{ "rubric": "K1-K10", "note": "Deterministic: K2,K3,K5,K6,refCountConsistency,K10(siblingScopeOverlap). LLM-judge (operator-gated): K1,K4,K7,K8,K9.", "skills": [ { "name": "ms-ai-advisor", "skillMd": "skills/ms-ai-advisor/SKILL.md", "refFilesActual": 62, "refCountsPerFolder": { "architecture": 16, "copilot-extensibility": 22, "development": 1, "platforms": 5, "prompt-engineering": 18 }, "deterministic": { "K2_descriptionFormat": { "quotedPhrases": 6, "useWhenForm": true, "imperativeStart": false, "pass": true }, "K3_bodyLength": { "bodyLines": 245, "pass": true }, "K5_progressiveDisclosure": { "namedFileLinks": 25, "folderRefs": 5, "totalRefFiles": 62, "namedRatio": 0.4032, "pass": true, "sampleNamed": [ "references/architecture/diagram-prompt-templates.md", "references/platforms/azure-ai-foundry.md", "references/platforms/m365-copilot.md", "references/platforms/copilot-studio.md", "references/platforms/power-platform.md" ] }, "K6_routingTable": { "namedStartFiles": 25, "pass": true }, "refCountConsistency": { "consistent": true, "mismatches": [] }, "K9_timeSensitiveHints": { "timeSensitiveTokenHits": 8, "sample": [ "Preview", "GA", "preview", "2026", "v3.0" ] }, "K10_siblingScopeOverlap": { "maxCombined": 4.5833, "worstSibling": "ms-ai-engineering", "pass": true, "threshold": 7 } }, "judgeInputs": { "description": "Microsoft AI platform selection — choosing between Azure AI Foundry, M365 Copilot, Copilot Studio, Power Platform, Azure OpenAI, and Microsoft Agent Framework for a given scenario. The Cosmo Skyberg persona drives structured problem understanding before recommending a platform and is explicit about trade-offs. Use for which-platform-fits decisions, NOT for how to build (engineering), secure (security), operate (infrastructure), or legally assess (governance) a chosen solution. Triggers on: \"which Microsoft AI platform\", \"Copilot vs Foundry\", \"Copilot Studio or Azure AI Foundry\", \"help me choose an AI platform\", \"Cosmo\", \"/architect\".", "bodyLines": 245, "refFileSample": [ "skills/ms-ai-advisor/references/architecture/adr-template.md", "skills/ms-ai-advisor/references/architecture/ai-utredning-template.md", "skills/ms-ai-advisor/references/architecture/alternativanalyse-methodology.md", "skills/ms-ai-advisor/references/architecture/capacity-feasibility-benchmarks.md", "skills/ms-ai-advisor/references/architecture/cost-models.md", "skills/ms-ai-advisor/references/architecture/decision-trees.md", "skills/ms-ai-advisor/references/architecture/diagram-prompt-templates.md", "skills/ms-ai-advisor/references/architecture/licensing-matrix.md" ] }, "judge": { "_updated": "S11 (2026-06-20) — K1 authoritative (blinded judge vs curated 20-set); description tightened against over-trigging", "K1_triggerPrecision": { "provisional": false, "inDomainHitRate": 1, "outDomainFalsePositiveRate": 0, "precision": 1, "pass": true, "misclassified": [], "notes": "S11 authoritative: blinded judge vs operator-curated 20-set. 10/10 in-domain hits, 0/10 false positives. Over-trigging fix VALIDATED — judge cited the new 'NOT for build/secure/operate/legally-assess' exclusion when rejecting all four sibling-domain prompts (engineering/security/governance/infra)." }, "K4_noDuplication": { "score": 5, "pass": true, "evidence": "Body = persona + 7-phase workflow + ref-index; no ref detail reproduced. Only internal MCP-table redundancy (SKILL-internal, not SKILL<->ref)." }, "K7_imperativeStyle": { "ratio": 1, "pass": true, "notes": "10/10 sampled instruction sentences imperative." }, "K8_sourceCitation": { "ratio": 0.8, "pass": true, "notes": "AT THRESHOLD: architecture/decision-trees.md lacks dated header (footer source only). Add dated header to harden margin." }, "K9_noTimeSensitive": { "pass": true, "findings": [ "Only meta-instructions (preview/GA as dynamic-to-verify) + stable identifiers (M365 SKUs, MADR v3.0). No stale-able product claim in body." ] } } }, { "name": "ms-ai-engineering", "skillMd": "skills/ms-ai-engineering/SKILL.md", "refFilesActual": 153, "refCountsPerFolder": { "agent-orchestration": 24, "api-management": 19, "azure-ai-services": 20, "data-engineering": 22, "mlops-genaiops": 22, "multi-modal": 18, "rag-architecture": 28 }, "deterministic": { "K2_descriptionFormat": { "quotedPhrases": 6, "useWhenForm": true, "imperativeStart": false, "pass": true }, "K3_bodyLength": { "bodyLines": 164, "pass": true }, "K5_progressiveDisclosure": { "namedFileLinks": 35, "folderRefs": 10, "totalRefFiles": 153, "namedRatio": 0.2288, "pass": true, "sampleNamed": [ "references/rag-architecture/rag-core-patterns.md", "references/rag-architecture/agentic-rag-patterns.md", "references/rag-architecture/azure-ai-search-setup.md", "references/rag-architecture/chunking-strategies.md", "references/rag-architecture/hybrid-search-configuration.md" ] }, "K6_routingTable": { "namedStartFiles": 35, "pass": true }, "refCountConsistency": { "consistent": true, "mismatches": [] }, "K9_timeSensitiveHints": { "timeSensitiveTokenHits": 3, "sample": [ "ga", "preview", "GA" ] }, "K10_siblingScopeOverlap": { "maxCombined": 7.4167, "worstSibling": "ms-ai-infrastructure", "pass": false, "threshold": 7 } }, "judgeInputs": { "description": "Deep technical guidance for building AI solutions in the Microsoft stack — RAG architecture, multi-agent orchestration, Azure AI Services, data engineering with Fabric, MLOps/GenAIOps, multimodal AI, API Management for AI. Triggers on: \"RAG architecture on Azure\", \"multi-agent orchestration pattern\", \"MLOps for generative AI\", \"Azure AI Search\", \"Semantic Kernel agent\", \"Fabric data pipeline\".", "bodyLines": 164, "refFileSample": [ "skills/ms-ai-engineering/references/agent-orchestration/agent-365-governance-and-deployment.md", "skills/ms-ai-engineering/references/agent-orchestration/agent-autonomy-and-control-governance.md", "skills/ms-ai-engineering/references/agent-orchestration/agent-compliance-and-audit-trails.md", "skills/ms-ai-engineering/references/agent-orchestration/agent-cost-optimization-strategies.md", "skills/ms-ai-engineering/references/agent-orchestration/agent-ecosystem-and-plugin-marketplace.md", "skills/ms-ai-engineering/references/agent-orchestration/agent-evaluation-testing-frameworks.md", "skills/ms-ai-engineering/references/agent-orchestration/agent-feedback-and-learning-loops.md", "skills/ms-ai-engineering/references/agent-orchestration/agent-latency-optimization.md" ] }, "judge": { "_updated": "S11 (2026-06-20) — K1 authoritative (blinded judge vs curated 20-set); K9 prior S10", "K1_triggerPrecision": { "provisional": false, "inDomainHitRate": 1, "outDomainFalsePositiveRate": 0, "precision": 1, "pass": true, "misclassified": [], "notes": "S11 authoritative: blinded judge vs operator-curated 20-set. 10/10 in-domain hits, 0/10 false positives. Clean sibling separation (advisor/governance/security/infra/off-topic not triggered)." }, "K4_noDuplication": { "score": 5, "pass": true, "evidence": "S10 re-judge: 7 section intros are orientation prose routing to references// + named kjernefiler; the two body tables (RAG-vs-finetuning, MLOps test-types) have no verbatim row-match in refs. No duplication." }, "K7_imperativeStyle": { "ratio": 1, "pass": true, "notes": "10/10 sampled instruction sentences imperative." }, "K8_sourceCitation": { "ratio": 1, "pass": true, "notes": "5/5 dated headers across 5 domains; format inconsistent (EN/NO, month vs day granularity)." }, "K9_noTimeSensitive": { "pass": true, "findings": [] } } }, { "name": "ms-ai-governance", "skillMd": "skills/ms-ai-governance/SKILL.md", "refFilesActual": 78, "refCountsPerFolder": { "monitoring-observability": 18, "norwegian-public-sector-governance": 30, "responsible-ai": 30 }, "deterministic": { "K2_descriptionFormat": { "quotedPhrases": 8, "useWhenForm": true, "imperativeStart": false, "pass": true }, "K3_bodyLength": { "bodyLines": 289, "pass": true }, "K5_progressiveDisclosure": { "namedFileLinks": 25, "folderRefs": 5, "totalRefFiles": 78, "namedRatio": 0.3205, "pass": true, "sampleNamed": [ "references/norwegian-public-sector-governance/utredningsinstruksen-ai-methodology.md", "references/norwegian-public-sector-governance/forvaltningsloven-ai-decisions.md", "references/norwegian-public-sector-governance/samfunnsokonomisk-analyse-nnv.md", "references/norwegian-public-sector-governance/gevinstrealisering-dfo-methodology.md", "references/norwegian-public-sector-governance/anskaffelser-ai-procurement-framework.md" ] }, "K6_routingTable": { "namedStartFiles": 25, "pass": true }, "refCountConsistency": { "consistent": true, "mismatches": [] }, "K9_timeSensitiveHints": { "timeSensitiveTokenHits": 1, "sample": [ "2024" ] }, "K10_siblingScopeOverlap": { "maxCombined": 6.6667, "worstSibling": "ms-ai-engineering", "pass": true, "threshold": 7 } }, "judgeInputs": { "description": "Norwegian public sector AI compliance, utredningsinstruksen for AI, EU AI Act risk classification, DPIA for AI systems, cross-border personal-data transfer assessment (Schrems II / TIA), Digdir architecture principles, responsible AI governance, monitoring and observability. Triggers on: \"Norwegian public sector AI compliance\", \"AI Act risk classification\", \"DPIA for AI system\", \"Schrems II data transfer\", \"overføring av persondata til tredjeland\", \"Digdir architecture principles\", \"ansvarlig AI i offentlig sektor\", \"Forvaltningsloven AI\".", "bodyLines": 289, "refFileSample": [ "skills/ms-ai-governance/references/monitoring-observability/alerting-strategies-escalation.md", "skills/ms-ai-governance/references/monitoring-observability/anomaly-detection-ai-systems.md", "skills/ms-ai-governance/references/monitoring-observability/application-insights-llm-monitoring.md", "skills/ms-ai-governance/references/monitoring-observability/azure-monitor-setup-ai-workloads.md", "skills/ms-ai-governance/references/monitoring-observability/compliance-monitoring-ai-governance.md", "skills/ms-ai-governance/references/monitoring-observability/cost-monitoring-cost-attribution.md", "skills/ms-ai-governance/references/monitoring-observability/custom-dashboards-ai-operations.md", "skills/ms-ai-governance/references/monitoring-observability/data-residency-audit-monitoring.md" ] }, "judge": { "_updated": "S11 (2026-06-20) — K1 authoritative (blinded judge vs curated 20-set); Schrems II trigger added; K4+K9 prior S9", "K1_triggerPrecision": { "provisional": false, "inDomainHitRate": 1, "outDomainFalsePositiveRate": 0, "precision": 1, "pass": true, "misclassified": [], "notes": "S11 authoritative: blinded judge vs operator-curated 20-set. 10/10 in-domain hits, 0/10 false positives (was 0.85). Schrems II recall fix VALIDATED — judge triggered on Schrems II / overføring-til-tredjeland prompts via the two new trigger phrases." }, "K4_noDuplication": { "score": 5, "pass": true, "evidence": "S9 FIX: §6.2 now a compact decision-flow with explicit pointers ('[full forbudsliste i ai-act-classification-methodology.md]', '[åtte kategorier ...]') — Art.5 + Annex III lists no longer enumerated in body; they live only in references/responsible-ai/ai-act-classification-methodology.md. §2.1 is the single 4-level overview table ('ikke gjenta dem her'). §6.1 (DPIA tree), §1.2 (Digdir table), §6.3/§6.4 are routing/orientation, not verbatim copies of ref files." }, "K7_imperativeStyle": { "ratio": 1, "pass": true, "notes": "10/10 sampled instruction sentences imperative/infinitive." }, "K8_sourceCitation": { "ratio": 1, "pass": true, "notes": "5/5 sampled refs carry Last updated + Status + Category headers." }, "K9_noTimeSensitive": { "pass": true, "findings": [] } } }, { "name": "ms-ai-infrastructure", "skillMd": "skills/ms-ai-infrastructure/SKILL.md", "refFilesActual": 34, "refCountsPerFolder": { "bcdr": 16, "hybrid-edge": 18 }, "deterministic": { "K2_descriptionFormat": { "quotedPhrases": 5, "useWhenForm": true, "imperativeStart": false, "pass": true }, "K3_bodyLength": { "bodyLines": 279, "pass": true }, "K5_progressiveDisclosure": { "namedFileLinks": 33, "folderRefs": 5, "totalRefFiles": 34, "namedRatio": 0.9706, "pass": true, "sampleNamed": [ "references/bcdr/multi-region-azure-openai-deployment.md", "references/bcdr/rto-rpo-planning-ai-services.md", "references/bcdr/backup-recovery-strategies-ai-workloads.md", "references/bcdr/failover-testing-ai-services.md", "references/bcdr/chaos-engineering-ai-systems.md" ] }, "K6_routingTable": { "namedStartFiles": 33, "pass": true }, "refCountConsistency": { "consistent": true, "mismatches": [] }, "K9_timeSensitiveHints": { "timeSensitiveTokenHits": 6, "sample": [ "preview", "GA" ] }, "K10_siblingScopeOverlap": { "maxCombined": 7.4167, "worstSibling": "ms-ai-engineering", "pass": false, "threshold": 7 } }, "judgeInputs": { "description": "Disaster recovery for AI workloads, multi-region Azure AI deployment, hybrid or edge AI architecture, sovereign cloud for Norway, offline-first AI patterns, AI infrastructure resilience. Covers BCDR, Azure Arc for AI, ONNX Runtime edge deployment, disconnected scenarios, Norwegian data sovereignty. Triggers on: \"disaster recovery for AI workloads\", \"edge AI deployment\", \"sovereign cloud AI\", \"Azure Arc for AI\", \"BCDR for AI\".", "bodyLines": 279, "refFileSample": [ "skills/ms-ai-infrastructure/references/bcdr/ai-foundry-disaster-recovery-planning.md", "skills/ms-ai-infrastructure/references/bcdr/backup-recovery-strategies-ai-workloads.md", "skills/ms-ai-infrastructure/references/bcdr/capacity-planning-dr-configurations.md", "skills/ms-ai-infrastructure/references/bcdr/chaos-engineering-ai-systems.md", "skills/ms-ai-infrastructure/references/bcdr/compliance-requirements-bcdr.md", "skills/ms-ai-infrastructure/references/bcdr/cost-analysis-dr-configurations.md", "skills/ms-ai-infrastructure/references/bcdr/data-replication-patterns-ai.md", "skills/ms-ai-infrastructure/references/bcdr/failover-testing-ai-services.md" ] }, "judge": { "_updated": "S11 (2026-06-20) — K1 authoritative (blinded judge vs curated 20-set); K9 prior S10", "K1_triggerPrecision": { "provisional": false, "inDomainHitRate": 1, "outDomainFalsePositiveRate": 0, "precision": 1, "pass": true, "misclassified": [], "notes": "S11 authoritative: blinded judge vs operator-curated 20-set. 10/10 in-domain hits, 0/10 false positives. BCDR/edge/sovereign/hybrid in-domain all triggered; engineering/governance/security/advisor/cost correctly excluded." }, "K4_noDuplication": { "score": 5, "pass": true, "evidence": "S10 re-judge: consistent summary+pointer pattern; §1.2 RTO/RPO now ~2 lines delegating to bcdr/rto-rpo-planning-ai-services.md (265 lines). SLA table replaced by relative-guidance prose. No procedural duplication." }, "K7_imperativeStyle": { "ratio": 1, "pass": true, "notes": "10/10 sampled instruction sentences imperative/infinitive." }, "K8_sourceCitation": { "ratio": 1, "pass": true, "notes": "5/5 'Last updated: 2026-02' + Status; no source-URL on header line." }, "K9_noTimeSensitive": { "pass": true, "findings": [] } } }, { "name": "ms-ai-security", "skillMd": "skills/ms-ai-security/SKILL.md", "refFilesActual": 62, "refCountsPerFolder": { "ai-security-engineering": 22, "cost-optimization": 22, "performance-scalability": 18 }, "deterministic": { "K2_descriptionFormat": { "quotedPhrases": 6, "useWhenForm": true, "imperativeStart": false, "pass": true }, "K3_bodyLength": { "bodyLines": 191, "pass": true }, "K5_progressiveDisclosure": { "namedFileLinks": 20, "folderRefs": 6, "totalRefFiles": 62, "namedRatio": 0.3226, "pass": true, "sampleNamed": [ "references/ai-security-engineering/security-scoring-rubrics-6x5.md", "references/ai-security-engineering/ai-security-scoring-framework.md", "references/ai-security-engineering/owasp-llm-top10-azure-mitigations.md", "references/ai-security-engineering/defender-threat-protection-ai-services.md", "references/ai-security-engineering/secure-model-deployment-hardening.md" ] }, "K6_routingTable": { "namedStartFiles": 20, "pass": true }, "refCountConsistency": { "consistent": true, "mismatches": [] }, "K9_timeSensitiveHints": { "timeSensitiveTokenHits": 1, "sample": [ "2025" ] }, "K10_siblingScopeOverlap": { "maxCombined": 6.5, "worstSibling": "ms-ai-governance", "pass": true, "threshold": 7 } }, "judgeInputs": { "description": "Security assessment, cost estimation, OWASP LLM Top 10 mitigations, performance optimization for AI on Microsoft stack. Deterministic 6x5 security scoring, P10/P50/P90 cost confidence intervals, FinOps practices. Triggers on: \"security assessment for AI\", \"AI threat modeling\", \"cost estimation for Azure AI\", \"FinOps for AI workloads\", \"OWASP LLM\", \"kostnadsestimat for AI-løsning\".", "bodyLines": 191, "refFileSample": [ "skills/ms-ai-security/references/ai-security-engineering/adversarial-input-robustness-testing.md", "skills/ms-ai-security/references/ai-security-engineering/ai-incident-response-procedures.md", "skills/ms-ai-security/references/ai-security-engineering/ai-prompt-shield-network.md", "skills/ms-ai-security/references/ai-security-engineering/ai-red-team-operations-practical.md", "skills/ms-ai-security/references/ai-security-engineering/ai-security-scoring-framework.md", "skills/ms-ai-security/references/ai-security-engineering/ai-threat-modeling-stride.md", "skills/ms-ai-security/references/ai-security-engineering/content-safety-filter-calibration.md", "skills/ms-ai-security/references/ai-security-engineering/data-leakage-prevention-ai.md" ] }, "judge": { "_updated": "S11 (2026-06-20) — K1 authoritative (blinded judge vs curated 20-set); K4+K9 prior S9", "K1_triggerPrecision": { "provisional": false, "inDomainHitRate": 1, "outDomainFalsePositiveRate": 0, "precision": 1, "pass": true, "misclassified": [], "notes": "S11 authoritative: blinded judge vs operator-curated 20-set. 10/10 in-domain hits, 0/10 false positives. Borderline content-safety/TCO correctly included; sibling governance/engineering/infra/advisor correctly excluded." }, "K4_noDuplication": { "score": 5, "pass": true, "evidence": "S9 re-judge (cold, post-fix): (a) 6x5 weights — body L50 routes to security-scoring-rubrics-6x5.md ('Ikke dupliser vekttallene her'), no body numbers; (b) risk-classification thresholds — body L54 routes to same rubric ('Ikke dupliser terskeltallene her'), canonical mapping incl. 1.00-1.49 Uakseptabel lives only in rubric; (c) P10/P50/P90 — body L94 affirms 'per komponent (ikke flat multiplikator)' owned by deterministic-cost-calculation-model.md §3, concrete factors only in cost model — body affirms, does not contradict; (d) OWASP table + §3 perf are routing/orientation with explicit volatile-numbers-live-in-refs note. No duplication/contradiction." }, "K7_imperativeStyle": { "ratio": 1, "pass": true, "notes": "10/10 sampled instruction sentences imperative/infinitive." }, "K8_sourceCitation": { "ratio": 1, "pass": true, "notes": "5/5 dated headers + Status; 3/5 also carry Verified: MCP ." }, "K9_noTimeSensitive": { "pass": true, "findings": [] } } } ] }