Retention Archival

Retention Archival

Skill Profile

(Select at least one profile to enable specific modules)

• DevOps
• Backend
• Frontend
• AI-RAG
• Security Critical

Overview

Policy and automation for data retention (how long to keep data), archival (move to cold storage), and deletion (permanently remove). This skill enables compliance with GDPR/​CCPA retention limits, cost optimization through cold storage, improved query performance, and legal right to deletion.

Why This Matters

• Compliance: GDPR, CCPA retention limits
• Cost: Cold storage is cheaper than hot storage
• Performance: Less data = faster queries
• Legal: Right to deletion

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Core Concepts & Rules

1. Core Principles

• Follow established patterns and conventions
• Maintain consistency across codebase
• Document decisions and trade-offs

2. Implementation Guidelines

• Start with the simplest viable solution
• Iterate based on feedback and requirements
• Test thoroughly before deployment

Inputs / Outputs / Contracts

• Inputs:
  • Retention policy configuration
  • Table schemas with retention requirements
  • Archive storage configuration (S3, Glacier)
  • Deletion request data
• Entry Conditions:
  • Archive storage is configured and accessible
  • Database supports partitioning
  • Retention policy is documented and approved
• Outputs:
  • Archived data in cold storage
  • Deleted data from hot storage
  • Audit logs for all operations
  • Retention compliance reports
• Artifacts Required (Deliverables):
  • Retention policy document
  • Archival scripts
  • Deletion scripts
  • Audit logging implementation
• Acceptance Evidence:
  • Data is archived according to policy
  • Data is deleted after retention period
  • User deletion requests are processed
  • Audit logs capture all operations
• Success Criteria:
  • 100% of data archived before retention deadline
  • 100% of data deleted after retention period
  • User deletion requests processed within 24 hours
  • Audit trail complete and immutable

Skill Composition

• Depends on: pii-policy-enforcement
• Compatible with: backfill-and-reconciliation-playbook
• Conflicts with: Indefinite data retention without legal justification
• Related Skills: data-classification

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Quick Start / Implementation Example

1. Review requirements and constraints
2. Set up development environment
3. Implement core functionality following patterns
4. Write tests for critical paths
5. Run tests and fix issues
6. Document any deviations or decisions

# Example implementation following best practices
def example_function():
    # Your implementation here
    pass

Assumptions / Constraints / Non-goals

• Assumptions:
  • Development environment is properly configured
  • Required dependencies are available
  • Team has basic understanding of domain
• Constraints:
  • Must follow existing codebase conventions
  • Time and resource limitations
  • Compatibility requirements
• Non-goals:
  • This skill does not cover edge cases outside scope
  • Not a replacement for formal training

Compatibility & Prerequisites

• Supported Versions:
  • Python 3.8+
  • Node.js 16+
  • Modern browsers (Chrome, Firefox, Safari, Edge)
• Required AI Tools:
  • Code editor (VS Code recommended)
  • Testing framework appropriate for language
  • Version control (Git)
• Dependencies:
  • Language-specific package manager
  • Build tools
  • Testing libraries
• Environment Setup:
  • .env.example keys: API_KEY, DATABASE_URL (no values)

Test Scenario Matrix (QA Strategy)

Technical Guardrails & Security Threat Model

1. Security & Privacy (Threat Model)

• Top Threats: Injection attacks, authentication bypass, data exposure

• Data Handling: Sanitize all user inputs to prevent Injection attacks. Never log raw PII
• Secrets Management: No hardcoded API keys. Use Env Vars/​Secrets Manager
• Authorization: Validate user permissions before state changes

2. Performance & Resources

• Execution Efficiency: Consider time complexity for algorithms
• Memory Management: Use streams/​pagination for large data
• Resource Cleanup: Close DB connections/​file handlers in finally blocks

3. Architecture & Scalability

• Design Pattern: Follow SOLID principles, use Dependency Injection
• Modularity: Decouple logic from UI/​Frameworks

4. Observability & Reliability

• Logging Standards: Structured JSON, include trace IDs request_id
• Metrics: Track error_rate, latency, queue_depth
• Error Handling: Standardized error codes, no bare except
• Observability Artifacts:
  • Log Fields: timestamp, level, message, request_id
  • Metrics: request_count, error_count, response_time
  • Dashboards/​Alerts: High Error Rate > 5%

Agent Directives & Error Recovery

(ข้อกำหนดสำหรับ AI Agent ในการคิดและแก้ปัญหาเมื่อเกิดข้อผิดพลาด)

• Thinking Process: Analyze root cause before fixing. Do not brute-force.
• Fallback Strategy: Stop after 3 failed test attempts. Output root cause and ask for human intervention/​clarification.
• Self-Review: Check against Guardrails & Anti-patterns before finalizing.
• Output Constraints: Output ONLY the modified code block. Do not explain unless asked.

Definition of Done (DoD) Checklist

• Tests passed + coverage met
• Lint/​Typecheck passed
• Logging/​Metrics/​Trace implemented
• Security checks passed
• Documentation/​Changelog updated
• Accessibility/​Performance requirements met (if frontend)

Anti-patterns / Pitfalls

• ⛔ Don't: Log PII, catch-all exception, N+1 queries
• ⚠️ Watch out for: Common symptoms and quick fixes
• 💡 Instead: Use proper error handling, pagination, and logging

Reference Links & Examples

• Internal documentation and examples
• Official documentation and best practices
• Community resources and discussions

Versioning & Changelog

• Version: 1.0.0
• Changelog:
  • 2026-02-22: Initial version with complete template structure