chore(privacy): scrub real-org references from plugin internals (phase 2)
Same bulk replacement applied to plugin-internal KB, examples, fixtures, tests, and docs. Real organization names, persona names, internal system identifiers, and domain-specific terms replaced with fictional generic public-sector entity (DDT) and generic terminology. Scope: - okr/ — examples, governance, framework, integrations, sources - ms-ai-architect/ — KB references (engineering, governance, security, infrastructure, advisor), tests/fixtures, agents, docs - linkedin-thought-leadership/ — voice samples, network-builder, examples (genericized identifying headlines to "[your organization]") - llm-security/ — research notes, scan report Manual genericization beyond bulk replace: - okr SKILL.md "Primary user / Domain" — generic Norwegian public sector - linkedin-voice SKILL.md headline placeholder - network-builder.md headline placeholder - high-engagement-posts.md voice sample employer line + hashtag Phase 3 (factual-attribution review) remains: a few KB files attribute publicly known transport-sector docs/datasets (e.g. håndbok V440, NVDB) to the fictional DDT after bulk replace. Needs manual semantic review to either remove or restore correct citation without re-introducing affiliation references. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
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## Fremragende eksempler
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### Eksempel 1: Trafikksikkerhet (Statens vegvesen)
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### Eksempel 1: Trafikksikkerhet (Direktoratet for digital tjenesteutvikling)
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**Objective**: Redusere alvorlige ulykker på E6 gjennom Trøndelag
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### Eksempel 2: Digitale tjenester
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**Objective**: Levere digital-først opplevelse for førerkortsøknader
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**Objective**: Levere digital-først opplevelse for saksbehandlingsøknader
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**Key Results**:
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1. 90% av førerkortssøknader sendes inn digitalt (opp fra 65%)
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1. 90% av saksbehandlingssøknader sendes inn digitalt (opp fra 65%)
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2. Gjennomsnittlig behandlingstid reduseres fra 5 dager til 2 dager
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3. Brukertilfredshet øker fra 3.8 til 4.5 (skala 1-5)
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