PIA Generation

Generate a Privacy Impact Assessment in house format for a new feature, product, or processing activity, using the structure learned from your seed PIA.

PIA Generation

Skill content lives upstream at the path above (commit 9cecd91b0f26f732d18315afc3c9bb5ff99e0fbb). Pull the upstream file before invocation; do not edit this manifest in place.

Added: September 23, 2026 · Last updated: September 24, 2026

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SKILL.mdSKILL.md

Jurisdiction: General
Practice area: Data protection
License: MIT
Author: stubbi
Source: general/data-protection/skills/pia-generation-stubbi

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개인정보처리방침 생성기

MITGeneralData protection

데이터 유형, 관할권, GDPR 및 컴플라이언스 고려사항, 법률 검토가 필요한 조항을 다루는 상세한 개인정보처리방침을 초안 작성합니다. 개인정보처리방침 작성, 데이터 보호 문서 업데이트, 또는 컴플라이언스 준비 시 사용하세요.

$privacy-legal:dpa-review

Apache-2.0GeneralData protection

Review a Data Processing Agreement against your DPA playbook — auto-detects whether you're processor or controller and applies the right half of the playbook. Use when the user says "review this DPA", "check this data processing addendum", "customer sent their DPA", "is this DPA okay", or attaches a DPA.

$privacy-legal:dsar-response

Apache-2.0GeneralData protection

Walk through a Data Subject Access Request (or deletion, portability, correction request) and draft the response — verify identity, locate data system-by-system, assess exemptions, draft the acknowledgment and substantive response letters. Use when a DSAR comes in, the user pastes an access/deletion/portability/correction request, or says "DSAR came in", "access request", "right to be forgotten", or "someone wants their data".

AI Vendor Privacy Due Diligence

Apache-2.0GeneralData protection

Determines controller-processor relationships for AI services and conducts privacy due diligence. Covers SaaS AI (processor), embedded AI (joint controller), API-based AI (assessment framework), and vendor risk assessment. Keywords: AI vendor, controller-processor, due diligence, SaaS AI, joint controller, Art. 28.

Anonymization as Retention Alternative

Apache-2.0GeneralData protection

Evaluates anonymization as a retention alternative under GDPR Recital 26, applying the WP29 Opinion 05/2014 techniques including randomization and generalization. Validates anonymization effectiveness using k-anonymity, l-diversity, and t-closeness metrics. Activate for anonymization, de-identification, k-anonymity, retention alternative queries.

Applying Privacy Design Patterns

Apache-2.0GeneralData protection

Systematic application of the eight privacy design patterns per Hoepman: minimize, hide, separate, abstract, inform, control, enforce, and demonstrate. Covers pattern selection methodology per processing activity, mapping to GDPR principles, and practical implementation guidance for privacy-by-design system architecture.