
    - Audit logging of all access
  dpia_required: false
  last_reviewed: "2024-06-01"

# Template for each processing activity
processing_activity_template:
  name: ""
  controller: ""
  joint_controller: ""  # if applicable
  processor: ""  # if acting as processor
  dpo_contact: ""
  purpose: ""
  legal_basis: ""  # consent | contract | legal_obligation | vital_interests | public_interest | legitimate_interest
  legitimate_interest_assessment: ""  # if legitimate interest
  categories_of_data_subjects: []
  categories_of_personal_data: []
  special_categories: ""  # Art. 9 data
  recipients: []
  international_transfers: []
  retention_period: ""
  security_measures: []
  dpia_required: false
  date_added: ""
  last_reviewed: ""
python
"""
Consent management system implementing GDPR Article 7 requirements.
Consent must be freely given, specific, informed, and unambiguous.
"""
from datetime import datetime, timezone
from enum import Enum
import json
import hashlib


class ConsentPurpose(Enum):
    MARKETING_EMAIL = "marketing_email"
    MARKETING_SMS = "marketing_sms"
    ANALYTICS = "analytics"
    PERSONALIZATION = "personalization"
    THIRD_PARTY_SHARING = "third_party_sharing"
    PROFILING = "profiling"


class ConsentManager:
    def __init__(self, db):
        self.db = db

    def record_consent(self, user_id, purpose, granted, source,
                       privacy_policy_version, ip_address=None):
        """Record a consent decision with full audit trail."""
        consent_record = {
            "user_id": user_id,
            "purpose": purpose.value,
            "granted": granted,
            "timestamp": datetime.now(timezone.utc).isoformat(),
            "source": source,  # e.g., "web_signup", "preference_center", "cookie_banner"
            "privacy_policy_version": privacy_policy_version,
            "ip_address": ip_address,
            "withdrawal_timestamp": None,
        }
        # Store with immutable audit trail
        consent_record["record_hash"] = hashlib.sha256(
            json.dumps(consent_record, sort_keys=True).encode()
        ).hexdigest()
        self.db.consent_records.insert(consent_record)
        return consent_record

    def withdraw_consent(self, user_id, purpose):
        """Process consent withdrawal - must be as easy as giving consent."""
        record = self.record_consent(
            user_id=user_id,
            purpose=purpose,
            granted=False,
            source="withdrawal",
            privacy_policy_version="N/A",
        )
        # Trigger downstream actions
        self._notify_processors(user_id, purpose, "withdrawn")
        self._stop_processing(user_id, purpose)
        return record

    def get_consent_status(self, user_id, purpose):
        """Get current consent status for a specific purpose."""
        latest = self.db.consent_records.find_one(
            {"user_id": user_id, "purpose": purpose.value},
            sort=[("timestamp", -1)]
        )
        return latest["granted"] if latest else False

    def get_all_consents(self, user_id):
        """Get all consent records for a user (for DSAR response)."""
        return list(self.db.consent_records.find(
            {"user_id": user_id},
            sort=[("timestamp", -1)]
        ))

    def export_consent_proof(self, user_id, purpose):
        """Export verifiable consent proof for accountability."""
        records = list(self.db.consent_records.find(
            {"user_id": user_id, "purpose": purpose.value},
            sort=[("timestamp", 1)]
        ))
        return {
            "user_id": user_id,
            "purpose": purpose.value,
            "consent_history": records,
            "current_status": self.get_consent_status(user_id, purpose),
            "exported_at": datetime.now(timezone.utc).isoformat(),
        }

    def _notify_processors(self, user_id, purpose, action):
        """Notify downstream processors of consent change."""
        pass  # Implement webhook/API calls to processors

    def _stop_processing(self, user_id, purpose):
        """Immediately stop processing for withdrawn consent."""
        pass  # Implement processing halt logic

Data Subject Access Request (DSAR) Procedures

yaml
dsar_workflow:
  step_1_receive:
    actions:
      - Log the request with timestamp and channel received
      - Assign unique tracking ID
      - Acknowledge receipt within 3 business days
    identity_verification:
      - Verify identity before providing any data
      - Use existing authentication where possible
      - Request additional proof if necessary (but not excessive)
    sla: "Must respond within 30 days (extendable to 90 days for complex requests)"

  step_2_assess:
    actions:
      - Determine request type (access, rectification, erasure, portability, etc.)
      - Identify all systems containing the individual's data
      - Check for lawful grounds to refuse (legal obligations, etc.)
      - Assess if extension is needed (complex or numerous requests)

  step_3_collect:
    systems_to_search:
      - Primary application database
      - CRM system
      - Email marketing platform
      - Analytics systems
      - Customer support tickets
      - Backup systems (if practically retrievable)
      - Log files containing PII
      - Third-party processors (request from each)

  step_4_respond:
    access_request:
      - Provide copy of all personal data in commonly used electronic format
      - Include processing purposes, categories, recipients, retention periods
      - Include source of data if not collected from the individual
      - Include information about automated decision-making
    rectification_request:
      - Update data in all systems
      - Notify all recipients of the correction
    erasure_request:
      - Delete data from all active systems
      - Remove from backups where technically feasible
      - Notify all processors and recipients
      - Document what was deleted and any retained data with legal basis
    portability_request:
      - Provide data in structured, machine-readable format (JSON/CSV)
      - Include only data provided by the data subject
      - Transfer directly to another controller if requested and feasible

  step_5_close:
    actions:
      - Send response to data subject
      - Document the entire handling process
      - Archive DSAR record for accountability
      - Update data mapping if new data stores discovered
python
"""DSAR automation - data collection across systems."""
import json
from datetime import datetime, timezone


class DSARProcessor:
    def __init__(self, data_sources):
        self.data_sources = data_sources  # Dict of system_name: DataSource

    def process_access_request(self, user_identifier):
        """Collect all personal data across registered systems."""
        collected_data = {
            "request_id": f"DSAR-{datetime.now(timezone.utc).strftime('%Y%m%d%H%M%S')}",
            "generated_at": datetime.now(timezone.utc).isoformat(),
            "data_subject": user_identifier,
            "systems": {},
        }

        for system_name, source in self.data_sources.items():
            try:
                data = source.extract_user_data(user_identifier)
                collected_data["systems"][system_name] = {
                    "status": "collected",
                    "record_count": len(data) if isinstance(data, list) else 1,
                    "data": data,
                }
            except Exception as e:
                collected_data["systems"][system_name] = {
                    "status": "error",
                    "error": str(e),
                }

        return collected_data

    def process_erasure_request(self, user_identifier):
        """Delete personal data across all systems (right to erasure)."""
        results = {
            "request_id": f"ERASE-{datetime.now(timezone.utc).strftime('%Y%m%d%H%M%S')}",
            "data_subject": user_identifier,
            "systems": {},
        }

        for system_name, source in self.data_sources.items():
            try:
                deleted = source.delete_user_data(user_identifier)
                retained = source.get_retained_data(user_identifier)
                results["systems"][system_name] = {
                    "status": "deleted",
                    "records_deleted": deleted,
                    "retained_data": retained,  # Data kept for legal obligations
                    "retention_basis": source.retention_legal_basis,
                }
            except Exception as e:
                results["systems"][system_name] = {
                    "status": "error",
                    "error": str(e),
                }

        return results

    def export_portable_data(self, user_identifier, format="json"):
        """Export data in machine-readable format for portability."""
        data = self.process_access_request(user_identifier)
        if format == "json":
            return json.dumps(data, indent=2, default=str)
        elif format == "csv":
            return self._convert_to_csv(data)
        raise ValueError(f"Unsupported format: {format}")

Data Processing Agreement (DPA) Requirements

yaml
dpa_requirements:
  mandatory_clauses:
    article_28:
      - Subject matter, duration, nature, and purpose of processing
      - Type of personal data and categories of data subjects
      - Obligations and rights of the controller
      - Processing only on documented instructions from controller
      - Confidentiality obligations on processor personnel
      - Appropriate technical and organizational security measures
      - Conditions for engaging sub-processors (prior authorization)
      - Assistance with data subject rights requests
      - Assistance with security obligations (Art. 32-36)
      - Deletion or return of data after service ends
      - Audit and inspection rights for the controller

  sub_processor_management:
    - [ ] List of current sub-processors provided by processor
    - [ ] Notification mechanism for new sub-processors (30-day notice)
    - [ ] Right to object to new sub-processors
    - [ ] Sub-processors bound by same data protection obligations
    - [ ] Processor remains liable for sub-processor compliance

  international_transfers:
    mechanisms:
      - Standard Contractual Clauses (SCCs) - most common
      - Binding Corporate Rules (BCRs) - intra-group transfers
      - Adequacy decision (countries deemed adequate by EC)
      - Derogations for specific situations (explicit consent, contract necessity)
    transfer_impact_assessment:
      - [ ] Assess laws of the destination country
      - [ ] Evaluate effectiveness of safeguards
      - [ ] Document supplementary measures if needed
      - [ ] Review periodically for legal changes

  dpa_registry:
    track_per_processor:
      - Processor name and contact details
      - DPA execution date
      - Data types processed
      - Sub-processors and their locations
      - SCC version used for international transfers
      - TIA completion date
      - Next review date

Contents

When to Use

  • Processing personal data of EU/EEA residents in any capacity
  • Building consent management and preference centers
  • Implementing Data Subject Access Request (DSAR) workflows
  • Conducting Data Protection Impact Assessments (DPIAs)
  • Setting up data processing agreements with third-party processors
  • Designing systems with privacy by design and by default principles

Limitations

  • Guidance and checklists only; not legal advice and not a substitute for a qualified auditor.
  • Docs-only import: upstream templates and scripts not bundled.
Example
markdown
Map this skill's control checklist to our current evidence and list gaps.

Adapted from BagelHole/DevOps-Security-Agent-Skills (MIT); frontmatter, When to Use/Limitations, and safety boundaries added for upstream compliance. Docs-only import: helper scripts and templates not bundled.

© sickn33, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 1 other file (references) in skills/gdpr-compliance of sickn33/agentic-awesome-skills.

  • SKILL.md
  • references/details.md

Open the folder on GitHubat commit b84d35a

Used in 2 other repositories

We found 6 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 2 other GitHub owners. This page covers the copy in sickn33/agentic-awesome-skills, which our catalogue first saw on October 7, 2026.

Compare with similar skills

Gdpr Compliance next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Gdpr Compliance compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Gdpr Compliance this skillsickn33/agentic-awesome-skills47k2 repos~4.2kAutomated safety check: PassMIT
C15tc15t/c15t1.9k1 repos~1.6kAutomated safety check: PassApache-2.0
HIPAA Safe Harbor Coverage Auditmaziyarpanahi/openmed5.5k—~1.7kAutomated safety check: PassApache-2.0
Korean Privacy Termskimlawtech/korean-privacy-terms587—~2.9kAutomated safety check: PassApache-2.0
Gdpr ComplianceSushegaad/Claude-Skills-Governance-Risk-and-Compliance9461 repos~3.9kAutomated safety check: PassMIT
Hipaa ComplianceSushegaad/Claude-Skills-Governance-Risk-and-Compliance9461 repos~2.3kAutomated safety check: PassMIT

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Questions about Gdpr Compliance

What does Gdpr Compliance do?

Implement GDPR data protection requirements. An agent skill from sickn33/agentic-awesome-skills. Gdpr Compliance is an agent skill from sickn33/agentic-awesome-skills. Implement GDPR data protection requirements.

When should I use Gdpr Compliance?

Gdpr Compliance fits situations like: processing EU personal data; tasks that involve Privacy and GDPR.

How do I install Gdpr Compliance in Claude Code?

Run `npx skills add sickn33/agentic-awesome-skills --skill gdpr-compliance -a claude-code`. Or copy the skill folder (skills/gdpr-compliance in sickn33/agentic-awesome-skills) into .claude/skills/gdpr-compliance in your project. Claude Code loads it when a task matches its description.

How do I install Gdpr Compliance in Codex?

Run `npx skills add sickn33/agentic-awesome-skills --skill gdpr-compliance -a codex`. Or copy the skill folder (skills/gdpr-compliance in sickn33/agentic-awesome-skills) into .agents/skills/gdpr-compliance in your project. Codex loads it when a task matches its description.

Can I use Gdpr Compliance in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add sickn33/agentic-awesome-skills --skill gdpr-compliance -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/gdpr-compliance, .gemini/skills/gdpr-compliance, .github/skills/gdpr-compliance and .opencode/skills/gdpr-compliance in your project.

What does Gdpr Compliance need to run?

SKILL.md names no scripts, command-line tools or credentials: Gdpr Compliance is instructions for the agent only. Our summary lists: Python 3. Compatibility (from SKILL.md): Checklist and framework guidance; no privileged tooling required. Apply controls through your own change process..

Does Gdpr Compliance access the network?

SKILL.md names 1 domain. As links in the text: github.com. This is read from the text; nothing was executed.

Is Gdpr Compliance safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Gdpr Compliance use?

Gdpr Compliance is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Gdpr Compliance use?

About 4.2k tokens (SKILL.md is roughly 17k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 1.4k tokens, read only when the agent opens those files.

What are the alternatives to Gdpr Compliance?

Skills that share tags, products or a category with Gdpr Compliance: C15t (c15t/c15t, 1.9k stars), HIPAA Safe Harbor Coverage Audit (maziyarpanahi/openmed, 5.5k stars), Korean Privacy Terms (kimlawtech/korean-privacy-terms, 587 stars) and Gdpr Compliance (Sushegaad/Claude-Skills-Governance-Risk-and-Compliance, 946 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Gdpr Compliance?

sickn33 (a GitHub user) maintains it in sickn33/agentic-awesome-skills, which has 47,405 GitHub stars. The repository holds 1,497 skills in this directory. The repository was last updated on October 9, 2026.

Source: sickn33/agentic-awesome-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.