diff --git a/scripts/kb-eval/data/eval-baseline.json b/scripts/kb-eval/data/eval-baseline.json
new file mode 100644
index 0000000..9b92ff8
--- /dev/null
+++ b/scripts/kb-eval/data/eval-baseline.json
@@ -0,0 +1,500 @@
+{
+  "rubric": "K1-K9",
+  "note": "Deterministic: K2,K3,K5,K6,refCountConsistency. 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": 5,
+          "useWhenForm": true,
+          "imperativeStart": false,
+          "pass": true
+        },
+        "K3_bodyLength": {
+          "bodyLines": 240,
+          "pass": true
+        },
+        "K5_progressiveDisclosure": {
+          "namedFileLinks": 1,
+          "folderRefs": 5,
+          "totalRefFiles": 62,
+          "namedRatio": 0.0161,
+          "pass": false,
+          "sampleNamed": [
+            "references/architecture/diagram-prompt-templates.md"
+          ]
+        },
+        "K6_routingTable": {
+          "namedStartFiles": 1,
+          "pass": true
+        },
+        "refCountConsistency": {
+          "consistent": true,
+          "mismatches": []
+        },
+        "K9_timeSensitiveHints": {
+          "timeSensitiveTokenHits": 7,
+          "sample": [
+            "Preview",
+            "GA",
+            "preview",
+            "v3.0"
+          ]
+        }
+      },
+      "judgeInputs": {
+        "description": "Microsoft AI architecture guidance, choosing between Azure AI platforms, Copilot vs Foundry trade-offs. Cosmo Skyberg persona guides through structured problem understanding before technology selection. Specialist in Azure AI Foundry, M365 Copilot, Copilot Studio, Power Platform, Azure OpenAI, Microsoft Agent Framework. Triggers on: \"Microsoft AI architecture\", \"Copilot vs Foundry\", \"which Microsoft AI platform\", \"Cosmo\", \"/architect\".",
+        "bodyLines": 240,
+        "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": {
+        "K1_triggerPrecision": {
+          "provisional": true,
+          "precision": 1,
+          "notes": "20/20 from description. BORDERLINE: cost/diagram/DPIA/AI-Act prompts are MS-AI-adjacent; broad 'Microsoft AI architecture' phrase risks over-triggering vs sibling commands. Operator must curate + stress-test sibling overlap."
+        },
+        "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": 150,
+          "pass": true
+        },
+        "K5_progressiveDisclosure": {
+          "namedFileLinks": 0,
+          "folderRefs": 10,
+          "totalRefFiles": 153,
+          "namedRatio": 0,
+          "pass": false,
+          "sampleNamed": []
+        },
+        "K6_routingTable": {
+          "namedStartFiles": 0,
+          "pass": false
+        },
+        "refCountConsistency": {
+          "consistent": false,
+          "mismatches": [
+            {
+              "folder": "agent-orchestration",
+              "cited": 20,
+              "actual": 24
+            },
+            {
+              "folder": "agent-orchestration",
+              "citedMultiple": [
+                24,
+                20
+              ],
+              "actual": 24
+            }
+          ]
+        },
+        "K9_timeSensitiveHints": {
+          "timeSensitiveTokenHits": 3,
+          "sample": [
+            "GA",
+            "preview"
+          ]
+        }
+      },
+      "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": 150,
+        "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": {
+        "K1_triggerPrecision": {
+          "provisional": true,
+          "precision": 0.95,
+          "notes": "19/20. Miss: 'Semantic Kernel agent governance' overlaps security/governance because description lists 'governance' under agent-orchestration. Operator must curate."
+        },
+        "K4_noDuplication": {
+          "score": 4,
+          "pass": true,
+          "evidence": "Mostly router. Two inline tables (RAG-vs-finetuning; MLOps test-types w/ Ragas/red-teaming) restate comparison content also in ref files — summary-level, not verbatim."
+        },
+        "K7_imperativeStyle": {
+          "ratio": 0.9,
+          "pass": true,
+          "notes": "9/10; section intros are descriptive prose by design."
+        },
+        "K8_sourceCitation": {
+          "ratio": 1,
+          "pass": true,
+          "notes": "5/5 dated headers; schema drift ('Dato:' vs 'Last updated'); no source-URL in headers."
+        },
+        "K9_noTimeSensitive": {
+          "pass": false,
+          "findings": [
+            "Line 119: 'Foundry Agent Service GA' — explicit GA-status claim in body",
+            "GPT-4o/Whisper/text-embedding-3/Florence — version-pinned product names in body",
+            "Line 48: 'Agent Framework (erstatter Semantic Kernel Agents)' — lifecycle/transition claim",
+            "A2A/CUA/Foundry Workflows — era-bound feature names"
+          ]
+        }
+      }
+    },
+    {
+      "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": 6,
+          "useWhenForm": true,
+          "imperativeStart": false,
+          "pass": true
+        },
+        "K3_bodyLength": {
+          "bodyLines": 299,
+          "pass": true
+        },
+        "K5_progressiveDisclosure": {
+          "namedFileLinks": 24,
+          "folderRefs": 5,
+          "totalRefFiles": 78,
+          "namedRatio": 0.3077,
+          "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": 24,
+          "pass": true
+        },
+        "refCountConsistency": {
+          "consistent": false,
+          "mismatches": [
+            {
+              "folder": "norwegian-public-sector-governance",
+              "cited": 29,
+              "actual": 30
+            }
+          ]
+        },
+        "K9_timeSensitiveHints": {
+          "timeSensitiveTokenHits": 1,
+          "sample": [
+            "2024"
+          ]
+        }
+      },
+      "judgeInputs": {
+        "description": "Norwegian public sector AI compliance, utredningsinstruksen for AI, EU AI Act risk classification, DPIA for AI systems, Digdir architecture principles, responsible AI governance, monitoring and observability. Triggers on: \"Norwegian public sector AI compliance\", \"AI Act risk classification\", \"DPIA for AI system\", \"Digdir architecture principles\", \"ansvarlig AI i offentlig sektor\", \"Forvaltningsloven AI\".",
+        "bodyLines": 299,
+        "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": {
+        "K1_triggerPrecision": {
+          "provisional": true,
+          "precision": 0.95,
+          "notes": "19/20. Miss: Schrems II / data-transfer — body section 2.3 covers it but description has NO Schrems/dataoverføring keyword. Add trigger phrase to description."
+        },
+        "K4_noDuplication": {
+          "score": 3,
+          "pass": false,
+          "evidence": "Moderate duplication: §6.1 DPIA risk-factor tree (incl. >=2-faktorer threshold) duplicates dpia-norwegian-methodology-ai.md; §6.2 + §2.1 AI Act taxonomy duplicates ai-act-classification-methodology.md; §1.2 Digdir 7-principle table restates per-principle files. Regulatory update must be applied twice."
+        },
+        "K7_imperativeStyle": {
+          "ratio": 1,
+          "pass": true,
+          "notes": "10/10 imperative."
+        },
+        "K8_sourceCitation": {
+          "ratio": 1,
+          "pass": true,
+          "notes": "5/5 dated headers + Status + inline source citations (Lovdata/NSM/europalov). BONUS DEFECT: SKILL.md line 191 references 'drift-detection-automated-retraining.md' which does NOT exist (actual: model-performance-drift-detection.md) — broken ref path."
+        },
+        "K9_noTimeSensitive": {
+          "pass": false,
+          "findings": [
+            "Line 121: 'Microsoft EU Data Boundary ... Azure OpenAI (Sweden Central, West Europe)' — volatile region/availability claim in body (most genuine finding)",
+            "Lines 81/121: 'Regulation 2024/1689' + 'Schrems II (C-311/18)' — legal identifiers (stable, borderline)",
+            "§2.1/§6.2 AI Act obligation status presented without Digital Omnibus caveat — may date"
+          ]
+        }
+      }
+    },
+    {
+      "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": 290,
+          "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": 7,
+          "sample": [
+            "preview",
+            "GA",
+            "v2"
+          ]
+        }
+      },
+      "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": 290,
+        "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": {
+        "K1_triggerPrecision": {
+          "provisional": true,
+          "precision": 1,
+          "notes": "20/20. Tightly scoped + named trigger phrases, low false-positive risk. Borderline (hybrid RAG, multi-region cost) need real-corpus validation. Operator must curate."
+        },
+        "K4_noDuplication": {
+          "score": 4,
+          "pass": true,
+          "evidence": "Largely summary/routing w/ '> Ref:' deferral. Minor: RTO/RPO table + SLA table put specific values in body that also live in ref files."
+        },
+        "K7_imperativeStyle": {
+          "ratio": 1,
+          "pass": true,
+          "notes": "10/10 imperative (concentrated in per-section directives)."
+        },
+        "K8_sourceCitation": {
+          "ratio": 1,
+          "pass": true,
+          "notes": "5/5 'Last updated: 2026-02' + Status. NO source-URL in headers; identical batch date reads as generation timestamp, not per-file verification."
+        },
+        "K9_noTimeSensitive": {
+          "pass": false,
+          "findings": [
+            "Lines 93-98: SLA table w/ hardcoded percentages (99.9/99.999/99.95%) + 'Standard v2'",
+            "Lines 149/158/184/186/249: Phi-3/Phi-4 model versions + param counts (3.8B/14B) in body",
+            "Line 141: 'Azure Local (tidl. Azure Stack HCI)' rename note",
+            "Line 87: hardcoded 'peak + 30% buffer'"
+          ]
+        }
+      }
+    },
+    {
+      "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": 212,
+          "pass": true
+        },
+        "K5_progressiveDisclosure": {
+          "namedFileLinks": 10,
+          "folderRefs": 6,
+          "totalRefFiles": 62,
+          "namedRatio": 0.1613,
+          "pass": false,
+          "sampleNamed": [
+            "references/ai-security-engineering/security-scoring-rubrics-6x5.md",
+            "references/ai-security-engineering/ai-security-scoring-framework.md",
+            "references/ai-security-engineering/secure-model-deployment-hardening.md",
+            "references/ai-security-engineering/zero-trust-ai-services.md",
+            "references/cost-optimization/deterministic-cost-calculation-model.md"
+          ]
+        },
+        "K6_routingTable": {
+          "namedStartFiles": 10,
+          "pass": true
+        },
+        "refCountConsistency": {
+          "consistent": true,
+          "mismatches": []
+        },
+        "K9_timeSensitiveHints": {
+          "timeSensitiveTokenHits": 3,
+          "sample": [
+            "2025",
+            "GA",
+            "Preview"
+          ]
+        }
+      },
+      "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": 212,
+        "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": {
+        "K1_triggerPrecision": {
+          "provisional": true,
+          "precision": 0.95,
+          "notes": "19/20. Clean sibling separation (AI Act/DPIA/platform/RAG/BCDR not triggered). One ambiguity: model feature-comparison could be pulled by broad 'performance optimization for AI'. Operator must curate."
+        },
+        "K4_noDuplication": {
+          "score": 3,
+          "pass": false,
+          "evidence": "DUPLICATION-WITH-CONTRADICTION: body weighting table (L52-59 Standard: Identity 20/Network 15/Data 20/Content 20/Compliance 15/Monitoring 10) CONTRADICTS canonical rubric security-scoring-rubrics-6x5.md (Compliance 25/Data 20/Identity 20/Content 15/Network 10/Monitoring 10). Body scoring rule (weighted sum) also diverges from rubric (Ja-checkpoint count). Real correctness bug."
+        },
+        "K7_imperativeStyle": {
+          "ratio": 1,
+          "pass": true,
+          "notes": "10/10 imperative."
+        },
+        "K8_sourceCitation": {
+          "ratio": 1,
+          "pass": true,
+          "notes": "5/5 dated headers; mostly no header source-URL (URLs in body cost-register rows)."
+        },
+        "K9_noTimeSensitive": {
+          "pass": false,
+          "findings": [
+            "Line 73: 'OWASP LLM Top 10 (2025)' — dated standard version",
+            "Line 92: 'Defender ... GA for AI applications, Preview for AI agents' + 'ikke i Azure Government' — GA/preview + region status in body",
+            "Line 138: 'GPT-4o mini vs GPT-4o' — model versions",
+            "Lines 149-153: hardcoded perf/price figures (20-50ms, 5-10x, 50%/80%, Batch 50% @ 24h SLA)"
+          ]
+        }
+      }
+    }
+  ]
+}
diff --git a/scripts/kb-eval/data/judge-results.json b/scripts/kb-eval/data/judge-results.json
new file mode 100644
index 0000000..6c0e6c4
--- /dev/null
+++ b/scripts/kb-eval/data/judge-results.json
@@ -0,0 +1,43 @@
+{
+  "_meta": {
+    "rubric": "scripts/kb-eval/judge-prompt.md",
+    "judge_model": "opus",
+    "method": "5 parallel adversarial LLM-judges, one per skill",
+    "note": "K1 precision is PROVISIONAL — operator must curate the final 20 trigger-prompts per skill before K1 is authoritative."
+  },
+  "ms-ai-advisor": {
+    "K1_triggerPrecision": { "provisional": true, "precision": 1.0, "notes": "20/20 from description. BORDERLINE: cost/diagram/DPIA/AI-Act prompts are MS-AI-adjacent; broad 'Microsoft AI architecture' phrase risks over-triggering vs sibling commands. Operator must curate + stress-test sibling overlap." },
+    "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": 1.0, "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."] }
+  },
+  "ms-ai-engineering": {
+    "K1_triggerPrecision": { "provisional": true, "precision": 0.95, "notes": "19/20. Miss: 'Semantic Kernel agent governance' overlaps security/governance because description lists 'governance' under agent-orchestration. Operator must curate." },
+    "K4_noDuplication": { "score": 4, "pass": true, "evidence": "Mostly router. Two inline tables (RAG-vs-finetuning; MLOps test-types w/ Ragas/red-teaming) restate comparison content also in ref files — summary-level, not verbatim." },
+    "K7_imperativeStyle": { "ratio": 0.9, "pass": true, "notes": "9/10; section intros are descriptive prose by design." },
+    "K8_sourceCitation": { "ratio": 1.0, "pass": true, "notes": "5/5 dated headers; schema drift ('Dato:' vs 'Last updated'); no source-URL in headers." },
+    "K9_noTimeSensitive": { "pass": false, "findings": ["Line 119: 'Foundry Agent Service GA' — explicit GA-status claim in body", "GPT-4o/Whisper/text-embedding-3/Florence — version-pinned product names in body", "Line 48: 'Agent Framework (erstatter Semantic Kernel Agents)' — lifecycle/transition claim", "A2A/CUA/Foundry Workflows — era-bound feature names"] }
+  },
+  "ms-ai-governance": {
+    "K1_triggerPrecision": { "provisional": true, "precision": 0.95, "notes": "19/20. Miss: Schrems II / data-transfer — body section 2.3 covers it but description has NO Schrems/dataoverføring keyword. Add trigger phrase to description." },
+    "K4_noDuplication": { "score": 3, "pass": false, "evidence": "Moderate duplication: §6.1 DPIA risk-factor tree (incl. >=2-faktorer threshold) duplicates dpia-norwegian-methodology-ai.md; §6.2 + §2.1 AI Act taxonomy duplicates ai-act-classification-methodology.md; §1.2 Digdir 7-principle table restates per-principle files. Regulatory update must be applied twice." },
+    "K7_imperativeStyle": { "ratio": 1.0, "pass": true, "notes": "10/10 imperative." },
+    "K8_sourceCitation": { "ratio": 1.0, "pass": true, "notes": "5/5 dated headers + Status + inline source citations (Lovdata/NSM/europalov). BONUS DEFECT: SKILL.md line 191 references 'drift-detection-automated-retraining.md' which does NOT exist (actual: model-performance-drift-detection.md) — broken ref path." },
+    "K9_noTimeSensitive": { "pass": false, "findings": ["Line 121: 'Microsoft EU Data Boundary ... Azure OpenAI (Sweden Central, West Europe)' — volatile region/availability claim in body (most genuine finding)", "Lines 81/121: 'Regulation 2024/1689' + 'Schrems II (C-311/18)' — legal identifiers (stable, borderline)", "§2.1/§6.2 AI Act obligation status presented without Digital Omnibus caveat — may date"] }
+  },
+  "ms-ai-infrastructure": {
+    "K1_triggerPrecision": { "provisional": true, "precision": 1.0, "notes": "20/20. Tightly scoped + named trigger phrases, low false-positive risk. Borderline (hybrid RAG, multi-region cost) need real-corpus validation. Operator must curate." },
+    "K4_noDuplication": { "score": 4, "pass": true, "evidence": "Largely summary/routing w/ '> Ref:' deferral. Minor: RTO/RPO table + SLA table put specific values in body that also live in ref files." },
+    "K7_imperativeStyle": { "ratio": 1.0, "pass": true, "notes": "10/10 imperative (concentrated in per-section directives)." },
+    "K8_sourceCitation": { "ratio": 1.0, "pass": true, "notes": "5/5 'Last updated: 2026-02' + Status. NO source-URL in headers; identical batch date reads as generation timestamp, not per-file verification." },
+    "K9_noTimeSensitive": { "pass": false, "findings": ["Lines 93-98: SLA table w/ hardcoded percentages (99.9/99.999/99.95%) + 'Standard v2'", "Lines 149/158/184/186/249: Phi-3/Phi-4 model versions + param counts (3.8B/14B) in body", "Line 141: 'Azure Local (tidl. Azure Stack HCI)' rename note", "Line 87: hardcoded 'peak + 30% buffer'"] }
+  },
+  "ms-ai-security": {
+    "K1_triggerPrecision": { "provisional": true, "precision": 0.95, "notes": "19/20. Clean sibling separation (AI Act/DPIA/platform/RAG/BCDR not triggered). One ambiguity: model feature-comparison could be pulled by broad 'performance optimization for AI'. Operator must curate." },
+    "K4_noDuplication": { "score": 3, "pass": false, "evidence": "DUPLICATION-WITH-CONTRADICTION: body weighting table (L52-59 Standard: Identity 20/Network 15/Data 20/Content 20/Compliance 15/Monitoring 10) CONTRADICTS canonical rubric security-scoring-rubrics-6x5.md (Compliance 25/Data 20/Identity 20/Content 15/Network 10/Monitoring 10). Body scoring rule (weighted sum) also diverges from rubric (Ja-checkpoint count). Real correctness bug." },
+    "K7_imperativeStyle": { "ratio": 1.0, "pass": true, "notes": "10/10 imperative." },
+    "K8_sourceCitation": { "ratio": 1.0, "pass": true, "notes": "5/5 dated headers; mostly no header source-URL (URLs in body cost-register rows)." },
+    "K9_noTimeSensitive": { "pass": false, "findings": ["Line 73: 'OWASP LLM Top 10 (2025)' — dated standard version", "Line 92: 'Defender ... GA for AI applications, Preview for AI agents' + 'ikke i Azure Government' — GA/preview + region status in body", "Line 138: 'GPT-4o mini vs GPT-4o' — model versions", "Lines 149-153: hardcoded perf/price figures (20-50ms, 5-10x, 50%/80%, Batch 50% @ 24h SLA)"] }
+  }
+}
diff --git a/scripts/kb-eval/eval.mjs b/scripts/kb-eval/eval.mjs
new file mode 100644
index 0000000..4913a76
--- /dev/null
+++ b/scripts/kb-eval/eval.mjs
@@ -0,0 +1,257 @@
+#!/usr/bin/env node
+// eval.mjs — Skill quality baseline (rubrikk K1–K9) for ms-ai-architect.
+//
+// READ-ONLY by default. Computes the deterministic criteria (K2/K3/K5/K6 +
+// reference-count consistency) for every skill and extracts the inputs an LLM
+// judge needs for the semantic criteria (K1/K4/K7/K8/K9). The baseline file is
+// written ONLY with --write (operator gate), per the redesign spec.
+//
+// Usage:
+//   node scripts/kb-eval/eval.mjs            # human-readable summary
+//   node scripts/kb-eval/eval.mjs --json     # machine output (pure JSON to stdout)
+//   node scripts/kb-eval/eval.mjs --write    # also persist data/eval-baseline.json
+//
+// Zero dependencies. Reuses kb-update/lib/atomic-write.mjs for the gated write.
+
+import { readdirSync, readFileSync, existsSync, mkdirSync } from 'node:fs';
+import { join, dirname, relative } from 'node:path';
+import { fileURLToPath } from 'node:url';
+import { atomicWriteJson } from '../kb-update/lib/atomic-write.mjs';
+
+const __dirname = dirname(fileURLToPath(import.meta.url));
+const PLUGIN_ROOT = join(__dirname, '..', '..');
+const SKILLS_DIR = join(PLUGIN_ROOT, 'skills');
+const OUT_DIR = join(__dirname, 'data');
+const OUT_FILE = join(OUT_DIR, 'eval-baseline.json');
+
+// Thresholds — see spec "Rubrikk K1–K9".
+const K3_MAX_BODY_LINES = 500;
+const K5_MIN_NAMED_RATIO = 0.2;
+
+/** Recursively list .md files under dir. */
+function listMarkdown(dir) {
+  const out = [];
+  if (!existsSync(dir)) return out;
+  for (const e of readdirSync(dir, { withFileTypes: true })) {
+    const p = join(dir, e.name);
+    if (e.isDirectory()) out.push(...listMarkdown(p));
+    else if (e.isFile() && e.name.endsWith('.md')) out.push(p);
+  }
+  return out;
+}
+
+/** Count .md files per immediate subfolder of references/. */
+function refCountsPerFolder(refDir) {
+  const counts = {};
+  if (!existsSync(refDir)) return counts;
+  for (const e of readdirSync(refDir, { withFileTypes: true })) {
+    if (e.isDirectory()) counts[e.name] = listMarkdown(join(refDir, e.name)).length;
+  }
+  return counts;
+}
+
+/** Split YAML frontmatter from body. */
+export function splitFrontmatter(content) {
+  if (!content.startsWith('---')) return { frontmatter: '', body: content };
+  const close = content.indexOf('\n---', 3);
+  if (close === -1) return { frontmatter: '', body: content };
+  const bodyStart = content.indexOf('\n', close + 1);
+  return {
+    frontmatter: content.slice(0, bodyStart === -1 ? content.length : bodyStart + 1),
+    body: bodyStart === -1 ? '' : content.slice(bodyStart + 1),
+  };
+}
+
+/** Extract the (possibly folded) description value from frontmatter. */
+export function extractDescription(frontmatter) {
+  const lines = frontmatter.split('\n');
+  const out = [];
+  let collecting = false;
+  for (const line of lines) {
+    if (/^description:/.test(line)) {
+      collecting = true;
+      out.push(line.replace(/^description:\s*>?-?\s*/, ''));
+      continue;
+    }
+    if (collecting) {
+      if (/^[A-Za-z0-9_-]+:\s/.test(line) || line.trim() === '---' || line.trim() === '') {
+        if (line.trim() === '') continue; // folded blocks keep going across blanks
+        break;
+      }
+      out.push(line.trim());
+    }
+  }
+  return out.join(' ').replace(/\s+/g, ' ').trim();
+}
+
+/** K2 — description format: third-person/use-when OR >=3 quoted user phrases. */
+export function checkK2(description) {
+  const quoted = (description.match(/"[^"]+"/g) || []).length;
+  const useWhenForm = /should be used when|use this skill|triggers on/i.test(description);
+  const imperativeStart = /^(use|bruk|run|kjør)\b/i.test(description);
+  const pass = useWhenForm || quoted >= 3;
+  return { quotedPhrases: quoted, useWhenForm, imperativeStart, pass };
+}
+
+/** K3 — SKILL.md body length. */
+export function checkK3(body) {
+  const lines = body.replace(/\n+$/, '').split('\n').length;
+  return { bodyLines: lines, pass: lines <= K3_MAX_BODY_LINES };
+}
+
+/** K5/K6 — progressive disclosure: named .md links vs folder-only references. */
+export function checkK5K6(body, totalRefFiles) {
+  const named = [...new Set((body.match(/references\/[A-Za-z0-9_\-/]+\.md/g) || []))];
+  // folder references like `references/foo/` not immediately followed by a filename
+  const folderRefs = [...new Set((body.match(/references\/[A-Za-z0-9_-]+\/(?![A-Za-z0-9_-]+\.md)/g) || []))];
+  const namedRatio = totalRefFiles > 0 ? named.length / totalRefFiles : 0;
+  return {
+    K5: {
+      namedFileLinks: named.length,
+      folderRefs: folderRefs.length,
+      totalRefFiles,
+      namedRatio: Number(namedRatio.toFixed(4)),
+      pass: namedRatio >= K5_MIN_NAMED_RATIO,
+      sampleNamed: named.slice(0, 5),
+    },
+    K6: {
+      // routing table = at least one named start file the model can Read directly
+      namedStartFiles: named.length,
+      pass: named.length >= 1,
+    },
+  };
+}
+
+/** Reference-count consistency: cited per-folder counts vs actual on disk. */
+export function checkRefConsistency(body, actualPerFolder) {
+  const mismatches = [];
+  const seen = {};
+  // prose form: references/<folder>/ (N files)   and   `references/<folder>/` (N files)
+  const proseRe = /references\/([a-z0-9-]+)\/`?\s*\((\d+)\s*files?\)/gi;
+  // table form: `references/<folder>/` | N
+  const tableRe = /`references\/([a-z0-9-]+)\/`\s*\|\s*(\d+)/gi;
+  for (const re of [proseRe, tableRe]) {
+    let m;
+    while ((m = re.exec(body)) !== null) {
+      const folder = m[1];
+      const cited = Number(m[2]);
+      seen[folder] = seen[folder] || new Set();
+      seen[folder].add(cited);
+      const actual = actualPerFolder[folder];
+      if (actual !== undefined && cited !== actual) {
+        mismatches.push({ folder, cited, actual });
+      }
+    }
+  }
+  // also flag folders cited with two different numbers
+  for (const [folder, set] of Object.entries(seen)) {
+    if (set.size > 1) mismatches.push({ folder, citedMultiple: [...set], actual: actualPerFolder[folder] ?? null });
+  }
+  return { consistent: mismatches.length === 0, mismatches };
+}
+
+/** Light deterministic pre-scan for K9 (time-sensitive tokens in body). */
+function scanK9Hints(body) {
+  const tokens = body.match(/\b(GA|preview|deprecat\w*|retir\w*|20\d\d|v\d+(?:\.\d+)?)\b/gi) || [];
+  return { timeSensitiveTokenHits: tokens.length, sample: [...new Set(tokens)].slice(0, 12) };
+}
+
+function evalSkill(skillName) {
+  const skillDir = join(SKILLS_DIR, skillName);
+  const skillMd = join(skillDir, 'SKILL.md');
+  if (!existsSync(skillMd)) return null;
+  const content = readFileSync(skillMd, 'utf8');
+  const { frontmatter, body } = splitFrontmatter(content);
+  const description = extractDescription(frontmatter);
+  const refDir = join(skillDir, 'references');
+  const refFiles = listMarkdown(refDir);
+  const perFolder = refCountsPerFolder(refDir);
+
+  const K2 = checkK2(description);
+  const K3 = checkK3(body);
+  const { K5, K6 } = checkK5K6(body, refFiles.length);
+  const refConsistency = checkRefConsistency(body, perFolder);
+  const K9hints = scanK9Hints(body);
+
+  return {
+    name: skillName,
+    skillMd: relative(PLUGIN_ROOT, skillMd),
+    refFilesActual: refFiles.length,
+    refCountsPerFolder: perFolder,
+    deterministic: {
+      K2_descriptionFormat: K2,
+      K3_bodyLength: K3,
+      K5_progressiveDisclosure: K5,
+      K6_routingTable: K6,
+      refCountConsistency: refConsistency,
+      K9_timeSensitiveHints: K9hints,
+    },
+    // Pointers for the LLM-judge pass (K1/K4/K7/K8/K9). The judge reads the files
+    // directly; we only carry the description + sampled ref files here.
+    judgeInputs: {
+      description,
+      bodyLines: K3.bodyLines,
+      refFileSample: refFiles.slice(0, 8).map((f) => relative(PLUGIN_ROOT, f)),
+    },
+    // Filled in by the operator-gated LLM-judge merge step. Null = not yet judged.
+    judge: null,
+  };
+}
+
+function main() {
+  const args = process.argv.slice(2);
+  const jsonOut = args.includes('--json');
+  const doWrite = args.includes('--write');
+
+  const skillNames = readdirSync(SKILLS_DIR, { withFileTypes: true })
+    .filter((e) => e.isDirectory() && existsSync(join(SKILLS_DIR, e.name, 'SKILL.md')))
+    .map((e) => e.name)
+    .sort();
+
+  const skills = skillNames.map(evalSkill).filter(Boolean);
+
+  // Merge operator-gated LLM-judge results (K1/K4/K7/K8/K9) if present.
+  const judgeFile = join(OUT_DIR, 'judge-results.json');
+  if (existsSync(judgeFile)) {
+    const jr = JSON.parse(readFileSync(judgeFile, 'utf8'));
+    for (const s of skills) if (jr[s.name]) s.judge = jr[s.name];
+  }
+
+  const report = {
+    rubric: 'K1-K9',
+    note: 'Deterministic: K2,K3,K5,K6,refCountConsistency. LLM-judge (operator-gated): K1,K4,K7,K8,K9.',
+    skills,
+  };
+
+  if (doWrite) {
+    mkdirSync(OUT_DIR, { recursive: true });
+    atomicWriteJson(OUT_FILE, report);
+  }
+
+  if (jsonOut) {
+    process.stdout.write(JSON.stringify(report) + '\n');
+    return;
+  }
+
+  // Human-readable summary
+  console.log(`\nKB skill eval — ${skills.length} skills (deterministic pass)\n`);
+  for (const s of skills) {
+    const d = s.deterministic;
+    const flag = (b) => (b ? 'PASS' : 'FAIL');
+    console.log(`■ ${s.name}  (${s.refFilesActual} ref-filer)`);
+    console.log(`   K2 description : ${flag(d.K2_descriptionFormat.pass)} (quoted=${d.K2_descriptionFormat.quotedPhrases}, useWhen=${d.K2_descriptionFormat.useWhenForm})`);
+    console.log(`   K3 body ≤500   : ${flag(d.K3_bodyLength.pass)} (${d.K3_bodyLength.bodyLines} linjer)`);
+    console.log(`   K5 prog.disc.  : ${flag(d.K5_progressiveDisclosure.pass)} (navngitte=${d.K5_progressiveDisclosure.namedFileLinks}, mapper=${d.K5_progressiveDisclosure.folderRefs}, ratio=${d.K5_progressiveDisclosure.namedRatio})`);
+    console.log(`   K6 routing-tab : ${flag(d.K6_routingTable.pass)} (navngitte startfiler=${d.K6_routingTable.namedStartFiles})`);
+    const rc = d.refCountConsistency;
+    console.log(`   ref-tall       : ${flag(rc.consistent)}${rc.consistent ? '' : ' — ' + JSON.stringify(rc.mismatches)}`);
+    console.log(`   K9 hints       : ${d.K9_timeSensitiveHints.timeSensitiveTokenHits} tid-sensitive tokens i body`);
+    console.log('');
+  }
+  console.log(`(LLM-judge K1/K4/K7/K8/K9 kjøres som operatør-gated subagent-steg; baseline skrives med --write.)\n`);
+}
+
+// Run only when invoked directly (not when imported by tests).
+if (process.argv[1] && fileURLToPath(import.meta.url) === process.argv[1]) {
+  main();
+}
diff --git a/scripts/kb-eval/judge-prompt.md b/scripts/kb-eval/judge-prompt.md
new file mode 100644
index 0000000..341ddd9
--- /dev/null
+++ b/scripts/kb-eval/judge-prompt.md
@@ -0,0 +1,34 @@
+# LLM-judge-rubrikk (K1/K4/K7/K8/K9) — skill-kvalitet, ms-ai-architect
+
+Pinnet rubrikk for de semantiske eval-kriteriene som ikke kan måles deterministisk.
+Kjøres som operatør-gated subagent-steg (Opus, én dommer per skill). De deterministiske
+kriteriene (K2/K3/K5/K6 + ref-tall) måles av `eval.mjs`; denne dekker resten.
+
+Placeholders: `<NAME>` = skill-navn, `<ROOT>` = plugin-rot, `<PATH>` = sti til SKILL.md.
+
+---
+
+Du er en skill-kvalitets-dommer (LLM-as-judge) for ms-ai-architect. Vurder skillen
+`<NAME>` mot K1/K4/K7/K8/K9. Plugin-rot: `<ROOT>`. SKILL.md: `<PATH>`. Referansefiler:
+`<ROOT>/skills/<NAME>/references/`.
+
+Les SKILL.md i sin helhet. For K8: sample 5 tilfeldige referansefiler og les headeren deres.
+Vær streng og adversariell — ikke ros, ikke pynt på tall.
+
+- **K1 trigger-presisjon [PROVISIONAL]:** generer 10 in-domain + 10 out-of-domain prompts for
+  domenet. For hver, avgjør KUN fra `description`-feltet om skillen burde trigge. Rapporter
+  provisional precision (korrekte/20). FLAGG eksplisitt at operatør må kuratere de endelige 20.
+- **K4 ingen duplisering (SKILL.md ↔ ref-filer):** score 1–5 (5 = ingen duplisering). Finnes detalj
+  i SKILL.md body som dupliserer ref-filer? Gi konkret eksempel. pass = score ≥ 4.
+- **K7 imperativ/instruksjons-stil:** sample 10 instruksjonssetninger fra body; andel i
+  imperativ/infinitiv. pass = ratio ≥ 0.80.
+- **K8 kildehenvisning i ref-filer:** for 5 samplede ref-filer, har header `Last updated` /
+  `Verified` / kilde-URL? Rapporter andel. pass = ratio ≥ 0.80.
+- **K9 ingen tid-sensitiv info i SKILL.md body:** finnes datoer/versjoner/GA/preview-status DIREKTE
+  i body (ikke i ref-filer)? List funn. pass = ingen funn.
+
+RETURNER KUN dette JSON-objektet (ingen annen tekst, ingen markdown-fence):
+
+```
+{"skill":"<NAME>","K1_triggerPrecision":{"provisional":true,"precision":0.0,"notes":""},"K4_noDuplication":{"score":0,"pass":false,"evidence":""},"K7_imperativeStyle":{"ratio":0.0,"pass":false,"notes":""},"K8_sourceCitation":{"ratio":0.0,"pass":false,"sampledFiles":[],"notes":""},"K9_noTimeSensitive":{"pass":false,"findings":[]}}
+```
diff --git a/tests/kb-eval/test-eval.test.mjs b/tests/kb-eval/test-eval.test.mjs
new file mode 100644
index 0000000..27bf908
--- /dev/null
+++ b/tests/kb-eval/test-eval.test.mjs
@@ -0,0 +1,102 @@
+// tests/kb-eval/test-eval.test.mjs
+// Unit tests for the deterministic scoring functions in scripts/kb-eval/eval.mjs
+
+import { test } from 'node:test';
+import assert from 'node:assert/strict';
+import {
+  splitFrontmatter,
+  extractDescription,
+  checkK2,
+  checkK3,
+  checkK5K6,
+  checkRefConsistency,
+} from '../../scripts/kb-eval/eval.mjs';
+
+test('splitFrontmatter — separates frontmatter from body', () => {
+  const c = '---\nname: x\ndescription: hi\n---\n# Body\nline2\n';
+  const { body } = splitFrontmatter(c);
+  assert.ok(body.startsWith('# Body'), `body was: ${JSON.stringify(body)}`);
+  assert.ok(!body.includes('name: x'));
+});
+
+test('splitFrontmatter — no frontmatter returns full content as body', () => {
+  const { frontmatter, body } = splitFrontmatter('# No frontmatter\ntext');
+  assert.equal(frontmatter, '');
+  assert.equal(body, '# No frontmatter\ntext');
+});
+
+test('extractDescription — extracts folded description value', () => {
+  const fm = '---\nname: x\ndescription: >-\n  Deep guidance. Triggers on: "a", "b".\nother: y\n---\n';
+  const desc = extractDescription(fm);
+  assert.match(desc, /Deep guidance/);
+  assert.match(desc, /Triggers on/);
+  assert.ok(!desc.includes('other: y'));
+});
+
+test('checkK2 — >=3 quoted phrases passes', () => {
+  const r = checkK2('Some guidance. "alpha" and "beta" and "gamma".');
+  assert.equal(r.quotedPhrases, 3);
+  assert.equal(r.pass, true);
+});
+
+test('checkK2 — use-when form passes even without quotes', () => {
+  const r = checkK2('This skill should be used when designing X.');
+  assert.equal(r.useWhenForm, true);
+  assert.equal(r.pass, true);
+});
+
+test('checkK2 — plain description without quotes or use-when fails', () => {
+  const r = checkK2('A plain description of the domain.');
+  assert.equal(r.quotedPhrases, 0);
+  assert.equal(r.useWhenForm, false);
+  assert.equal(r.pass, false);
+});
+
+test('checkK3 — body within 500 lines passes', () => {
+  const r = checkK3('line1\nline2\nline3\n');
+  assert.equal(r.bodyLines, 3);
+  assert.equal(r.pass, true);
+});
+
+test('checkK3 — body over 500 lines fails', () => {
+  const r = checkK3('x\n'.repeat(501));
+  assert.equal(r.pass, false);
+  assert.ok(r.bodyLines > 500);
+});
+
+test('checkK5K6 — named file links count toward progressive disclosure', () => {
+  const body = 'See references/foo/bar.md and references/foo/baz.md for detail.';
+  const { K5, K6 } = checkK5K6(body, 10);
+  assert.equal(K5.namedFileLinks, 2);
+  assert.equal(K5.namedRatio, 0.2);
+  assert.equal(K5.pass, true); // 0.2 >= 0.20
+  assert.equal(K6.pass, true); // >=1 named start file
+});
+
+test('checkK5K6 — folder-only references fail K5 and K6', () => {
+  const body = 'For detailed guidance, see references/foo/ (28 files).';
+  const { K5, K6 } = checkK5K6(body, 10);
+  assert.equal(K5.namedFileLinks, 0);
+  assert.equal(K5.pass, false);
+  assert.equal(K6.pass, false);
+});
+
+test('checkRefConsistency — cited count differing from actual is a mismatch', () => {
+  const body = '| `references/foo/` | 20 | desc |';
+  const r = checkRefConsistency(body, { foo: 24 });
+  assert.equal(r.consistent, false);
+  assert.ok(r.mismatches.some((m) => m.folder === 'foo' && m.cited === 20 && m.actual === 24));
+});
+
+test('checkRefConsistency — matching count is consistent', () => {
+  const body = '| `references/foo/` | 24 | desc |';
+  const r = checkRefConsistency(body, { foo: 24 });
+  assert.equal(r.consistent, true);
+  assert.deepEqual(r.mismatches, []);
+});
+
+test('checkRefConsistency — same folder cited with two different numbers flags inconsistency', () => {
+  const body = 'prose says references/foo/ (24 files) but table `references/foo/` | 20 |';
+  const r = checkRefConsistency(body, { foo: 24 });
+  assert.equal(r.consistent, false);
+});