docs(01): capture phase context

Phase 01: GNN Verifier Foundation
- Implementation decisions documented
- Phase boundary established

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

Author: vmehtacode

.planning/phases/01-gnn-verifier-foundation/01-CONTEXT.md

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+# Phase 1: GNN Verifier Foundation - Context
+
+**Gathered:** 2026-01-27
+**Status:** Ready for planning
+
+<domain>
+## Phase Boundary
+
+Build a topology-aware GNN verifier that transforms SSEN grid metadata into per-node anomaly scores. This phase delivers the graph construction pipeline and GNN model architecture. Training loops, hybrid verifier integration, and evaluation frameworks are separate phases.
+
+</domain>
+
+<decisions>
+## Implementation Decisions
+
+### Graph Construction
+- Three-level node hierarchy: Substations → Feeders → Households (matches physical grid structure)
+- Edges represent binary connectivity only — no edge weights, capacity, or distance attributes
+- Nodes with missing or incomplete SSEN metadata are excluded from the graph (cleaner training signal)
+- Temporal features attached as fixed time-window snapshots per node (e.g., [current_load, avg_24h, peak_7d])
+
+### GNN Architecture
+- Primary layer type: GAT (Graph Attention Network) with learnable attention weights
+- Network depth: 3 layers — captures substation→feeder→household in single forward pass
+- Temporal processing: Encode time patterns per-node first, then GNN aggregates across spatial structure
+- Embedding dimension: 64-dimensional hidden states (lightweight for faster inference)
+
+### Claude's Discretion
+- Anomaly score normalization and threshold handling
+- Oversmoothing prevention techniques (skip connections, layer norm)
+- Number of attention heads in GAT layers
+- Specific temporal encoder architecture (LSTM vs 1D-Conv vs simple MLP)
+- Exact node feature engineering from SSEN metadata
+- Batch size and learning rate tuning
+
+</decisions>
+
+<specifics>
+## Specific Ideas
+
+No specific requirements — open to standard approaches that meet the success criteria (>85% accuracy on synthetic anomalies, <30ms inference latency).
+
+</specifics>
+
+<deferred>
+## Deferred Ideas
+
+None — discussion stayed within phase scope
+
+</deferred>
+
+---
+
+*Phase: 01-gnn-verifier-foundation*
+*Context gathered: 2026-01-27*