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pii-lifecycle-designer

Classify PII and map retention and erasure across data stores.

2|Updated Jul 6, 2026
Search Tags:#retention#data mapping#pii#anonymization#erasure#privacy engineering
One-click install
npx skills add https://github.com/nguyenpv1980-wq/Project-Aegis --skill pii-lifecycle-designer
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: pii-lifecycle-designer
Source: https://github.com/nguyenpv1980-wq/Project-Aegis/tree/main/.claude/skills/pii-lifecycle-designer
Command: npx skills add https://github.com/nguyenpv1980-wq/Project-Aegis --skill pii-lifecycle-designer

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill helps you design how personal data is collected, classified, retained, and deleted across the entire estate so you can answer where PII lives, why it is kept, and how it is removed with honest, enforceable mechanisms.

Core Features & Use Cases

  • PII classification and inventory: Defines direct, quasi-identifying, sensitive, derived, and free-text personal data classes with purpose, retention class, and allowed stores.
  • Per-store data mapping: Maps personal data across operational tables, logs, caches, search indexes, vector stores, analytics, backups, and third-party processors.
  • Retention and deletion design: Specifies triggers, enforcement mechanisms, deletion semantics, backup-aging policy, and erasure propagation for data-subject requests.
  • Use case: A privacy or platform team can use this Skill when they cannot confidently say where customer data resides or how a deletion request should reach every copy and derivative.

Quick Start

Ask for a full-estate PII lifecycle design that maps where personal data lives, sets retention by class and store, and explains how deletion requests propagate through live systems and backups.

Frequently Asked Questions about pii-lifecycle-designer

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I map where personal data lives across operational systems, logs, caches, and backups?▼

PII lifecycle design maps personal data across operational tables, logs, caches, search indexes, vector stores, analytics, backups, and third-party processors to identify where every copy and derivative resides.

How do I design deletion request propagation for data-subject erasure across my estate?▼

Deletion request propagation defines per-store deletion semantics, erasure triggers, and enforcement mechanisms so data-subject requests reach every copy, derivative, and backup with honest, enforceable removal rules.

What is the difference between pseudonymization and anonymization in PII retention design?▼

Pseudonymization replaces identifiers with pseudonyms, while anonymization irreversibly removes the ability to identify individuals; the lifecycle design includes explicit checks to distinguish the two and their residual risks.

How should I handle personal data retention rules when backups contain PII that needs deleting?▼

Backup rotation handling defines honest aging policies for personal data retention, acknowledging that immediate deletion from backups is often infeasible and specifying when erasure propagates through scheduled rotations.

Can I use this to classify direct, quasi-identifying, and free-text personal data for privacy engineering?▼

Yes, PII classification defines direct, quasi-identifying, sensitive, derived, and free-text personal data classes, assigning each a purpose, retention class, and allowed stores for privacy engineering decisions.

What are the limitations of designing PII retention and erasure across complex data estates?▼

Limitations include explicit residual-risk reporting for scenarios where complete erasure is impossible, such as immutable backups or derived analytics, requiring honest acknowledgment of where deletion cannot fully propagate.

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