GDPR Data Handling

GDPR Data Handling

Practical implementation guide for GDPR-compliant data processing, consent management, and privacy controls.

Use this skill when

• 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

Do not use this skill when

• The task is unrelated to gdpr data handling
• You need a different domain or tool outside this scope

Instructions

• Clarify goals, constraints, and required inputs.
• Apply relevant best practices and validate outcomes.
• Provide actionable steps and verification.
• If detailed examples are required, open resources/​implementation-playbook.md.

Resources

• resources/​implementation-playbook.md for detailed patterns and examples.

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AGI Framework Integration

Adapted for @techwavedev/​agi-agent-kit Original source: antigravity-awesome-skills

Memory-First Protocol

Cache data schemas, transformation rules, and query patterns. BM25 excels at finding specific column names, table references, and SQL patterns.

# Check for prior data engineering context before starting
python3 execution/​memory_manager.py auto --query "data processing patterns and pipeline configurations for Gdpr Data Handling"

Storing Results

After completing work, store data engineering decisions for future sessions:

python3 execution/​memory_manager.py store \
  --content "Data pipeline: ETL from PostgreSQL to Qdrant, 50K records/​batch, incremental sync via updated_at" \
  --type technical --project <project> \
  --tags gdpr-data-handling data

Multi-Agent Collaboration

Share data schema changes with backend and frontend agents so they update their models accordingly.

python3 execution/​cross_agent_context.py store \
  --agent "<your-agent>" \
  --action "Data pipeline implemented — ETL processing with validation, deduplication, and error recovery" \
  --project <project>

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