Assess whether a Fundamental Rights Impact Assessment (FRIA) is required under Article 27 EU AI Act, and structure or draft that assessment for a specific high-risk AI deployment. Covers deployer scope gating (public bodies and private entities providing public services), affected group mapping, Charter rights analysis, proportionality, safeguards evaluation, residual risk, DPIA/FRIA interaction, notification under Article 27(3), and DACH-specific considerations. Use when asked about FRIA obligations, Article 27 scope, fundamental rights and AI, or deployer assessment duties.
npx skills add https://github.com/lawve-ai/awesome-legal-skills --skill eu-ai-act-fria
Assess whether a deployer must perform a Fundamental Rights Impact Assessment (FRIA) under Article 27 of the EU AI Act, and structure that assessment for a specific high-risk AI use case before the system is put into use.
Important: This skill supports a structured legal-compliance workflow. It does not replace legal judgment. A FRIA is inherently contextual and should never be treated as a box-ticking exercise. Always identify assumptions, open questions, and contested interpretations explicitly.
Before you start: If you have not yet confirmed that the system is a high-risk AI system, use the EU AI Act System Classifier first. Article 27 applies only in the context of high-risk AI systems and only for a subset of deployers.
Follow this sequence in order. Do not skip the scope questions.
Article 27 only applies where the intended use concerns a high-risk AI system within the meaning of the AI Act.
Check:
If high-risk status is not yet confirmed: stop here and use the EU AI Act System Classifier first.
This is the most important gating step.
Article 27 does not apply to all deployers of high-risk AI. It applies to deployers that are:
Assess carefully:
If NO: document that Article 27 FRIA is not mandatory for this deployer, while separate deployer obligations under Article 26 may still apply.
If YES: proceed.
A FRIA must be carried out:
Check:
Article 27(2) requires the FRIA to be grounded in the deployer's actual processes.
Document:
If the process description is vague, the FRIA will be weak. Push for operational specificity.
Article 27(2) expressly requires the deployer to identify the categories of natural persons and groups likely to be affected.
Map:
Then identify which fundamental rights are realistically at stake under the EU Charter of Fundamental Rights, including where relevant:
→ For the detailed rights catalogue and examples, read references/fundamental-rights-catalogue.md.
Article 27(2) requires identification of the specific risks of harm likely to impact the identified persons/groups.
Assess, for each relevant right and affected group:
Use a structured assessment across:
→ For the scoring method and decision framework, read references/fria-methodology.md.
Article 27(2) requires a description of:
Check existing safeguards such as:
The question is not whether a safeguard exists on paper, but whether it is effective for this specific risk.
After accounting for safeguards, assess the residual risk.
Ask:
This is the core judgment section. Do not auto-approve because controls exist. Explain the reasoning.
If the FRIA identifies a specific risk to the rights of natural persons or groups of persons, the deployer must notify the relevant market surveillance authority.
Where the risk relates to processing of personal data and is relevant under data protection law, the deployer must also notify the competent data protection authority.
Check:
→ For authority mapping and notification structure, read references/notification-requirements.md.
Under Article 27(4), the FRIA may be conducted together with a GDPR Article 35 Data Protection Impact Assessment (DPIA), where relevant.
Do not merge them blindly. First determine:
Key point: A DPIA and a FRIA overlap, but they are not the same thing. A FRIA extends beyond data protection into broader Charter rights, procedural fairness, access, equality, and remedy.
→ For overlap and integration guidance, read references/dpia-fria-interaction.md.
If the deployment is in Germany, Austria, or Switzerland, consider the local governance and constitutional overlay.
In Germany in particular, assess:
→ For DACH-specific analysis, read references/dach-specific.md.
Use these questions at intake before drafting the FRIA:
System and Scope
Operational Context
Affected Persons and Rights
10. Are vulnerable groups, children, patients, customers, benefit applicants, job candidates, or employees involved?
11. Which fundamental rights could realistically be interfered with?
12. What is the worst plausible harm for each key group?
Safeguards and Governance
13. What human oversight measures exist in real operation?
14. What complaint, appeal, or redress mechanisms exist?
15. What happens if the system produces an error, bias, or adverse outcome?
16. Are there data quality controls, logging, audits, or monitoring processes?
DPIA / Notification / Change
17. Is personal data processed, and has a DPIA been done or planned?
18. Has the use already started, or is this assessment still pre-deployment?
19. Has anything significantly changed since the last assessment?
20. Has the FRIA identified a specific risk that may require notification?
If key answers are missing, state assumptions and identify them as blockers or legal-risk gaps.
Load these as needed during the assessment:
| File | When to read |
|------|-------------|
| references/fundamental-rights-catalogue.md | Mapping the rights at stake - Charter rights, practical AI impact examples |
| references/fria-methodology.md | Running the assessment - scoring, proportionality, residual risk, decision logic |
| references/dpia-fria-interaction.md | Determining whether/how to combine a FRIA with a GDPR DPIA |
| references/notification-requirements.md | Determining whether notification is required and how to structure it |
| references/dach-specific.md | Germany/Austria/Switzerland overlay - authorities, procurement, works council, constitutional lens |
| references/templates.md | Producing practical outputs - FRIA report, matrix, notifications, management briefing |
Every FRIA engagement should produce these deliverables:
→ For templates and model wording, read references/templates.md.
This skill provides structured workflow support for Article 27 of Regulation (EU) 2024/1689 (EU AI Act). It does not constitute legal advice. Whether an entity is a body governed by public law, a private provider of public services, or whether a specific risk requires notification may depend on national law, sector rules, procurement structures, and supervisory practice. The analysis should be reviewed by qualified counsel, especially before deployment, authority engagement, or high-impact operational decisions.
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-fria 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.