baa2d0220b
Add /ultraresearch-local for structured research combining local codebase analysis with external knowledge via parallel agent swarms. Produces research briefs with triangulation, confidence ratings, and source quality assessment. New command: /ultraresearch-local with modes --quick, --local, --external, --fg. New agents: research-orchestrator (opus), docs-researcher, community-researcher, security-researcher, contrarian-researcher, gemini-bridge (all sonnet). New template: research-brief-template.md. Integration: --research flag in /ultraplan-local accepts pre-built research briefs (up to 3), enriches the interview and exploration phases. Planning orchestrator cross-references brief findings during synthesis. Design principle: Context Engineering — right information to right agent at right time. Research briefs are structured artifacts in the pipeline: ultraresearch → brief → ultraplan --research → plan → ultraexecute. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
406 lines
16 KiB
JSON
406 lines
16 KiB
JSON
{
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"version": "1.0",
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"created": "2026-02-03",
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"target_dir": "skills/ms-ai-engineering/references",
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"total_estimated_skills": "300-350",
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"waves": [
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{
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"wave": 1,
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"priority": "HIGH",
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"description": "Kritisk manglende kunnskap for enterprise AI-arkitektur",
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"categories": [
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"azure-ai-services",
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"rag-architecture",
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"responsible-ai",
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"copilot-extensibility",
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"prompt-engineering",
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"cost-optimization",
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"mlops-genaiops"
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]
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},
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{
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"wave": 1.5,
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"priority": "HIGH",
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"description": "Utredningsstøtte: norsk offentlig sektor, AI-sikkerhet og observerbarhet",
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"categories": [
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"norwegian-public-sector-governance",
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"ai-security-engineering",
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"monitoring-observability"
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]
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},
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{
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"wave": 2,
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"priority": "MEDIUM",
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"description": "Verdifulle tillegg for komplett arkitekturdekning",
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"categories": [
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"agent-orchestration",
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"bcdr",
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"data-engineering",
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"api-management",
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"hybrid-edge",
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"multi-modal",
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"performance-scalability"
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]
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}
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],
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"categories": {
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"azure-ai-services": {
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"name": "Azure AI Services (Foundry Tools)",
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"dir": "azure-ai-services",
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"priority": "HIGH",
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"description": "Pre-bygde AI-tjenester: Vision, Speech, Language, Document Intelligence, Translator, Content Understanding. Fundamentale byggeblokker for enterprise AI.",
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"estimated_skills": 20,
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"examples": [
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"azure-ai-vision-overview",
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"document-intelligence-models",
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"speech-services-architecture",
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"language-services-text-analytics",
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"content-understanding-multimodal",
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"translator-custom-models",
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"azure-ai-search-indexing",
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"custom-vision-vs-florence",
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"ai-services-networking-security",
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"ai-services-pricing-optimization"
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],
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"existing_overlap": ["platforms/azure-ai-foundry.md"],
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"notes": "Foundry Tools er ny branding (2025). Unngå duplikering med azure-ai-foundry.md som dekker overordnet plattform."
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},
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"rag-architecture": {
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"name": "RAG Architecture & Semantic Search",
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"dir": "rag-architecture",
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"priority": "HIGH",
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"description": "Retrieval-Augmented Generation med Azure AI Search. Vektorindeksering, embedding, hybrid search, reranking, chunking, citation tracking.",
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"estimated_skills": 22,
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"examples": [
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"rag-architecture-patterns",
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"azure-ai-search-vector-indexing",
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"embedding-model-selection",
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"chunking-strategies",
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"hybrid-search-configuration",
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"semantic-ranker-optimization",
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"rag-evaluation-metrics",
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"multi-index-federation",
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"rag-security-rbac",
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"graphrag-knowledge-graphs"
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],
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"existing_overlap": ["architecture/decision-trees.md"],
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"notes": "RAG er det vanligste mønsteret for enterprise AI. Detaljer er planlagt som ms-rag-architect plugin men grunnleggende arkitektur dekkes her."
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},
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"responsible-ai": {
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"name": "Responsible AI & Governance",
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"dir": "responsible-ai",
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"priority": "HIGH",
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"description": "Microsofts Responsible AI-rammeverk, AI-etikk, bias-deteksjon, forklarbarhet, GDPR/AI Act compliance, AI governance for offentlig sektor.",
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"estimated_skills": 22,
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"examples": [
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"responsible-ai-framework-overview",
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"ai-act-compliance-guide",
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"bias-detection-mitigation",
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"model-explainability-techniques",
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"ai-governance-structure",
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"ai-center-of-excellence",
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"red-teaming-ai-models",
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"content-safety-implementation",
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"ai-impact-assessment",
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"transparency-documentation"
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],
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"existing_overlap": ["architecture/security.md", "architecture/public-sector-checklist.md"],
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"notes": "Utfyller security.md (teknisk sikkerhet) med governance og compliance. Spesielt viktig for offentlig sektor etter AI Act."
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},
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"copilot-extensibility": {
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"name": "Copilot Extensibility & Integration",
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"dir": "copilot-extensibility",
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"priority": "HIGH",
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"description": "Utvidelse av M365 Copilot og Copilot Studio: declarative agents, custom engine agents, plugins, connectors, Graph API, MCP.",
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"estimated_skills": 22,
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"examples": [
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"declarative-agents-overview",
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"custom-engine-agents",
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"copilot-studio-topics-entities",
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"graph-api-for-copilot",
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"copilot-connectors-patterns",
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"mcp-integration-copilot-studio",
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"copilot-analytics-usage",
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"teams-copilot-extensions",
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"sharepoint-agents",
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"copilot-studio-dlp-governance"
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],
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"existing_overlap": ["platforms/copilot-studio.md", "platforms/m365-copilot.md"],
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"notes": "Går dypere enn eksisterende plattformfiler. Fokus på implementeringsmønstre, ikke overordnet arkitektur."
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},
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"prompt-engineering": {
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"name": "Prompt Engineering & LLM Optimization",
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"dir": "prompt-engineering",
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"priority": "HIGH",
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"description": "System message design, few-shot/zero-shot teknikker, chain-of-thought, reasoning-modeller (O1/O3), grounding, output-formatering.",
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"estimated_skills": 18,
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"examples": [
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"system-message-design-patterns",
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"few-shot-learning-techniques",
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"chain-of-thought-prompting",
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"reasoning-models-o1-o3",
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"structured-output-json-mode",
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"function-calling-patterns",
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"grounding-with-search",
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"temperature-and-sampling",
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"token-optimization-techniques",
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"prompt-testing-evaluation"
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],
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"existing_overlap": [],
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"notes": "Helt nytt område. Direkte påvirkning på kvaliteten av alle AI-løsninger."
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},
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"cost-optimization": {
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"name": "Cost Optimization & FinOps for AI",
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"dir": "cost-optimization",
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"priority": "HIGH",
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"description": "Token-optimalisering, caching, reserved capacity, modellvalg, Azure Cost Management, chargeback, budsjettplanlegging for AI.",
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"estimated_skills": 20,
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"examples": [
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"token-cost-optimization",
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"semantic-caching-patterns",
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"reserved-capacity-planning",
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"model-selection-price-performance",
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"azure-cost-management-ai",
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"ptu-vs-paygo-decision",
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"ai-builder-credits-transition",
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"cost-allocation-chargeback",
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"budget-forecasting-ai",
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"small-language-models-cost"
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],
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"existing_overlap": ["architecture/cost-models.md"],
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"notes": "Utfyller cost-models.md med dypere strategier. cost-models.md dekker prislister, dette dekker optimaliseringsteknikker."
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},
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"mlops-genaiops": {
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"name": "MLOps & GenAIOps",
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"dir": "mlops-genaiops",
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"priority": "HIGH",
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"description": "CI/CD for AI, modellmonitorering, versjonshåndtering, A/B-testing, retraining, evaluering, Azure ML pipelines for produksjon.",
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"estimated_skills": 22,
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"examples": [
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"genaiops-overview",
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"azure-ml-pipelines",
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"model-versioning-registry",
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"llm-evaluation-framework",
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"ab-testing-ai-models",
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"data-drift-monitoring",
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"automated-retraining",
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"ci-cd-ai-models",
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"prompt-flow-production",
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"model-deployment-strategies"
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],
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"existing_overlap": [],
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"notes": "Helt nytt område. Kritisk for å gå fra prototyp til produksjon."
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},
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"data-engineering": {
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"name": "Data Engineering for AI",
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"dir": "data-engineering",
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"priority": "MEDIUM",
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"description": "Dataintegrasjon med Microsoft Fabric, Data Factory, OneLake, Databricks. Zero-ETL, lakehouse-arkitektur, AI-drevet dataintegrering.",
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"estimated_skills": 22,
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"examples": [
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"microsoft-fabric-for-ai",
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"onelake-data-strategy",
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"data-factory-ai-pipelines",
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"zero-etl-patterns",
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"data-quality-for-ai",
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"real-time-streaming-ai",
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"dataverse-ai-integration",
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"data-lakehouse-architecture",
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"data-governance-purview",
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"synthetic-data-generation"
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],
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"existing_overlap": [],
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"notes": "Datakvalitet er #1 årsak til AI-prosjektfeil. Microsoft Fabric er raskt voksende."
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},
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"api-management": {
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"name": "API Management & AI Gateway",
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"dir": "api-management",
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"priority": "MEDIUM",
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"description": "Azure API Management som AI-gateway: rate limiting, token quota, load balancing, circuit breaker, autentisering, multi-region.",
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"estimated_skills": 18,
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"examples": [
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"apim-ai-gateway-overview",
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"token-rate-limiting",
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"load-balancing-openai",
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"circuit-breaker-patterns",
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"multi-region-gateway",
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"apim-authentication-patterns",
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"backend-pool-management",
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"streaming-support-apim",
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"cost-tracking-apim",
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"apim-vs-direct-access"
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],
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"existing_overlap": [],
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"notes": "Viktig for enterprise-skalering. APIM AI Gateway er relativt nytt (2024-2025)."
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},
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"hybrid-edge": {
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"name": "Hybrid Cloud & Edge AI",
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"dir": "hybrid-edge",
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"priority": "MEDIUM",
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"description": "Azure Arc, Azure Local, IoT Operations, edge AI inferencing, disconnected scenarios, datasuverenitet for offentlig sektor.",
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"estimated_skills": 18,
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"examples": [
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"azure-arc-ai-management",
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"azure-local-ai-workloads",
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"edge-ai-inferencing",
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"disconnected-ai-scenarios",
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"data-sovereignty-patterns",
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"iot-operations-ai",
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"hybrid-rag-architecture",
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"on-premises-llm-deployment",
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"azure-confidential-computing",
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"sovereign-cloud-norway"
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],
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"existing_overlap": [],
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"notes": "Spesielt relevant for norsk offentlig sektor med suverenitetskrav og sikkerhetsgradert informasjon."
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},
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"bcdr": {
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"name": "Business Continuity & Disaster Recovery",
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"dir": "bcdr",
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"priority": "MEDIUM",
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"description": "HA, DR og BCDR for AI: multi-region, backup, failover, RTO/RPO for Azure OpenAI og AI Foundry.",
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"estimated_skills": 16,
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"examples": [
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"multi-region-azure-openai",
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"ai-foundry-dr-planning",
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"backup-recovery-strategies",
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"failover-testing-ai",
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"rto-rpo-ai-services",
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"data-replication-patterns",
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"geo-redundancy-search",
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"incident-response-ai",
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"capacity-planning-dr",
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"compliance-bcdr-requirements"
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],
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"existing_overlap": [],
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"notes": "Nødvendig for kritiske produksjonssystemer i offentlig sektor."
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},
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"multi-modal": {
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"name": "Multi-Modal AI",
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"dir": "multi-modal",
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"priority": "MEDIUM",
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"description": "Tekst + bilde + tale + video: GPT-4V/GPT-5 vision, Video Indexer, Speech-integrasjon, multi-modal RAG, aksessibilitet.",
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"estimated_skills": 18,
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"examples": [
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"gpt-vision-architecture",
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"video-indexer-ai",
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"multi-modal-rag",
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"speech-to-ai-pipelines",
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"image-generation-dall-e",
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"document-vision-processing",
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"accessibility-multi-modal",
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"real-time-audio-api",
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"video-analysis-patterns",
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"multi-modal-evaluation"
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],
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"existing_overlap": [],
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"notes": "Økende etterspørsel etter multi-modale løsninger. GPT-5 styrker vision-kapabiliteter."
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},
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"agent-orchestration": {
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"name": "Agent Orchestration & Automation",
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"dir": "agent-orchestration",
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"priority": "MEDIUM",
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"description": "Multi-agent systemer, orkesteringsmønstre, agent-kommunikasjon, Agent 365, Semantic Kernel/Agent Framework-mønstre.",
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"estimated_skills": 20,
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"examples": [
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"multi-agent-patterns",
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"agent-orchestration-topologies",
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"agent-to-agent-communication",
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"agent-365-governance",
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"semantic-kernel-agents",
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"agent-memory-patterns",
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"tool-use-patterns",
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"agent-evaluation-testing",
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"human-in-the-loop-agents",
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"autonomous-workflow-patterns"
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],
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"existing_overlap": ["development/agent-framework.md"],
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"notes": "Utfyller agent-framework.md med orkestrerings- og designmønstre."
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},
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"performance-scalability": {
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"name": "Performance & Scalability",
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"dir": "performance-scalability",
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"priority": "MEDIUM",
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"description": "Latency-reduksjon, throughput, caching, batching, streaming, auto-scaling, CDN for AI-workloads.",
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"estimated_skills": 18,
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"examples": [
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"latency-optimization-openai",
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"streaming-responses-patterns",
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"batch-api-usage",
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"auto-scaling-ai-infra",
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"cdn-edge-caching-ai",
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"connection-pooling-patterns",
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"throughput-optimization",
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"model-distillation-perf",
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"async-processing-patterns",
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"load-testing-ai-services"
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],
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"existing_overlap": [],
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"notes": "Viktig for brukeropplevelse. Komplementerer cost-optimization."
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},
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"monitoring-observability": {
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"name": "Monitoring & Observability",
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"dir": "monitoring-observability",
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"priority": "HIGH",
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"description": "Azure Monitor, Application Insights, Log Analytics for AI. Token tracking, anomaly detection, dashboards, alerting.",
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"estimated_skills": 18,
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"examples": [
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"azure-monitor-ai-workloads",
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"application-insights-llm",
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"token-usage-tracking",
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"anomaly-detection-ai",
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"custom-ai-dashboards",
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"alerting-strategies-ai",
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"distributed-tracing-ai",
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"log-analytics-ai-queries",
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"sla-monitoring-ai",
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"cost-attribution-monitoring"
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],
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"existing_overlap": [],
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"notes": "Nødvendig for produksjonsoperasjoner. Komplementerer MLOps."
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},
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"norwegian-public-sector-governance": {
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"name": "Norwegian Public Sector AI Governance",
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"dir": "norwegian-public-sector-governance",
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"priority": "HIGH",
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"description": "Norsk lovverk, Digdir-rammeverk og forvaltningsmetodikk anvendt på AI. Utredningsinstruksen, Digdirs 7 arkitekturprinsipper, rammeverk for digital samhandling (EIF), DPIA, ROS-analyse, NSM grunnprinsipper, anskaffelser og gevinstrealisering for AI i offentlig sektor.",
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"estimated_skills": 20,
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"research_sources": ["websearch", "regjeringen.no", "lovdata.no", "digdir.no", "nsm.no", "datatilsynet.no"],
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"examples": [
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"utredningsinstruksen-methodology",
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"forvaltningsloven-ai-decisions",
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"digdir-principle-1-user-centric",
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"digdir-principle-4-trust",
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"digital-samhandling-5-layers",
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"dpia-norwegian-methodology",
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"ros-analyse-ai-systems",
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"nsm-grunnprinsipper-ai-mapping",
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"anskaffelser-ai-procurement",
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"gevinstrealisering-ai-projects"
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],
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"existing_overlap": ["architecture/public-sector-checklist.md", "architecture/ai-utredning-template.md"],
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"notes": "Fundamentalt annerledes enn øvrige kategorier: primærkilder er regjeringen.no, lovdata.no, digdir.no, nsm.no — IKKE microsoft-learn. Innhold er regulatorisk/juridisk, ikke teknisk produktdokumentasjon. Prompt-template må bruke WebSearch for norske kilder i tillegg til microsoft-learn MCP."
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},
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"ai-security-engineering": {
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"name": "AI Security Engineering",
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"dir": "ai-security-engineering",
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"priority": "HIGH",
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"description": "Operasjonell AI-sikkerhet: prompt injection forsvar, jailbreak-prevention, content safety kalibrering, PII-deteksjon, trusselmodellering, sikkerhetsscoring, hendelseshåndtering, output-validering, zero trust for AI, datalekkasjeforebygging og red teaming.",
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"estimated_skills": 15,
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"examples": [
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"prompt-injection-defense-patterns",
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"jailbreak-prevention-production",
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"content-safety-filter-calibration",
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"pii-detection-norwegian-text",
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"ai-threat-modeling-stride",
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"security-scoring-rubric-6dimensions",
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"ai-incident-response-procedures",
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"output-validation-grounding-verification",
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"zero-trust-ai-services",
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"ai-red-team-operations-practical"
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],
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"existing_overlap": ["architecture/security.md", "responsible-ai/red-teaming-ai-models.md", "responsible-ai/content-safety-implementation.md", "prompt-engineering/adversarial-prompting-and-jailbreaks.md"],
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"notes": "Komplementerer responsible-ai (governance/teori) og prompt-engineering (angrepsteknikker) med OPERASJONELLE forsvarsmønstre. Fokus: forsvar, deteksjon, respons — ikke policy eller angrep."
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}
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}
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}
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