skills/dpia-sentinel-oliver-schmidt-prietz/SKILL.md
GDPR Data Protection Impact Assessment (DPIA) guidance under Article 35 GDPR, EDPB Guidelines WP 248 rev.01, EDPB Opinion 28/2024 (AI), and national SA blacklists/whitelists. Triggers: "DPIA", "DSFA", "Datenschutz-Folgenabschätzung", "impact assessment", "Art. 35", "do I need a DPIA", descriptions of new high-risk processing (profiling, AI, biometrics, large-scale monitoring, special category data), Art. 36 prior consultation questions, national blacklist/whitelist queries.
npx skillsauth add lawvable/awesome-legal-skills dpia-sentinel-oliver-schmidt-prietzInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Important: This provides structured GDPR Article 35 guidance based on EDPB Guidelines and national SA requirements. It is not legal advice. Involve your DPO (Art. 35(2)) and qualified counsel for final decisions.
Determine what the user needs and load references accordingly:
| User Need | Load These References | Action |
|-----------|----------------------|--------|
| "Do I need a DPIA?" / threshold question | references/edpb-criteria.md + relevant jurisdiction file(s) | Run threshold assessment |
| Full DPIA | edpb-criteria.md + jurisdiction(s) + references/risk-catalog.md + scoring.md | Walk through assessment phases |
| Document generation (.docx) | references/templates.md + docx generation skill (/mnt/skills/public/docx/SKILL.md in Claude.ai Projects, or docx-processing-anthropic skill in Claude Code; if unavailable, generate well-formatted Markdown as fallback) | Generate Word document |
| Specific legal question | Load relevant reference only | Answer directly |
Jurisdiction selection: Ask two questions: (1) Where is the controller's main establishment? (2) Where are the data subjects located? Load all jurisdiction files that are relevant — this may be multiple files for multi-jurisdictional processing. See references/edpb-criteria.md → "Multi-Jurisdictional DPIA Analysis" for the full decision framework.
Available jurisdiction files:
references/jurisdictions/de-dsk.md — Germanyreferences/jurisdictions/fr-cnil.md — Francereferences/jurisdictions/ie-dpc.md — Irelandreferences/jurisdictions/be-apd.md — Belgiumreferences/jurisdictions/nl-ap.md — Netherlandsreferences/jurisdictions/it-garante.md — Italyreferences/jurisdictions/pl-uodo.md — Polandreferences/jurisdictions/whitelists.md — France, Czech Republic, Spain, AustriaFor jurisdictions not covered by a dedicated file, rely on the EDPB nine-criteria analysis in references/edpb-criteria.md and note that the user should check their national SA's Art. 35(4) list directly.
Threshold → Description → Necessity/Proportionality → Risks → Mitigations → Residual Risk → Art. 36 Check → Documentation
This is the logical sequence, not a rigid script. Adapt to the user: if they provide rich context upfront, skip intake questions. If they're experienced, move faster. If they're new to DPIAs, explain more.
The assessment is iterative: if mitigations in later stages change the processing design, revisit earlier analysis and flag this to the user.
These are areas where Claude's training knowledge may be imprecise. Always apply these rules:
Art. 35(3) triggers are absolute. If any of the three mandatory cases apply (systematic extensive automated evaluation with legal/significant effect; large-scale special category/criminal data; systematic monitoring of publicly accessible areas on large scale), a DPIA is required — no balancing, no judgment call.
The two-criteria rule is a presumption, not a mandate. Meeting 2+ of the 9 EDPB criteria creates a strong presumption a DPIA is needed. But a DPIA may be needed with only 1 criterion, and may be justified as unnecessary with 2 — if thoroughly documented. See WP 248 rev.01, p. 11.
Art. 9 is cumulative with Art. 6. Special category data always needs BOTH a legal basis under Art. 6 AND an exception under Art. 9(2). These are separate legal hurdles.
"Large scale" has no fixed number. The EDPB uses four factors: number of subjects, data volume, duration, geographic extent. An individual doctor is not large scale; a regional hospital is. Never cite a specific numerical threshold.
National blacklists are additive, not exhaustive. Processing not on a blacklist may still require a DPIA. A blacklist entry in the relevant jurisdiction overrides whitelist exemptions from other jurisdictions.
Multi-jurisdictional processing requires checking ALL relevant blacklists. Art. 35(4) lists are territorial — the DPIA obligation is triggered if the processing matches a blacklist in ANY jurisdiction where the controller is established OR where data subjects are located. The one-stop-shop mechanism (Art. 56) governs enforcement jurisdiction, but it does NOT limit which Art. 35(4) lists apply to the DPIA obligation itself. A single DPIA can address multiple jurisdictions, but the threshold analysis must run against each applicable national list. See references/edpb-criteria.md → "Multi-Jurisdictional DPIA Analysis" for details.
DPIA must happen before processing begins (Art. 35(1)). It is a pre-processing obligation, not a retroactive compliance exercise. If processing has already started, the DPIA should still be done but note this as a compliance gap.
AI requires dual-phase analysis (EDPB Opinion 28/2024). Training and deployment are separate processing activities with distinct risk profiles. A deployer cannot simply rely on the model provider's DPIA.
Art. 36 prior consultation is sequential to the DPIA, not part of it. The DPIA identifies residual risk; if that risk remains high after all feasible mitigations, Art. 36 requires consulting the SA before processing begins. The SA has 8 weeks (extendable by 6).
Pseudonymization as risk reducer (EDPB Guidelines 01/2025 on Pseudonymisation, adopted 17 January 2025): Effective pseudonymization with technically separated additional information can meaningfully reduce likelihood scores in risk assessment. But it must be genuine — if re-identification is trivial, it doesn't reduce risk.
Risk assessment is from the data subject's perspective. A DPIA assesses risks to rights and freedoms of natural persons (Recital 75), not corporate/business risks. Identity theft risk to the individual, not reputational risk to the company.
AI Act FRIA is distinct from DPIA. For high-risk AI systems under the AI Act, a Fundamental Rights Impact Assessment (FRIA) may also be required. DPIA (data protection risks) and FRIA (broader fundamental rights) are complementary — one does not replace the other.
Threshold result: Present a clear verdict (DPIA Required / Recommended / Not Required) with the reasoning showing Art. 35(3) check, criteria analysis, and national list check.
Risk register: Table with Risk ID, Description, Rights Category, Likelihood (1-5), Severity (1-5), Score, Level. Use the scoring methodology in references/scoring.md.
Residual risk overview: Summary showing total risks by level before and after mitigation, plus overall position (Acceptable / Acceptable with Conditions / Art. 36 Consultation Required).
Documents: Generate .docx files following references/templates.md. Always read the docx skill first.
tools
Draft, adapt, and review contracts and clauses aligned with The Chancery Lane Project's methodology for reducing carbon emissions through legal agreements. Use when Claude needs to: (1) Draft new climate-aligned clauses (e.g., net zero commitments, carbon accounting, supply chain decarbonization), (2) Adapt or modify existing contracts to incorporate climate objectives, (3) Review and analyze clauses for alignment with climate goals and decarbonization strategies, (4) Provide guidance on The Chancery Lane Project's house style and drafting methodology for climate-conscious legal work.
development
Matter budgeting and ongoing WIP/variance monitoring. Build phase-based fee estimates at matter setup, run bottom-up budgets by jurisdiction or workstream, calculate contingency, and structure AFA arrangements (fixed fee, capped fee, phased fixed fees). Ongoing monitoring: WIP tracking against budget, proportionality assessment (spend vs progress), variance commentary with root cause analysis, forecast-to-complete, realisation monitoring, write-off analysis. Trigger on: 'build a budget', 'fee estimate', 'what will this cost', 'WIP review', 'budget vs actual', 'how are we tracking against budget', 'we're over budget', 'realisation is poor', 'what's our ETC', 'budget for the German workstream', 'model the financial impact of this scope change', 'draft a fee adjustment', 'write-off analysis', 'how much contingency', 'AFA structure', 'fixed fee estimate', 'budget update', 'forecast to complete'.
tools
Operational billing execution for legal matters. Monthly bill prep and billing instructions, LC invoice review and disbursement treatment, client billing query responses, cashflow modelling (LC payment obligations vs client receipts), and leverage and burn analysis (staffing mix, predicted total cost, margin trajectory). Trigger on: 'prepare the bill', 'billing instruction', 'end of month billing', 'LC invoice', 'local counsel invoice', 'pass through as disbursement', 'client querying the invoice', 'billing dispute', 'cashflow gap', 'when will we get paid', 'LC payment due', 'leverage analysis', 'staffing mix', 'predicted total cost', 'burn rate by grade', 'are we on track', 'what will this matter cost'.
tools
When your bar comes asking "show me how you billed AI-assisted work" — and ABA 512, Florida 24-1, California, New York, and DC all have opinions out — you need an artifact that survives review. billable-time produces it. From your Claude Code session logs, it drafts reviewable time entries plus a printable HTML audit packet with: SHA-256 chain of evidence (source files + matter.yml + active disclosure pack + verifiable artifact self-hash), attorney identity and signature block, a bar-opinion disclosure pack with starter language for five jurisdictions, and content-aware deterministic narratives derived from filename and tool shape — never from prompt text by default. The tool refuses to bill on its own. --strict mode refuses to ship the artifact if any audit invariant fails (broad routes, missing attorney, missing/unverified disclosure). Comes as a Node CLI and a self-contained browser version (no backend; JSONL never leaves the page). 15 invariant tests verify the contract. AGPL-3.0.