feat: implement KM-RAG methodology artifacts and core architectural standards with supporting query and service updates

.agent/skills/km-rag-methodology/SKILL.md

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+---
+name: km-rag-methodology
+description: Expertise in implementing Knowledge-Map RAG (KM-RAG), focusing on structured Knowledge Units, Graph relationships, and multi-stage retrieval in .NET.
+tags: [RAG, KnowledgeMap, GraphRAG, AI, .NET, CleanArchitecture]
+version: 1.0.0
+---
+
+# KM-RAG Methodology
+
+This skill provides a comprehensive framework for transitioning from basic chunk-based RAG to a structured **Knowledge-Map RAG (KM-RAG)** approach.
+
+## Core Concepts
+- **Knowledge Units (KU)**: Granular pieces of information with stable IDs and types (Section, Table, Definition, Rule).
+- **Knowledge Map (Graph)**: Explicit links between units (`Next`, `Defines`, `Contains`) enabling contextual expansion.
+- **Multi-Stage Retrieval**: A pipeline starting with semantic candidate generation followed by graph expansion and optional reranking.
+- **Provenance & Governance**: Full traceability of AI answers back to their source units.
+
+## Key Artifacts
+- [Core Concepts](artifacts/core_concepts.md): Deep dive into the methodology.
+- [Implementation Patterns (.NET)](artifacts/implementation_patterns.md): C# code for units, links, and retrieval.
+- [Quality Checklist](artifacts/evaluation_checklist.md): Metrics and safety procedures.
+- [Deep Research Report](artifacts/deep-research-report-rag.md): Original research on the KM-RAG approach.
+
+## Usage
+Use this skill when:
+- Designing or refactoring RAG systems for high precision.
+- Implementing multi-tenant knowledge bases.
+- Enhancing AI answers with structural context from a graph.
+- Building evaluation pipelines for hallucination detection.