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Get Started Free →EU AI Act readiness assessment and sprint playbook. Use when preparing for the Aug 2026 high-risk AI system deadline, when a notified-body conformity assessment is scheduled, or when GPAI (general-purpose AI) obligations apply.
.claude/skills/borghei-ai-act-readiness/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-02 | ✗→✓ | ▲ Improved | 47% | 0% |
| case-12 | ✗→✓ | ▲ Improved | 35% | 0% |
| case-13 | ✗→✓ | ▲ Improved | 95% | 0% |
| case-14 | ✗→✓ | ▲ Improved | 26% | 0% |
| case-20 | ✗→✓ | ▲ Improved | 60% | 0% |
Operational playbook for EU AI Act compliance readiness — focused on the sprint to demonstrate readiness for the Aug 2026 high-risk AI deadline and ongoing conformity assessments.
When to use this skill vs. eu-ai-act-specialist:
| Situation | Skill applies | |-----------|---------------| | Aug 2026 high-risk deadline approaching | Yes — readiness sprint | | Notified body conformity assessment scheduled | Yes — full prep | | GPAI model obligations apply (Aug 2025+) | Yes — GPAI-specific checklist | | Annual readiness review | Yes — periodic sprint | | Building AI Act program from scratch | Use ra-qm-team/eu-ai-act-specialist | | AI system classification | Use ra-qm-team/eu-ai-act-specialist |
| Date | Requirement | |------|-------------| | Aug 2, 2024 | AI Act enters into force | | Feb 2, 2025 | Prohibited practices effective; AI literacy requirements | | Aug 2, 2025 | GPAI provider obligations effective | | Aug 2, 2026 | Most high-risk AI requirements effective | | Aug 2, 2027 | All high-risk AI requirements + product safety harmonization |
Week 1-2: System classification confirmation; gap analysis
Week 3-5: Documentation buildout (technical file, risk management, data governance)
Week 6-7: Conformity assessment internal dry-run
Week 8: External notified-body engagement / assessmentWeek 1: System classification (provider/deployer/importer/etc.)
Week 2: Documentation prep (model card, training data summary, copyright compliance)
Week 3: Risk assessment + transparency obligations
Week 4: Submission / publication of required informationPer Article 6 / Annex III, AI systems classify into risk categories:
| Category | Examples | Requirements | |----------|----------|--------------| | Prohibited | Social scoring; behavior manipulation of vulnerable groups | Cannot deploy | | High-risk | Biometric ID; critical infrastructure; education; employment; access to essential services; law enforcement | Comprehensive obligations | | Limited-risk (transparency) | Chatbots; deepfakes; emotion recognition | Disclosure obligation | | Minimal-risk | Most enterprise AI; spam filters | Voluntary code of conduct | | GPAI | Large language models; foundation models | Separate obligations (Article 51+) |
| Requirement | Article | |-------------|---------| | Risk management system | Art. 9 | | Data governance + quality | Art. 10 | | Technical documentation | Art. 11 | | Record-keeping (logging) | Art. 12 | | Transparency to users | Art. 13 | | Human oversight | Art. 14 | | Accuracy, robustness, cybersecurity | Art. 15 | | Quality management system | Art. 17 | | Conformity assessment | Art. 43 | | Registration in EU database | Art. 71 | | Post-market monitoring | Art. 72 | | Serious incident reporting | Art. 73 |
| Requirement | Detail | |-------------|--------| | Technical documentation | Per Annex XI | | Training data summary (public) | Sufficiently detailed | | Copyright compliance | Honor opt-outs from text/data mining | | Information to downstream providers | Enable downstream compliance | | Code of practice compliance | (Optional but presumed conformity) |
Additional requirements:
Before running the readiness assessment, confirm these inputs. If any is unknown or vague, ASK — do not assume:
Stop rule: ask only the 2-3 that most change the output. If the user says "just draft it," proceed and list your assumptions at the top of the assessment.
python3 scripts/ai_act_readiness_score.py --config ai-system.yamlpython3 scripts/gpai_obligation_checker.py --model model.yaml| Script | Purpose | |--------|---------| | scripts/ai_act_readiness_score.py | Score current AI Act readiness per system | | scripts/gpai_obligation_checker.py | Validate GPAI provider obligations (Article 53+) |
ra-qm-team/eu-ai-act-specialist — deep AI Act program managementra-qm-team/iso42001-ai-management — ISO 42001 AIMS (companion AI governance)ra-qm-team/audit-prep/aims-audit — AIMS audit-prep variantra-qm-team/audit-prep/gdpr-audit-prep — GDPR overlay for AI processing personal datara-qm-team/audit-prep/compliance-readiness — multi-framework readiness| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-04 | pass→fail | 13,910 | 19,941 | +43% | 1 | 1 | 0% | 2,078 | 4,885 | +135% | 0 | 0 | — |
case-05 | fail→fail | 24,159 | 34,781 | +44% | 1 | 1 | 0% | 4,040 | 7,457 | +85% | 0 | 0 | — |
case-01 | fail→fail | 19,567 | 17,439 | -11% | 1 | 1 | 0% | 3,084 | 4,282 | +39% | 0 | 0 | — |
case-02 | fail→pass | 21,560 | 21,930 | +2% | 1 | 1 | 0% | 3,554 | 5,232 | +47% | 0 | 0 | — |
case-03 | fail→fail | 29,786 | 36,210 | +22% | 1 | 1 | 0% | 4,769 | 7,685 | +61% | 0 | 0 | — |
case-06 | pass→pass | 16,567 | 16,125 | -3% | 1 | 1 | 0% | 2,411 | 4,377 | +82% | 0 | 0 | — |
case-07 | pass→pass | 19,426 | 20,024 | +3% | 1 | 1 | 0% | 3,168 | 4,867 | +54% | 0 | 0 | — |
case-08 | pass→pass | 8,229 | 10,591 | +29% | 1 | 1 | 0% | 1,404 | 3,602 | +157% | 0 | 0 | — |
case-09 | pass→pass | 10,546 | 10,872 | +3% | 1 | 1 | 0% | 1,887 | 3,635 | +93% | 0 | 0 | — |
case-10 | pass→pass | 4,266 | 5,954 | +40% | 1 | 1 | 0% | 715 | 2,797 | +291% | 0 | 0 | — |
case-11 | pass→pass | 21,330 | 21,583 | +1% | 1 | 1 | 0% | 3,209 | 4,960 | +55% | 0 | 0 | — |
case-12 | fail→pass | 20,311 | 15,264 | -25% | 1 | 1 | 0% | 3,153 | 4,255 | +35% | 0 | 0 | — |
case-13 | fail→pass | 7,089 | 2,839 | -60% | 1 | 1 | 0% | 1,105 | 2,152 | +95% | 0 | 0 | — |
case-14 | fail→pass | 10,565 | 2,682 | -75% | 1 | 1 | 0% | 1,730 | 2,175 | +26% | 0 | 0 | — |
case-15 | pass→pass | 17,359 | 16,555 | -5% | 1 | 1 | 0% | 2,728 | 4,525 | +66% | 0 | 0 | — |
case-16 | pass→pass | 13,273 | 15,415 | +16% | 1 | 1 | 0% | 2,117 | 4,220 | +99% | 0 | 0 | — |
case-17 | pass→pass | 14,337 | 11,915 | -17% | 1 | 1 | 0% | 2,178 | 3,656 | +68% | 0 | 0 | — |
case-18 | pass→pass | 15,126 | 17,106 | +13% | 1 | 1 | 0% | 2,485 | 4,386 | +76% | 0 | 0 | — |
case-19 | pass→pass | 8,634 | 10,637 | +23% | 1 | 1 | 0% | 1,378 | 3,436 | +149% | 0 | 0 | — |
case-20 | fail→pass | 16,766 | 17,071 | +2% | 1 | 1 | 0% | 2,907 | 4,653 | +60% | 0 | 0 | — |
case-21 | fail→pass | 12,458 | 6,676 | -46% | 1 | 1 | 0% | 1,966 | 2,807 | +43% | 0 | 0 | — |
case-22 | pass→pass | 12,232 | 15,478 | +27% | 1 | 1 | 0% | 2,044 | 4,216 | +106% | 0 | 0 | — |
case-23 | fail→pass | 10,653 | 3,846 | -64% | 1 | 1 | 0% | 1,799 | 2,393 | +33% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 23 cases were attempted. The headline lift of +26 percentage points is the difference between those two pass rates over the 23 comparable cases. 1 case got worse with the skill loaded, and it is included in that figure.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.