feat(ms-ai-architect): R22 decision-b Enhet 3 — Status-backfill 25 none + ai-act dual-header-dedup 4 (Missing Status/Last-updated 29+4→0) [skip-docs]

To operasjoner, én økt (⊥ R7), begge ren metadata-normalisering (verdi aldri fabrikkert).

Premiss-korreksjon (ground truth 2026-07-07): roadmap sa «27 none + 4 ai-act».
Målt: 29 mangler bold **Status:** = 25 rene none + 4 ai-act (plain Status: GA).
De «27» inkluderte 2 for mye — 2 filer (custom-dashboards-ai-operations,
zero-trust-ai-services) har bold **Status:** KUN forbi byte 500 (present for
full-fil-audit, usynlig for 500B header-parser) → egen header-slanking-residual
(§8-register), utenfor Enhet 3.

Op A — Status-backfill 25 rene none: utvidet backfill-status.mjs MANIFEST 14→39
(samme statusForFile + insertMetaField + hard per-fil-invariant, idempotent skip
på de 14 R21-gjorte). Alle 25 → **Status:** Established Practice (ingen matcher
template|matrix|benchmarks|register). Diff +25/-0.

Op B — ai-act dual-header-dedup (4 filer): ny driver dedup-plain-header.mjs + 2
rene primitiver i transform.mjs — boldifyPlainField (plain→bold, verdi bevart
byte-eksakt, header-scoped, idempotent) + dropRedundantPlainField (sletter plain
KUN når bold m/ identisk verdi beviser redundans; kaster ved avvik/manglende bold).
Per fil: plain Last updated: + Status: GA → bold (2026-06-18/2026-02, GA bevart),
redundant plain Category: fjernet. Hard per-fil-invariant (net -1 linje, begge
felt bold m/ bevart verdi, ingen plain-header igjen, body byte-identisk). Diff -12/+8.

Verifisering: test-backfill-status 8/8 + test-dedup-plain-header 13/13; audit
Missing Status 29→0, Missing English Last updated 4→0; skills-diff 29 filer
+33/-12 (kun **Status:** + 8 bold-swaps), diff-kontekst inspisert per fil; begge
drivere idempotent (re-run 0 writes); suite 806/806 exit 0; none=8 uendret (Enhet 4).

skills/ms-ai-engineering/references/mlops-genaiops/cost-optimization-mlops-pipelines.md

@@ -5,6 +5,7 @@
 **Relevans:** Azure Machine Learning, MLOps-implementering, FinOps for AI
 **Type:** reference
 **Source:** https://learn.microsoft.com/azure/machine-learning/concept-ml-pipelines
+**Status:** Established Practice
 
 ## Innhold
 

skills/ms-ai-engineering/references/mlops-genaiops/feedback-loops-continuous-improvement.md

@@ -6,6 +6,7 @@
 **Confidence:** HIGH (basert på offisiell Microsoft-dokumentasjon)
 **Type:** reference
 **Source:** https://learn.microsoft.com/azure/foundry/concepts/observability
+**Status:** Established Practice
 
 ## Innhold
 

skills/ms-ai-engineering/references/mlops-genaiops/genaiops-llm-specific-practices.md

@@ -6,6 +6,7 @@
 **Konfidensgrad:** Høy (basert på 18 MCP-kilder fra Microsoft Learn)
 **Type:** reference
 **Source:** https://learn.microsoft.com/python/api/overview/azure/ai-evaluation-readme
+**Status:** Established Practice
 
 ---
 

skills/ms-ai-engineering/references/mlops-genaiops/governance-audit-ml-operations.md

@@ -5,6 +5,7 @@
 **Confidence:** 95% (High — bygger på offisiell Microsoft-dokumentasjon og Azure-referansearkitekturer)
 **Type:** reference
 **Source:** https://learn.microsoft.com/azure/machine-learning/concept-responsible-ai-dashboard
+**Status:** Established Practice
 
 ## Innhold
 

skills/ms-ai-engineering/references/mlops-genaiops/inferencing-optimization-caching.md

@@ -5,6 +5,7 @@
 **Forfattet av:** Cosmo Skyberg, Senior Microsoft AI Solution Architect
 **Type:** reference
 **Source:** https://learn.microsoft.com/azure/machine-learning/concept-onnx
+**Status:** Established Practice
 
 ## Innhold
 

skills/ms-ai-engineering/references/mlops-genaiops/infrastructure-as-code-mlops.md

@@ -5,6 +5,7 @@
 **Forfatter:** Cosmo Skyberg, Senior Microsoft AI Solution Architect
 **Type:** reference
 **Source:** https://learn.microsoft.com/azure/aks/concepts-machine-learning-ops
+**Status:** Established Practice
 
 ## Innhold
 

skills/ms-ai-engineering/references/mlops-genaiops/llm-evaluation-production.md

@@ -5,6 +5,7 @@
 **Confidence:** High (basert på offisiell Microsoft dokumentasjon, Microsoft Foundry SDK, og MLflow 3)
 **Type:** reference
 **Source:** https://learn.microsoft.com/azure/foundry/concepts/observability
+**Status:** Established Practice
 
 ---
 

skills/ms-ai-engineering/references/mlops-genaiops/mlops-security-access-control.md

@@ -6,6 +6,7 @@
 **Confidence:** HIGH — Basert på offisiell Microsoft Learn dokumentasjon (8 MCP-oppslag, 16 kilder)
 **Type:** reference
 **Source:** https://learn.microsoft.com/azure/machine-learning/concept-enterprise-security
+**Status:** Established Practice
 
 ---
 

skills/ms-ai-engineering/references/mlops-genaiops/mlops-teams-collaboration-tools.md

@@ -6,6 +6,7 @@
 **Konfidensgradering:** ⭐⭐⭐⭐⭐ (Verifisert mot offisiell Microsoft-dokumentasjon)
 **Type:** reference
 **Source:** https://learn.microsoft.com/azure/machine-learning/overview-what-is-azure-machine-learning
+**Status:** Established Practice
 
 ## Innhold
 

skills/ms-ai-engineering/references/mlops-genaiops/model-deployment-strategies-azure.md

@@ -7,6 +7,7 @@
 **Type:** reference
 **Source:** https://learn.microsoft.com/azure/machine-learning/concept-model-management-and-deployment
 **Category:** MLOps & GenAIOps
+**Status:** Established Practice
 
 **Last updated:** 2026-04
 

skills/ms-ai-engineering/references/mlops-genaiops/monitoring-observability-ml-systems.md

@@ -6,6 +6,7 @@
 **Konfidensgrad:** ⭐⭐⭐⭐⭐ (Verifisert mot offisiell Microsoft-dokumentasjon)
 **Type:** reference
 **Source:** https://learn.microsoft.com/azure/machine-learning/concept-model-monitoring
+**Status:** Established Practice
 
 ---
 

skills/ms-ai-engineering/references/mlops-genaiops/prompt-flow-production-deployment.md

@@ -6,6 +6,7 @@
 **Confidence:** 🟢 Høy (basert på offisiell Microsoft-dokumentasjon fra Microsoft Foundry og Azure Machine Learning)
 **Type:** reference
 **Source:** https://learn.microsoft.com/azure/machine-learning/prompt-flow/migrate-prompt-flow-to-agent-framework
+**Status:** Established Practice
 
 ---
 

skills/ms-ai-engineering/references/mlops-genaiops/responsible-ai-mlops-integration.md

@@ -5,6 +5,7 @@
 **Confidence:** 95% (basert på offisiell Microsoft-dokumentasjon og Azure Machine Learning-referanser)
 **Type:** reference
 **Source:** https://learn.microsoft.com/azure/machine-learning/concept-responsible-ai-dashboard
+**Status:** Established Practice
 
 ---
 

skills/ms-ai-governance/references/monitoring-observability/anomaly-detection-ai-systems.md

@@ -5,6 +5,7 @@
 **Målgruppe:** AI-arkitekter, DevOps-team, MLOps-ingeniører
 **Type:** reference
 **Source:** https://learn.microsoft.com/azure/ai-services/anomaly-detector/overview
+**Status:** Established Practice
 
 ## Innhold
 

skills/ms-ai-governance/references/monitoring-observability/azure-monitor-setup-ai-workloads.md

@@ -4,6 +4,7 @@
 **Last updated:** 2026-02-05
 **Gjelder for:** Azure OpenAI, Azure AI Services, Azure AI Search, Microsoft Foundry
 **Type:** reference
+**Status:** Established Practice
 
 ---
 

skills/ms-ai-governance/references/monitoring-observability/log-analytics-kql-ai-queries.md

@@ -5,6 +5,7 @@
 **Forfatter:** Cosmo Skyberg, AI Solution Architect
 **Type:** reference
 **Source:** https://learn.microsoft.com/azure/machine-learning/monitor-azure-machine-learning
+**Status:** Established Practice
 
 ## Innhold
 

skills/ms-ai-governance/references/monitoring-observability/token-usage-tracking-attribution.md

@@ -5,6 +5,7 @@
 **Versjon:** 1.0
 **Type:** reference
 **Source:** https://learn.microsoft.com/azure/foundry/concepts/manage-costs
+**Status:** Established Practice
 
 ## Innhold
 

skills/ms-ai-governance/references/responsible-ai/ai-act-classification-methodology.md

@@ -2,9 +2,8 @@
 **Type:** regulatory
 **Category:** Responsible AI & Governance
 
-Last updated: 2026-06-18
-Status: GA
-Category: Responsible AI & Governance
+**Last updated:** 2026-06-18
+**Status:** GA
 
 ---
 

skills/ms-ai-governance/references/responsible-ai/ai-act-deployer-obligations.md

@@ -2,9 +2,8 @@
 **Type:** regulatory
 **Category:** Responsible AI & Governance
 
-Last updated: 2026-06-18
-Status: GA
-Category: Responsible AI & Governance
+**Last updated:** 2026-06-18
+**Status:** GA
 
 ---
 

skills/ms-ai-governance/references/responsible-ai/ai-act-fria-template.md

@@ -2,9 +2,8 @@
 **Type:** template
 **Category:** Responsible AI & Governance
 
-Last updated: 2026-02
-Status: GA
-Category: Responsible AI & Governance
+**Last updated:** 2026-02
+**Status:** GA
 
 ---
 

skills/ms-ai-governance/references/responsible-ai/ai-act-provider-obligations.md

@@ -2,9 +2,8 @@
 **Type:** regulatory
 **Category:** Responsible AI & Governance
 
-Last updated: 2026-06-18
-Status: GA
-Category: Responsible AI & Governance
+**Last updated:** 2026-06-18
+**Status:** GA
 
 ---
 

skills/ms-ai-governance/references/responsible-ai/ai-center-of-excellence-setup.md

@@ -5,6 +5,7 @@
 **Confidence:** HIGH (basert på Microsoft Cloud Adoption Framework og offisiell dokumentasjon)
 **Type:** reference
 **Source:** https://learn.microsoft.com/azure/cloud-adoption-framework/scenarios/ai/govern
+**Status:** Established Practice
 
 ## Innhold
 

skills/ms-ai-governance/references/responsible-ai/ai-governance-structure-framework.md

@@ -5,6 +5,7 @@
 **Målgruppe:** Tekniske beslutningstakere, AI-arkitekter, governance-team
 **Oppdateringsfrekvens:** Kvartalsvis (Q1 2026)
 **Type:** methodology
+**Status:** Established Practice
 
 ---
 

skills/ms-ai-governance/references/responsible-ai/red-teaming-ai-models.md

@@ -6,6 +6,7 @@
 **Konfidensgrad:** ⚠️ HIGH — Basert på offisiell Microsoft-dokumentasjon (jun 2026)
 **Type:** reference
 **Source:** https://learn.microsoft.com/azure/ai-services/content-safety/concepts/jailbreak-detection
+**Status:** Established Practice
 
 ## Innhold
 

skills/ms-ai-security/references/ai-security-engineering/ai-red-team-operations-practical.md

@@ -5,6 +5,7 @@
 **Relatert:** ai-prompt-injection-defense.md, ai-jailbreak-prevention.md
 **Source:** https://learn.microsoft.com/security/ai-red-team/training
 **Type:** reference
+**Status:** Established Practice
 
 ---
 

skills/ms-ai-security/references/ai-security-engineering/data-leakage-prevention-ai.md

@@ -5,6 +5,7 @@
 **Målgruppe:** Enterprise AI architects og security teams
 **Type:** reference
 **Source:** https://learn.microsoft.com/security/benchmark/azure/mcsb-v2-artificial-intelligence-security
+**Status:** Established Practice
 
 ## Innhold
 

skills/ms-ai-security/references/ai-security-engineering/secure-model-deployment-hardening.md

@@ -4,6 +4,7 @@
 **Last updated:** 2026-02-05
 **Målgruppe:** Arkitekter som skal sikre AI-modeller i produksjonsmiljøer
 **Type:** reference
+**Status:** Established Practice
 
 ## Innhold
 

skills/ms-ai-security/references/ai-security-engineering/security-copilot-integration.md

@@ -5,6 +5,7 @@
 **Målgruppe:** Sikkerhetsarkitekter og SOC-ledere som vurderer AI-assistert sikkerhetsoperasjon
 **Type:** reference
 **Source:** https://learn.microsoft.com/copilot/security/agents-overview
+**Status:** Established Practice
 
 ## Innhold
 

skills/ms-ai-security/references/ai-security-engineering/supply-chain-security-ai-models.md

@@ -5,6 +5,7 @@
 **Relatert plattform:** Microsoft Foundry, Azure Machine Learning, Azure DevOps, Microsoft Defender for Cloud
 **Type:** reference
 **Source:** https://learn.microsoft.com/azure/machine-learning/concept-vulnerability-management
+**Status:** Established Practice
 
 ---