Classify an AI system under the EU AI Act (Regulation (EU) 2024/1689, KI-Verordnung) and determine compliance obligations. Walks Art. 3(1) AI system definition (7-criteria test), Art. 2 scope exclusions, Art. 5 prohibited practice screening, Annex I product safety, Annex III high-risk use cases (biometrics, critical infrastructure, education, employment, credit scoring, insurance, law enforcement, migration, justice), Art. 6(3) narrow procedural exception, Art. 51-56 GPAI with the 10^25 FLOP systemic risk threshold, and Art. 50 transparency triggers (deepfakes, emotion recognition, synthetic content). Roles: provider (Anbieter), deployer (Betreiber), importer, distributor, Art. 25 quasi-provider. DACH: Betriebsrat, BaFin, BSI, BNetzA, BfDI. Use when asked to classify an AI system or model, run a Risikoklassifizierung, assess Annex III high-risk status, screen Art. 5 prohibited practices, check the Art. 6(3) exception, classify a medical device, medical imaging, credit scoring, HR or CV screening, employment screening, biometric identification, emotion recognition, deepfake, content moderation, fraud detection, recommender, chatbot, generative AI or foundation model system, run an AI vendor or procurement compliance review, or determine GPAI obligations.
npx skills add https://github.com/lawve-ai/awesome-legal-skills --skill eu-ai-act-classification
Classify AI systems under Regulation (EU) 2024/1689 and determine obligations by role.
Important: This skill provides compliance workflow support, not legal advice. Always cite specific EU AI Act articles. Where facts are incomplete, state assumptions explicitly and ask targeted follow-up questions.
Follow this decision tree strictly in order. Stop at the first match.
Confirm the AI system falls within scope:
If out of scope, document why and stop.
Check every Article 5 category. If any match → PROHIBITED. Stop unless a narrow law enforcement exception applies.
Categories (check all eight):
→ For the complete checklist with examples and edge cases, read references/prohibited-practices.md.
Annex I path: Is the AI a product or safety component under EU harmonisation legislation (e.g., MDR, Machinery Regulation) subject to third-party conformity assessment? → HIGH-RISK
Annex III path: Does the intended purpose fall within one of the eight high-risk use-case categories?
| # | Category | Quick examples |
|---|----------|---------------|
| 1 | Biometrics | Remote identification, verification, categorisation |
| 2 | Critical infrastructure | Energy grid control, water systems, traffic management |
| 3 | Education | Admissions, grading, learning access decisions |
| 4 | Employment | CV screening, promotion, termination, task allocation |
| 5 | Essential services | Credit scoring, insurance pricing, housing, welfare |
| 6 | Law enforcement | Risk assessment, evidence evaluation, profiling |
| 7 | Migration & border | Border risk assessment, document verification |
| 8 | Justice & democracy | Judicial assistance, electoral process systems |
→ For all categories with examples and edge cases, read references/high-risk-annex-iii.md.
Article 6(3) exception: Even if an Annex III use case matches, a system is NOT high-risk if it:
AND the system does not pose a significant risk of harm to health, safety, or fundamental rights.
If claiming this exception, document the reasoning thoroughly.
Independent of system-level risk. A minimal-risk app can use a GPAI model with its own obligations. Downstream providers/deployers must still verify vendor evidence and ensure documentation is sufficient for their specific use case and risk profile.
Check:
→ For GPAI obligations and systemic risk details, read references/gpai-obligations.md.
If not prohibited or high-risk, check Article 50 transparency duties:
No AI Act-specific obligations. Recommend:
Use these questions at the start (and whenever facts are missing) to gather the information needed for classification. For borderline cases or uncertainty, escalate early to qualified legal counsel and document assumptions.
System & Purpose
Impact & Context
Technical
Organisational
10. Is it a product or safety component under EU harmonisation legislation?
If answers are incomplete, state assumptions explicitly and flag gaps.
Load these as needed based on the classification result:
| File | When to read |
|------|-------------|
| references/prohibited-practices.md | Evaluating Article 5 — complete checklist with examples and edge cases |
| references/high-risk-annex-iii.md | Evaluating Annex III — all 8 categories with examples, edge cases, and the Article 6(3) exception |
| references/gpai-obligations.md | System uses a GPAI model — Articles 51–56, systemic risk thresholds, downstream duties |
| references/obligations-matrix.md | After classification — provider/deployer/importer/distributor responsibilities by risk level |
| references/timeline.md | Building a compliance roadmap — all deadlines with practical planning guidance |
| references/dach-specific.md | Deployer is in Germany/Austria/Switzerland — works council, BaFin, BSI, BNetzA, sector-specific overlaps |
| references/templates.md | Producing deliverables — classification memo, risk register entry, executive summary templates |
Every classification produces three deliverables:
Always consider horizontal obligations such as AI literacy/training (Article 4) as part of the compliance roadmap, even for minimal-risk systems.
→ For complete templates, read references/templates.md.
Flag these in every assessment to ensure appropriate escalation:
| Violation | Maximum fine |
|-----------|-------------|
| Prohibited practices | €35M or 7% global annual turnover |
| High-risk system obligations | €15M or 3% global annual turnover |
| Incorrect/misleading information | €7.5M or 1% global annual turnover |
For SMEs and startups, the lower of the two amounts applies.
This skill provides structured compliance workflow support based on Regulation (EU) 2024/1689. It does not constitute legal advice. Classification outcomes should be reviewed by qualified legal counsel before being relied upon for compliance decisions. The EU AI Act is subject to delegated acts, implementing acts, and guidance from the EU AI Office that may affect interpretation.
Assess Kubernetes workloads and cluster configuration for AKS Automatic compatibility. Identifies incompatibilities, generates fixes, and guides migration from AKS Standard to AKS Automatic. WHEN: migrate to AKS Automatic, check AKS Automatic readiness, validate manifests for Automatic, assess cluster for Automatic compatibility, fix deployment for Automatic compatibility, identify AKS Automatic migration blockers, is my cluster ready for AKS Automatic.
Discovers available Azure OpenAI model capacity across regions and projects. Analyzes quota limits, compares availability, and recommends optimal deployment locations based on capacity requirements. USE FOR: find capacity, check quota, where can I deploy, capacity discovery, best region for capacity, multi-project capacity search, quota analysis, model availability, region comparison, check TPM availability. DO NOT USE FOR: actual deployment (hand off to preset or customize after discovery), quota increase requests (direct user to Azure Portal), listing existing deployments.
Interactive guided deployment flow for Azure OpenAI models with full customization control. Step-by-step selection of model version, SKU (GlobalStandard/Standard/ProvisionedManaged), capacity, RAI policy (content filter), and advanced options (dynamic quota, priority processing, spillover). USE FOR: custom deployment, customize model deployment, choose version, select SKU, set capacity, configure content filter, RAI policy, deployment options, detailed deployment, advanced deployment, PTU deployment, provisioned throughput. DO NOT USE FOR: quick deployment to optimal region (use preset).
Unified Azure OpenAI model deployment skill with intelligent intent-based routing. Handles quick preset deployments, fully customized deployments (version/SKU/capacity/RAI policy), and capacity discovery across regions and projects. USE FOR: deploy model, deploy gpt, create deployment, model deployment, deploy openai model, set up model, provision model, find capacity, check model availability, where can I deploy, best region for model, capacity analysis. DO NOT USE FOR: listing existing deployments (use foundry_models_deployments_list MCP tool), deleting deployments, agent creation (use agent/create), project creation (use project/create).
Intelligently deploys Azure OpenAI models to optimal regions by analyzing capacity across all available regions. Automatically checks current region first and shows alternatives if needed. USE FOR: quick deployment, optimal region, best region, automatic region selection, fast setup, multi-region capacity check, high availability deployment, deploy to best location. DO NOT USE FOR: custom SKU selection (use customize), specific version selection (use customize), custom capacity configuration (use customize), PTU deployments (use customize).
This skill should be used when working with LaminDB, an open-source data framework for biology that makes data queryable, traceable, reproducible, and FAIR. Use when managing biological datasets (scRNA-seq, spatial, flow cytometry, etc.), tracking computational workflows, curating and validating data with biological ontologies, building data lakehouses, or ensuring data lineage and reproducibility in biological research. Covers data management, annotation, ontologies (genes, cell types, diseases, tissues), schema validation, integrations with workflow managers (Nextflow, Snakemake) and MLOps platforms (W&B, MLflow), and deployment strategies.
Latch platform for bioinformatics workflows. Build pipelines with Latch SDK, @workflow/@task decorators, deploy serverless workflows, LatchFile/LatchDir, Nextflow/Snakemake integration.
Run Python code in the cloud with serverless containers, GPUs, and autoscaling. Use when deploying ML models, running batch processing jobs, scheduling compute-intensive tasks, or serving APIs that require GPU acceleration or dynamic scaling.
Take lawve-ai/eu-ai-act-classification from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.