tomevault-io 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.mdfor detailed patterns and examples.
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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