plugins/pm-compliance/skills/gdpr-compliance/SKILL.md
Assess GDPR compliance and build the core records (ROPA, lawful basis, DSAR, DPIA triggers). Use when asked to get GDPR-compliant, build a Record of Processing Activities, decide a lawful basis, handle data-subject requests, or check whether a DPIA is needed. Produces a GDPR assessment — a ROPA, lawful-basis mapping per activity, DSAR workflow, DPIA-trigger screen, and a prioritised gap list.
npx skillsauth add mohitagw15856/pm-claude-skills gdpr-complianceInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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GDPR compliance is mostly bookkeeping you can defend: knowing every place you process personal data, why you're allowed to, how long you keep it, and how a person can get it out or deleted. This skill builds that record (the ROPA), pins a lawful basis to each activity, and flags the high-risk processing that legally requires a DPIA — turning "are we GDPR-compliant?" into a documented, auditable answer.
Ask for these only if they aren't already provided:
1. ROPA — the Record of Processing Activities (Art. 30); one row per activity:
| Activity | Purpose | Data categories | Subjects | Lawful basis | Recipients | Retention | Transfers | |---|---|---|---|---|---|---|---|
2. Lawful basis — the chosen Art. 6 basis per activity (consent / contract / legal obligation / vital interests / public task / legitimate interests) and why. For special-category data, the additional Art. 9 condition. Don't default everything to "consent" — it's often the weakest, hardest-to-maintain basis.
3. DSAR workflow — how you handle access/erasure/portability/objection requests: intake, identity check, the one-month deadline, and how data is located and exported/deleted.
4. DPIA screen — flag activities that legally require a Data Protection Impact Assessment (large-scale special-category processing, systematic monitoring, profiling with legal effects).
5. Gaps — prioritised: missing lawful basis, no retention period, undocumented transfers, no DSAR process.
scripts/ropa_check.py (stdlib only) validates a ROPA and scores completeness so gaps are found
mechanically:
# ropa.json: [{"activity":"...","purpose":"...","lawful_basis":"contract","retention":"3y","recipients":["..."],"special_category":false,"large_scale":true}, ...]
python3 scripts/ropa_check.py ropa.json
python3 scripts/ropa_check.py ropa.json --json
It flags activities missing a lawful basis, purpose, or retention, and marks those that trigger a DPIA.
EU GDPR — Art. 6 (lawful basis), Art. 9 (special category), Art. 30 (ROPA), Art. 35 (DPIA), data-subject rights.
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