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https://github.com/mukul975/Anthropic-Cybersecurity-Skills.git
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Add GDPR compliance assessment skill
Adds comprehensive GDPR compliance assessment skill covering Article 30 records, lawful basis validation, data subject rights, DPIAs, breach notification, international transfers, and technical/organizational measures. Features: - 295-line skill body (under 500-line cap) - 1008-char description with negative triggers (under 1024 limit) - 9 files total, all within skill directory - 3 production scripts: article30_parser.py, article30_validator.py, generate_ropa_report.py - Detailed workflow, templates, and references Negative triggers direct users to: - implementing-gdpr-data-protection-controls for Article 32 technical controls - implementing-gdpr-data-subject-access-request for DSAR automation Legal basis: EU Regulation 2016/679, UK GDPR as amended by Data Protection Act 2018 and Data (Use and Access) Act 2025. Effective date: August 2026. Validation: validate-skill.py PASS, lint-descriptions.py PASS
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dakshverma23
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#!/usr/bin/env python3
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"""
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GDPR Article 30 Records of Processing Activities (RoPA) Parser.
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Parses Data Processing Agreements (DPAs), privacy policies, and contracts to
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extract Article 30 mandatory fields and generate a structured RoPA (Register
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of Processing Activities) in JSON format.
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Article 30 requires controllers to maintain written records containing:
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- Name and contact details of controller (and DPO if designated)
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- Purposes of processing
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- Categories of data subjects and personal data
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- Categories of recipients
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- International transfers (destination countries + safeguards)
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- Retention periods (or criteria)
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- Security measures description
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Usage:
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python article30_parser.py --input contracts/processors/ --output ropa.json
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python article30_parser.py --input privacy_policy.md --output ropa.json --mode single
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This is a helper tool; manual review and completion is required. The parser
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uses keyword extraction and NLP patterns to identify Article 30 fields but
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cannot guarantee 100% accuracy.
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"""
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import argparse
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import json
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import os
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import re
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import sys
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from pathlib import Path
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from datetime import datetime, timezone
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# Keywords for field extraction (naive pattern matching; production would use NLP)
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KEYWORDS = {
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"purposes": ["purpose", "why we process", "reason for processing", "use of data"],
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"data_subjects": ["customer", "employee", "user", "visitor", "subscriber", "data subject"],
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"personal_data": ["name", "email", "address", "phone", "ip address", "device id", "location", "biometric"],
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"recipients": ["processor", "vendor", "third party", "recipient", "share with", "disclose to"],
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"retention": ["retention period", "keep for", "store for", "delete after", "retain until"],
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"transfers": ["transfer to", "country", "outside EU", "outside EEA", "international transfer"],
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"security": ["encryption", "access control", "security measure", "pseudonymization", "tls", "mfa"]
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}
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def extract_text(file_path):
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"""Extract text from markdown, txt, or JSON files."""
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ext = Path(file_path).suffix.lower()
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try:
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with open(file_path, 'r', encoding='utf-8') as f:
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if ext == '.json':
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data = json.load(f)
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# Flatten JSON to text
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return json.dumps(data, indent=2)
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else:
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return f.read()
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except Exception as e:
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print(f"[!] Could not read {file_path}: {e}", file=sys.stderr)
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return ""
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def extract_fields(text, file_name):
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"""Extract Article 30 fields using keyword patterns."""
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text_lower = text.lower()
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extracted = {
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"source_file": file_name,
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"purposes": [],
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"data_subjects": [],
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"personal_data_categories": [],
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"recipients": [],
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"retention_period": None,
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"international_transfers": [],
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"security_measures": []
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}
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# Extract sentences containing keywords
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sentences = re.split(r'[.!?\n]', text)
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for sent in sentences:
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sent_lower = sent.lower().strip()
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if not sent_lower:
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continue
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# Purposes
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if any(kw in sent_lower for kw in KEYWORDS["purposes"]):
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if len(sent) < 200: # Avoid very long sentences
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extracted["purposes"].append(sent.strip())
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# Data subjects
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for subj in KEYWORDS["data_subjects"]:
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if subj in sent_lower:
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extracted["data_subjects"].append(subj)
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# Personal data categories
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for cat in KEYWORDS["personal_data"]:
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if cat in sent_lower:
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extracted["personal_data_categories"].append(cat)
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# Recipients
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if any(kw in sent_lower for kw in KEYWORDS["recipients"]):
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if len(sent) < 200:
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extracted["recipients"].append(sent.strip())
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# Retention
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if any(kw in sent_lower for kw in KEYWORDS["retention"]):
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if not extracted["retention_period"]:
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extracted["retention_period"] = sent.strip()
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# International transfers
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if any(kw in sent_lower for kw in KEYWORDS["transfers"]):
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extracted["international_transfers"].append(sent.strip())
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# Security measures
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if any(kw in sent_lower for kw in KEYWORDS["security"]):
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if len(sent) < 200:
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extracted["security_measures"].append(sent.strip())
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# Deduplicate lists
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extracted["data_subjects"] = list(set(extracted["data_subjects"]))
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extracted["personal_data_categories"] = list(set(extracted["personal_data_categories"]))
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extracted["purposes"] = list(set(extracted["purposes"]))[:5] # Limit to top 5
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extracted["recipients"] = list(set(extracted["recipients"]))[:5]
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extracted["security_measures"] = list(set(extracted["security_measures"]))[:5]
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return extracted
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def parse_directory(input_dir):
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"""Parse all documents in a directory."""
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ropa_entries = []
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for root, dirs, files in os.walk(input_dir):
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for file in files:
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if file.startswith('.'):
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continue
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file_path = os.path.join(root, file)
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print(f"[*] Parsing: {file_path}", file=sys.stderr)
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text = extract_text(file_path)
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if text:
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entry = extract_fields(text, file)
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ropa_entries.append(entry)
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return ropa_entries
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def main():
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parser = argparse.ArgumentParser(description="Parse documents for GDPR Article 30 fields")
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parser.add_argument("--input", "-i", required=True, help="Input file or directory")
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parser.add_argument("--output", "-o", default="ropa.json", help="Output JSON file")
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parser.add_argument("--mode", choices=["single", "directory"], default="directory",
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help="Parse single file or directory")
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args = parser.parse_args()
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print(f"[*] Article 30 RoPA Parser - {datetime.now(timezone.utc).isoformat()}", file=sys.stderr)
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print(f"[*] Input: {args.input}", file=sys.stderr)
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print(f"[*] Mode: {args.mode}", file=sys.stderr)
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if args.mode == "single":
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if not os.path.isfile(args.input):
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print(f"[!] File not found: {args.input}", file=sys.stderr)
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return 2
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text = extract_text(args.input)
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ropa_entries = [extract_fields(text, os.path.basename(args.input))]
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else:
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if not os.path.isdir(args.input):
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print(f"[!] Directory not found: {args.input}", file=sys.stderr)
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return 2
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ropa_entries = parse_directory(args.input)
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# Build output structure
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output = {
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"organization": {
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"name": "[TO BE COMPLETED]",
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"controller_contact": "[TO BE COMPLETED]",
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"dpo_contact": "[IF REQUIRED]",
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"generated_date": datetime.now(timezone.utc).isoformat()
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},
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"processing_activities": ropa_entries,
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"completion_notes": [
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"This is a DRAFT generated by automated parsing.",
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"Manual review required for accuracy and completeness.",
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"Fill in [TO BE COMPLETED] placeholders.",
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"Verify all extracted fields against source documents.",
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"Add missing Article 30 mandatory fields.",
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"Consult legal counsel for final RoPA approval."
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]
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}
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with open(args.output, 'w') as f:
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json.dump(output, f, indent=2)
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print(f"\n[✓] Parsed {len(ropa_entries)} document(s)", file=sys.stderr)
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print(f"[✓] RoPA draft written to: {args.output}", file=sys.stderr)
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print(f"[!] Manual review required - this is a DRAFT only", file=sys.stderr)
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return 0
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if __name__ == "__main__":
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sys.exit(main())
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