Eu Ai Act Obligations Oliver Schmidt Prietz logo

Eu Ai Act Obligations Oliver Schmidt Prietz

Organization
lawve-ai
eu-ai-act-obligations-oliver-schmidt-prietz

Maps the full set of legal obligations for the EU AI Act based on role + risk tier, producing an actionable compliance matrix with RACI assignments and implementation priorities. This skill should be used when the user asks to "map AI Act obligations", "check what we need to do under the AI Act", "create a compliance checklist", "check deployer obligations", "assess provider duties", or mentions Art. 26, Art. 16-17, AI literacy Art. 4, DPIA, fundamental rights assessment, or "Pflichtenkatalog" under the AI Act.

Overview

Publisherlawve-ai
Repositoryawesome-legal-skills
Skill nameeu-ai-act-obligations-oliver-schmidt-prietz
Stars
854
Forks
111
Bundled files
17
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 17 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by lawve-ai on GitHub. Read the source before you install it.

Installation

Install the Eu Ai Act Obligations Oliver Schmidt Prietz AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/lawve-ai/awesome-legal-skills/tree/main/skills/eu-ai-act-obligations-mapper-oliver-schmidt-prietz
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/lawve-ai/awesome-legal-skills.git /tmp/awesome-legal-skills
mkdir -p .claude/skills
cp -r /tmp/awesome-legal-skills/skills/eu-ai-act-obligations-mapper-oliver-schmidt-prietz .claude/skills/eu-ai-act-obligations-oliver-schmidt-prietz
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Eu Ai Act Obligations Oliver Schmidt Prietz in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Eu Ai Act Obligations Oliver Schmidt Prietz on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Eu Ai Act Obligations Oliver Schmidt Prietz is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

EU AI Act Obligations Mapper

Map the full set of legal obligations based on role + risk tier under the AI Act (Regulation (EU) 2024/1689), producing an actionable compliance matrix with RACI assignments and implementation priorities.

Disclaimer (show at session start, do not block)

Important: This skill provides structured AI Act obligations guidance based on the EU AI Act (Regulation (EU) 2024/1689). It is not legal advice. Implementation of compliance measures should involve qualified legal counsel and relevant technical experts. Effective dates for high-risk obligations reflect the AI Omnibus 2026 postponement (Annex III: 2 December 2027; Annex I: 2 August 2028).


When to Search the Web

On activation — search for:

EU AI Act harmonized standards EN ISO implementing requirements [current year]
EU AI Act conformity assessment notified bodies guidance [current year]

For management systems — search for:

ISO 42001 AI management system alignment EU AI Act [current year]
EU AI Act quality management system requirements guidance

For national rules — search for:

[user's jurisdiction] AI Act national implementation measures [current year]
[user's jurisdiction] AI Act supervisory authority designation

For conformity assessment — search for:

EU AI Act conformity assessment procedures latest guidance
EU AI Act notified body designations [current year]

Workflow: Ask Questions ONE AT A TIME

Phase 1: Input Context (Context-Aware Adaptive Intake)

Step 1 — Context detection (always first):

"Let's map your AI Act obligations."

If you've already done a prior AI Act assessment (risk classification, role determination, or a quick triage), paste the Assessment Context block below. Otherwise, describe your situation in your own words.

Step 2 — Coverage analysis (internal — do not show this table to the user):

Map the context block or narrative to these 6 fields:

#FieldSource in context blockFallback
1Risk classification"Classification:" lineAsk
2Organizational role"Role:" lineAsk
3Organization size"Org Size:" lineAsk
4Sector"Sector:" lineAsk
5Jurisdiction(s)"Jurisdiction:" lineAsk
6Existing frameworksNot in context blockAlways ask

If the risk tier has not been classified yet → run a risk-tier classification first (AI-system test under Art. 3(1); prohibited / high-risk / GPAI / limited / minimal). If the role hasn't been determined yet → determine it first (provider / deployer / importer / distributor under Art. 3 and Art. 25).

Step 3 — Adaptive follow-up:

  • If context block provided → confirm extracted fields, then ask only about gaps. Fields 1-5 are typically covered; only field 6 (existing frameworks) needs asking.
  • If narrative provided → extract what's covered, ask about remaining gaps in a single grouped question.
  • If minimal information provided → ask about all missing fields in a single prompt, grouped conversationally.

Existing compliance status is always asked since it's new information not carried in the context block. Frame conversationally:

"One more thing — which compliance foundations do you already have in place? (Risk management, data quality, QMS, DPIA, incident reporting, AI literacy training, or starting from scratch)"

Maximum 2 interaction turns for intake. If a field remains unclear, mark as [UNCLEAR — proceeding with cautious assumptions].


Phase 2: Obligation Mapping

Based on role + risk tier, load the applicable obligation set.

Read the relevant reference files:

Deployer + High-Risk → Read references/high-risk-deployer-obligations.md Provider + High-Risk → Read references/high-risk-provider-obligations.md Any role + Low-Risk/Minimal → Read references/low-risk-obligations.md Non-high-risk Annex III (Art. 6(3) exception) → Read references/art6-4-documentation.md GPAI provider → Read references/gpai-obligations.md FRIA-triggering deployer → Read references/fria-template.md for Art. 27 FRIA methodology and fillable template Provider conformity assessment → Read references/conformity-assessment.md for Art. 43 track selection, EU Declaration, and CE marking Provider post-market monitoring → Read references/post-market-monitoring.md for Art. 72 monitoring system design and serious incident reporting EU database registration → Read references/eu-database-registration.md for Art. 49 registration process (provider and deployer tracks) All roles → Art. 4 AI competence obligation always applies

Obligation count preview:

"Based on your role as [Role] of a [risk tier] system, you have N obligations across K categories. I'll walk through them in 4 batches."

Batched assessment (4 batches replacing per-obligation questioning):

Present obligations grouped by category. For each batch, show a table with all obligations in that category and ask the user to respond to the entire batch at once:

BatchCategoryTypical obligations
1Technical MeasuresUse per instructions, monitoring, input data, log retention, data quality
2Organizational MeasuresOversight persons, inform affected persons, employee info, AI competence, incident reporting, registration, authority cooperation
3Management SystemsRisk management, data quality mgmt, QMS, post-market monitoring
4Impact AssessmentsDPIA, FRIA

For each batch, present a table:

Batch [X] of 4: [Category]

#ObligationLegal BasisPriorityStatus
1[obligation][article][Immediate/Short-term/Ongoing]Already in place / Partially addressed / Not yet addressed

"For each obligation, indicate: already in place, partially addressed, or not yet addressed. You can respond with just the numbers (e.g., '1,3 = in place; 2,4 = partial; 5 = not addressed')."

Progress indicator after each batch: "Batch [X] of 4 complete. [N] obligations remaining."

Smart defaults: If the user indicated "starting from scratch" in Phase 1, default all obligations to "not yet addressed" and confirm: "Since you're starting from scratch, I've marked all obligations as not yet addressed. Any exceptions?"

Target: 4-5 interaction turns instead of 20+.

Flag priority levels: critical timeline obligations first (e.g., Art. 26(6) 6-month log retention must be operational from day one).

GDPR Cross-Reference Checks

Read references/gdpr-crosswalk.md.

At relevant obligation points, suggest existing GDPR skills:

ObligationTriggerSuggestion
Art. 26(9) DPIAHigh-risk deployer"Perform a DPIA incorporating the provider's Art. 13 information about system capabilities and limitations"
Art. 26(11) inform affected personsDeployer transparency"Prepare a combined AI Act/GDPR transparency notice covering Art. 26(11) and Art. 13/14 GDPR"
Art. 10 data governanceProvider data quality"Conduct a data inventory review mapping AI training data against GDPR data quality and minimization principles"
Art. 26(7) employee informationWorkplace AI"Prepare employee AI transparency documentation combining Art. 26(7) and GDPR Art. 13/14 requirements"
Art. 26(5) serious incidentsIncident detected"Establish a dual incident reporting procedure covering both AI Act (Art. 73) and GDPR (Art. 33/34) timelines"
Personal data processingAny AI processing personal data"Review your GDPR Art. 28 processor agreement to include AI Act cooperation provisions (Art. 25(2))"

Obligation Priority Decision Tree

         ┌─────────────────────────┐
         │ ROLE + RISK TIER        │
         └────────────┬────────────┘
                      │
    ┌─────────────────┼──────────────────┐
    │                 │                  │
    ▼                 ▼                  ▼
┌─────────┐   ┌──────────────┐   ┌──────────────┐
│ PROVIDER│   │   DEPLOYER   │   │ GPAI MODEL   │
│         │   │              │   │ PROVIDER     │
└────┬────┘   └──────┬───────┘   └──────┬───────┘
     │               │                  │
     ▼               ▼                  ▼
 High-Risk?      High-Risk?        Systemic Risk?
 ├─ YES:         ├─ YES:           ├─ YES:
 │ Art. 8-17     │ Art. 26         │ Art. 53 + 55
 │ Art. 17 QMS   │ Art. 27 FRIA    ├─ NO:
 │ Art. 9 Risk   │ Art. 26(9) DPIA │ Art. 53
 │ Art. 43 CA    │ Art. 49(3) Reg  └────────────
 │ Art. 49 Reg   └────────────
 │               ├─ NO (Art. 50):
 ├─ NO           │ Art. 50 only
 │ (Art. 50):    ├─ NO (Minimal):
 │ Art. 50       │ Art. 4 only
 │ +Art. 6(4)    └────────────
 │ if Annex III
 └────────────

 ALL ROLES: Art. 4 AI Competence (always applies)

For worked obligation mapping examples, see references/case-studies.md.


Phase 3: Implementation Roadmap

Group obligations by timeline:

1. IMMEDIATE (before deployment / already overdue if deployed):

  • Use system per operating instructions (Art. 26(1))
  • Monitoring system in place (Art. 26(5))
  • Qualified oversight persons assigned (Art. 26(2))
  • Log retention mechanism active (Art. 26(6))
  • AI competence measures (Art. 4)

2. SHORT-TERM (within 3 months of deployment):

  • Risk management system operational (Art. 9)
  • Data quality management (Art. 10)
  • Registration in EU database (Art. 49)
  • DPIA completed (Art. 26(9))
  • FRIA completed if required (Art. 27)
  • Employee information (Art. 26(7))

3. ONGOING (continuous):

  • System monitoring (Art. 26(5))
  • Logging and record-keeping (Art. 12, Art. 26(6))
  • Incident reporting (Art. 26(5) third sentence)
  • Authority cooperation (Art. 26(12))
  • Post-market monitoring data contribution

4. PERIODIC (regular intervals):

  • Risk reassessment (Art. 9 — recommended annually)
  • AI competence training updates (Art. 4)
  • Documentation review and update
  • Testing and validation (Art. 15)

Read references/technical-measures.md, references/organizational-measures.md, and references/management-systems.md for detailed requirements.

For the full compliance timeline with quarterly action calendar, resource estimates by organization size, and dependency mapping between activities, reference references/compliance-roadmap.md.


Phase 4: Obligations Matrix Output

markdown
## AI Act Compliance Obligations Matrix
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

Role: [Role]  |  Risk Tier: [Tier]  |  Basis: [legal basis]
Organization: [name]  |  Date: [date]

### Technical Measures
| # | Obligation | Legal Basis | Priority | Status | RACI | Effort |
|---|-----------|-------------|----------|--------|------|--------|
| 1 | Use system per operating instructions | Art. 26(1) | Immediate | [ ] | IT=R, Legal=A | Low |
| 2 | Monitor system operation | Art. 26(5) | Immediate | [ ] | IT=R, Compliance=A | Medium |
| 3 | Ensure input data relevance | Art. 26(4) | Immediate | [ ] | IT=R, Business=A | Medium |
| 4 | Retain auto-generated logs (6 months) | Art. 26(6) | Immediate | [ ] | IT=R, Legal=A | Low |
| 5 | Data quality management | Art. 10 | Short-term | [ ] | IT=R, Data=A | High |

### Organizational Measures
| # | Obligation | Legal Basis | Priority | Status | RACI | Effort |
|---|-----------|-------------|----------|--------|------|--------|
| 1 | Assign qualified oversight persons | Art. 26(2) | Immediate | [ ] | HR=R, Legal=A | Medium |
| 2 | Inform affected persons | Art. 26(11) | Immediate | [ ] | Legal=R, Comms=A | Medium |
| 3 | Inform employees/works council | Art. 26(7) | Short-term | [ ] | HR=R, Legal=A | Medium |
| 4 | AI competence training | Art. 4 | Short-term | [ ] | HR=R, Mgmt=A | Medium |
| 5 | Incident reporting procedure | Art. 26(5) s.3 | Immediate | [ ] | Legal=R, IT=C | High |
| 6 | Register use in EU database | Art. 49(3) | Short-term | [ ] | Legal=R | Low |
| 7 | Cooperate with authorities | Art. 26(12) | Ongoing | [ ] | Legal=R, Mgmt=A | Low |

### Management Systems Required
| System | Legal Basis | Scope | Existing? |
|--------|------------|-------|-----------|
| Risk Management | Art. 9 | Continuous lifecycle risk assessment | [ ] |
| Data Quality Mgmt | Art. 10 | Training/validation/test data governance | [ ] |
| Quality Management | Art. 17 | Processes, procedures, compliance concept | [ ] |
| Post-Market Monitoring | Art. 72 | Monitoring throughout lifetime | [ ] |

### Impact Assessments Required
| Assessment | Legal Basis | When | Status |
|-----------|------------|------|--------|
| DPIA | Art. 26(9) + Art. 35 GDPR | Before deployment | [ ] |
| Fundamental Rights Assessment | Art. 27 | Before deployment (public bodies + certain private) | [ ] |

### GDPR Cross-References
| AI Act Obligation | GDPR Parallel | Recommended Action |
|------------------|---------------|----------------|
| Art. 26(9) DPIA | Art. 35 GDPR | Perform DPIA per Art. 35 GDPR incorporating Art. 13 information |
| Art. 26(11) inform persons | Art. 13/14 GDPR | Draft combined AI Act + GDPR transparency notice |
| Art. 10 data governance | Art. 25 GDPR (DPbD) | Conduct data inventory and governance review |
| Art. 26(7) employee info | Art. 13/14 GDPR | Prepare employee AI transparency documentation |
| Incident reporting | Art. 33/34 GDPR | Establish dual AI Act/GDPR incident reporting procedure |

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
SUMMARY:
TOTAL: [X] obligations | [Y] immediate | [Z] require legal judgment
Timeline: [X] already compliant | [Y] gaps identified | [Z] not yet assessed

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
ASSESSMENT CONTEXT (paste into next skill)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
System: [name]
Classification: [risk tier]
Basis: [legal basis]
Role: [role]
Quasi-Provider: [risk level]
Sector: [sector]
Jurisdiction: [list]
Org Size: [size]
Art. 50: [applicable triggers]
GPAI: [yes/no, systemic risk]

NEXT STEPS:
→ Generate formal assessment documentation (classification rationale + obligation matrix)
→ Address [Y] immediate gaps as priority
→ Establish management systems within [timeline]
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

Critical Reminders

  1. Art. 4 AI competence applies to ALL roles and ALL risk tiers — even minimal risk systems
  2. Art. 26(6) log retention (6 months) — must be in place from day one of deployment
  3. Art. 26(9) DPIA — must be completed BEFORE deployment, not after
  4. Art. 27 FRIA — required for public bodies, private entities providing public services, and deployers of insurance risk assessment (Annex III Nr. 5(b)) and social benefits eligibility (Annex III Nr. 5(c)) systems
  5. Art. 26(5) incident reporting — "without undue delay" to provider and authority
  6. SME proportionality — Art. 62 requires authorities to consider SME capabilities
  7. Transition periods vary — prohibited practices (Feb 2025), GPAI (Aug 2025), high-risk Annex III (Dec 2027 — Omnibus-postponed from Aug 2026), high-risk Annex I (Aug 2028 — Omnibus-postponed from Aug 2027). The AI Omnibus is a legislative-in-progress instrument; verify the current status of the postponement via a web search before relying on these dates.
  8. Search for latest harmonized standards — technical implementation standards are still being developed
  9. Enforcement exposure — factor in the penalty tiers when prioritising gaps: up to €35M / 7% of worldwide annual turnover for Art. 5 prohibited-practice violations, and €15M / 3% for other infringements (Art. 99). Enforcement runs through national market-surveillance authorities and, for GPAI, the AI Office.
  10. Jurisdiction-specific obligations — reference references/regulatory-overlays.md for per-country employment law, financial regulator, and data protection overlay requirements that apply in addition to AI Act obligations
  11. Compliance timeline & resources — reference references/compliance-roadmap.md for quarterly action calendar, resource estimation by organization size, and phased compliance roadmap template

Part of the EU AI Act suite

This skill works on its own, but it's designed to interlock with my other EU AI Act skills — install any individually, or use them together for an end-to-end workflow:

  • EU AI Act Quick Assessment — 15–25 min preliminary triage
  • EU AI Act System Classifier — risk-tier classification across all five tiers
  • EU AI Act High-Risk Classifier — depth Annex I / Annex III assessment
  • EU AI Act Role Determination — provider / deployer / importer / distributor (incl. Art. 25)
  • EU AI Act Examination Report Generator — audit-ready compliance report
  • EU AI Act Knowledge Base — Q&A over the Act + Commission guidelines

Each is available as a separate skill — install only what you need.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Eu Ai Act Obligations Oliver Schmidt Prietz AI skill do?

Maps the full set of legal obligations for the EU AI Act based on role + risk tier, producing an actionable compliance matrix with RACI assignments and implementation priorities. This skill should be used when the user asks to "map AI Act obligations", "check what we need to do under the AI Act", "create a compliance checklist", "check deployer obligations", "assess provider duties", or mentions Art. 26, Art. 16-17, AI literacy Art. 4, DPIA, fundamental rights assessment, or "Pflichtenkatalog" under the AI Act.

Why use Eu Ai Act Obligations Oliver Schmidt Prietz on TypingMind?

Because you install it once and use it with any model. Eu Ai Act Obligations Oliver Schmidt Prietz is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Eu Ai Act Obligations Oliver Schmidt Prietz in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/lawve-ai/awesome-legal-skills/tree/main/skills/eu-ai-act-obligations-mapper-oliver-schmidt-prietz. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Eu Ai Act Obligations Oliver Schmidt Prietz?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Eu Ai Act Obligations Oliver Schmidt Prietz?

As many as you like. As long as a model supports skills, you can use Eu Ai Act Obligations Oliver Schmidt Prietz with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Eu Ai Act Obligations Oliver Schmidt Prietz AI skill free?

It is published on GitHub by lawve-ai. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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