privacy-data-protection-reviewer

name	privacy-data-protection-reviewer
description	Review privacy, personal data, data classification, deletion concepts, purpose limitation, GDPR risks, and sensitive-data logging.
version	1.1.0
since	2026-06-17
last_modified	2026-06-17
authors	["platform-engineering"]
stability	stable
min_platform_version	{"codex":"unknown","amazon-q":"unknown","antigravity":"unknown","auggie":"unknown","bob":"unknown","claude-code":"unknown","cline":"unknown","codebuddy":"unknown","continue":"unknown","costrict":"unknown","crush":"unknown","github-copilot":"unknown","gitlab-duo":"unknown","factory":"unknown","forgecode":"unknown","opencode":"unknown","openhands":"unknown","cursor":"unknown","roo-code":"unknown","kiro":"unknown","junie":"unknown","gemini-cli":"unknown","iflow":"unknown","kilocode":"unknown","kimi":"unknown","lingma":"unknown","pi":"unknown","qoder":"unknown","qwen":"unknown","windsurf":"unknown","ollama":"unknown"}
deprecated_since	null
replaces	null
supersedes	[]
changelog	[{"version":"1.1.0","date":"2026-06-17","change":"Initial generated production-ready SDLC / DevSecOps skill"}]

Privacy Data Protection Reviewer

Purpose

Review privacy, personal data, data classification, deletion concepts, purpose limitation, GDPR risks, and sensitive-data logging. Treat regulatory, security, and operational references as review and evidence guidance, not legal advice.

When to use

• privacy and data protection decisions, controls, or operating practices need independent review.
• A change affects privacy and data protection artifacts such as personal-data inventory, classification label, deletion concept, purpose statement, DPIA note, log sample.
• The user needs evidence-oriented findings for risks such as personal data overcollection, purpose creep, missing deletion path, sensitive logging, unlawful retention, weak data minimization.
• Audit, security, operations, or platform stakeholders need a concise readiness position.
• Existing documentation, tickets, tests, or logs must be turned into actionable remediation items.

Operating model

1. Identify the relevant privacy and data protection artifacts, owners, systems, environments, and review boundary.
2. Compare the available artifacts against expected signals such as data-flow map, retention setting, consent or legal basis, redaction rule, access log, privacy review.
3. Separate confirmed gaps from assumptions, missing evidence, and advisory improvement opportunities.
4. Rate findings by operational, security, compliance, customer, and auditability impact.
5. Recommend minimal remediation steps, validation evidence, owners, and review cadence.

Spec-Driven Change Context

• Treat repository specs, ADRs, runbooks, change proposals, design notes, and task files as durable context that outlives a chat session.
• For non-trivial changes, prefer a checked-in change artifact or equivalent proposal/design/tasks record before implementation begins.
• Capture requirement deltas explicitly: added, modified, removed, deprecated, or unchanged behavior.
• Keep implementation tasks traceable to acceptance criteria, affected specs, validation commands, and owners.
• During verification, compare the implementation against the proposal, design decisions, task checklist, and spec deltas.
• After completion, sync or archive completed change artifacts so the repository's source of truth reflects the final behavior.
• If the repository has no spec workflow yet, report the missing artifact and provide a minimal proposal/spec/tasks outline instead of relying on chat-only intent.

Skill-Specific Review Scope

• Primary artifacts: personal-data inventory, classification label, deletion concept, purpose statement, DPIA note, log sample.
• Risk themes: personal data overcollection, purpose creep, missing deletion path, sensitive logging, unlawful retention, weak data minimization.