diff --git a/skills/ms-ai-advisor/SKILL.md b/skills/ms-ai-advisor/SKILL.md index 09981ab..4cb6232 100644 --- a/skills/ms-ai-advisor/SKILL.md +++ b/skills/ms-ai-advisor/SKILL.md @@ -200,35 +200,40 @@ Du har tilgang til følgende MCP-servere: Du har tilgang til forhåndsresearchede kunnskapsbaser i `references/`-mappen: **Plattformer (`references/platforms/`):** -- `azure-ai-foundry.md` - Azure AI Foundry vs Copilot Studio vs Azure OpenAI -- `m365-copilot.md` - Microsoft 365 Copilot: kapasiteter, lisensiering, extensibility -- `copilot-studio.md` - Copilot Studio: agenttyper, MCP-støtte, autonome agenter -- `power-platform.md` - Power Automate, Power Apps, AI Builder +- `references/platforms/azure-ai-foundry.md` - Azure AI Foundry vs Copilot Studio vs Azure OpenAI +- `references/platforms/m365-copilot.md` - Microsoft 365 Copilot: kapasiteter, lisensiering, extensibility +- `references/platforms/copilot-studio.md` - Copilot Studio: agenttyper, MCP-støtte, autonome agenter +- `references/platforms/power-platform.md` - Power Automate, Power Apps, AI Builder +- `references/platforms/model-catalog-2026.md` - Modellkatalog: kapasiteter, kontekstvindu, prising **Arkitektur (`references/architecture/`):** -- `decision-trees.md` - Beslutningstrær for plattformvalg, agenttyper, RAG, sikkerhet -- `security.md` - Content Safety, Purview, Defender, identity, compliance -- `ai-utredning-template.md` - Utredningsmal for offentlig sektor -- `cost-models.md` - Kostnadsmodeller per plattform -- `licensing-matrix.md` - Lisensmatrise for Microsoft AI -- `poc-template.md` - POC-rammeverk -- `migration-patterns.md` - Migrasjonsmønstre mellom plattformer -- `public-sector-checklist.md` - Sjekkliste for offentlig sektor -- `adr-template.md` - ADR-mal (MADR v3.0) -- `diagram-prompt-templates.md` - Diagramprompts for Imagen 3 -- `recommended-mcp-servers.md` - Anbefalte MCP-servere -- `alternativanalyse-methodology.md` - Vektet multi-kriterie-analyse (brukt av `/architect:compare --weighted`) -- `capacity-feasibility-benchmarks.md` - Kompetanse-gap + tidsplan-benchmarks (brukt av `/architect:cost --capacity`) +- `references/architecture/decision-trees.md` - Beslutningstrær for plattformvalg, agenttyper, RAG, sikkerhet +- `references/architecture/security.md` - Content Safety, Purview, Defender, identity, compliance +- `references/architecture/ai-utredning-template.md` - Utredningsmal for offentlig sektor +- `references/architecture/cost-models.md` - Kostnadsmodeller per plattform +- `references/architecture/licensing-matrix.md` - Lisensmatrise for Microsoft AI +- `references/architecture/poc-template.md` - POC-rammeverk +- `references/architecture/migration-patterns.md` - Migrasjonsmønstre mellom plattformer +- `references/architecture/public-sector-checklist.md` - Sjekkliste for offentlig sektor +- `references/architecture/adr-template.md` - ADR-mal (MADR v3.0) +- `references/architecture/diagram-prompt-templates.md` - Diagramprompts for Imagen 3 +- `references/architecture/recommended-mcp-servers.md` - Anbefalte MCP-servere +- `references/architecture/alternativanalyse-methodology.md` - Vektet multi-kriterie-analyse (brukt av `/architect:compare --weighted`) +- `references/architecture/capacity-feasibility-benchmarks.md` - Kompetanse-gap + tidsplan-benchmarks (brukt av `/architect:cost --capacity`) - (+ øvrige filer i architecture/) **Utvikling (`references/development/`):** -- `agent-framework.md` - Microsoft Agent Framework +- `references/development/agent-framework.md` - Microsoft Agent Framework -**Copilot-utvidbarhet (`references/copilot-extensibility/`):** -- Declarative agents, custom engine, plugins, connectors (22 filer) +**Copilot-utvidbarhet (`references/copilot-extensibility/`, 22 filer — kjernefiler:):** +- `references/copilot-extensibility/declarative-agents-fundamentals.md` - Declarative agents (grunnlag) +- `references/copilot-extensibility/copilot-connectors-design-patterns.md` - Copilot connectors (designmønstre) +- `references/copilot-extensibility/custom-engine-agents-development.md` - Custom engine agents -**Prompt Engineering (`references/prompt-engineering/`):** -- System messages, few-shot, CoT, reasoning, grounding (18 filer) +**Prompt Engineering (`references/prompt-engineering/`, 18 filer — kjernefiler:):** +- `references/prompt-engineering/system-message-design-patterns.md` - System message-design +- `references/prompt-engineering/chain-of-thought-prompting.md` - Chain-of-thought-mønstre +- `references/prompt-engineering/prompt-testing-and-evaluation.md` - Testing og evaluering ### Kryss-referanser til andre skills diff --git a/skills/ms-ai-engineering/SKILL.md b/skills/ms-ai-engineering/SKILL.md index 4f22496..aa382b4 100644 --- a/skills/ms-ai-engineering/SKILL.md +++ b/skills/ms-ai-engineering/SKILL.md @@ -41,6 +41,8 @@ RAG er det mest brukte mønsteret for å gi LLM-er organisasjonsspesifikk kunnsk For detailed guidance, see `references/rag-architecture/` (28 files). +> **Kjernefiler:** `references/rag-architecture/rag-core-patterns.md`, `references/rag-architecture/agentic-rag-patterns.md`, `references/rag-architecture/azure-ai-search-setup.md`, `references/rag-architecture/chunking-strategies.md`, `references/rag-architecture/hybrid-search-configuration.md`, `references/rag-architecture/rag-evaluation-frameworks.md` + --- ## 2. Agent-orkestrering @@ -51,6 +53,8 @@ For offentlig sektor: Agenter krever AI Act-klassifisering, DPIA for persondata, For detailed guidance, see `references/agent-orchestration/` (24 files). +> **Kjernefiler:** `references/agent-orchestration/multi-agent-orchestration-patterns.md`, `references/agent-orchestration/semantic-kernel-agents-implementation.md`, `references/agent-orchestration/foundry-agent-service-ga.md`, `references/agent-orchestration/tool-use-and-function-calling-patterns.md`, `references/agent-orchestration/agent-to-agent-a2a-protocol.md` + --- ## 3. Azure AI Services @@ -61,6 +65,8 @@ Start med GPT-4o for prototyping, bruk spesialiserte tjenester for ytelse/kostna For detailed guidance, see `references/azure-ai-services/` (20 files). +> **Kjernefiler:** `references/azure-ai-services/ai-services-enterprise-architecture.md`, `references/azure-ai-services/document-intelligence-prebuilt-models.md`, `references/azure-ai-services/speech-services-speech-to-text.md`, `references/azure-ai-services/azure-ai-vision-image-analysis.md`, `references/azure-ai-services/language-services-text-analytics.md` + --- ## 4. Dataingeniør for AI @@ -71,6 +77,8 @@ For offentlig sektor: Anonymiser/pseudonymiser personopplysninger, bruk syntetis For detailed guidance, see `references/data-engineering/` (22 files). +> **Kjernefiler:** `references/data-engineering/fabric-lakehouse-architecture.md`, `references/data-engineering/onelake-data-strategy.md`, `references/data-engineering/data-factory-ai-pipelines.md`, `references/data-engineering/data-quality-ai-frameworks.md`, `references/data-engineering/microsoft-purview-governance.md` + --- ## 5. MLOps / GenAIOps @@ -87,6 +95,8 @@ MLOps og GenAIOps sikrer pålitelig bygging, deployment og drift av AI-løsninge For detailed guidance, see `references/mlops-genaiops/` (22 files). +> **Kjernefiler:** `references/mlops-genaiops/mlops-fundamentals-overview.md`, `references/mlops-genaiops/genaiops-llm-specific-practices.md`, `references/mlops-genaiops/ci-cd-for-ml-models.md`, `references/mlops-genaiops/model-versioning-registry-management.md`, `references/mlops-genaiops/llm-evaluation-production.md` + --- ## 6. Multimodal AI @@ -95,6 +105,8 @@ Multimodal AI kombinerer tekst, bilde, lyd og video. GPT-4o vision analyserer bi For detailed guidance, see `references/multi-modal/` (18 files). +> **Kjernefiler:** `references/multi-modal/gpt4o-vision-architecture.md`, `references/multi-modal/multimodal-rag-architecture.md`, `references/multi-modal/document-vision-processing.md`, `references/multi-modal/whisper-speech-recognition.md` + --- ## 7. API Management for AI @@ -105,6 +117,8 @@ Bruk semantisk caching for FAQ-lignende domener. Unngå for personaliserte svar For detailed guidance, see `references/api-management/` (19 files). +> **Kjernefiler:** `references/api-management/apim-ai-gateway-overview.md`, `references/api-management/genai-gateway-policies.md`, `references/api-management/token-rate-limiting-policies.md`, `references/api-management/load-balancing-openai-instances.md`, `references/api-management/semantic-caching-apim.md` + --- ## 8. Referansekatalog diff --git a/skills/ms-ai-security/SKILL.md b/skills/ms-ai-security/SKILL.md index eba56b9..e5119fc 100644 --- a/skills/ms-ai-security/SKILL.md +++ b/skills/ms-ai-security/SKILL.md @@ -87,7 +87,7 @@ Map each threat to the solution under assessment. Use the reference files for de | LLM09 | Misinformation | RAG grounding, Groundedness Detection, citation patterns, confidence scoring | `owasp-llm-top10-azure-mitigations.md` | | LLM10 | Unbounded Consumption | Rate limits, token budgets, PTU for capacity, Cost Management alerts | — | -All reference files are in `references/ai-security-engineering/`. LLM04/06/08/09 deler den konsoliderte filen `owasp-llm-top10-azure-mitigations.md`; LLM10 dekkes av rate-limit-/kostnadsfiler. +All reference files are in `references/ai-security-engineering/`. LLM04/06/08/09 deler den konsoliderte filen `references/ai-security-engineering/owasp-llm-top10-azure-mitigations.md`; LLM10 dekkes av rate-limit-/kostnadsfiler. Kjøretids-trusseldeteksjon for AI-endepunkter dekkes av `defender-threat-protection-ai-services.md` (Defender for Cloud AI threat protection — GA for AI applications, Preview for AI agents; merk: **ikke** tilgjengelig i Azure Government). @@ -155,11 +155,11 @@ Optimize latency, throughput, and scalability for AI workloads. Key strategies: - **Load testing:** Establish baseline, simulate peak traffic, identify breaking points, long-running soak tests For detailed implementation guidance, see specific files in `references/performance-scalability/`: -- `latency-optimization-azure-openai.md` — Latency tuning -- `auto-scaling-ai-infrastructure.md` — Scaling patterns -- `rate-limit-management.md` — TPM/RPM quota management -- `load-testing-ai-services.md` — Load testing methodology -- `gpu-compute-sizing.md` — GPU VM-sizing for selvhostet inferens (brukt av `/architect:cost --capacity`) +- `references/performance-scalability/latency-optimization-azure-openai.md` — Latency tuning +- `references/performance-scalability/auto-scaling-ai-infrastructure.md` — Scaling patterns +- `references/performance-scalability/rate-limit-management.md` — TPM/RPM quota management +- `references/performance-scalability/load-testing-ai-services.md` — Load testing methodology +- `references/performance-scalability/gpu-compute-sizing.md` — GPU VM-sizing for selvhostet inferens (brukt av `/architect:cost --capacity`) ---