feat(claude-flow): Init claude-flow v3, pretrain on repo, update CLAUDE.md

- Run npx @claude-flow/cli@latest init --force: 115 files created
  (agents, commands, helpers, skills, settings, MCP config)
- Initialize memory.db (147 KB): 84 files analyzed, 30 patterns
  extracted, 46 trajectories evaluated via 4-step RETRIEVE/JUDGE/DISTILL/CONSOLIDATE
- Run pretraining with MoE model: hyperbolic Poincaré embeddings,
  3 contradictions resolved, all-MiniLM-L6-v2 ONNX embedding index
- Include .claude/memory.db and .claude-flow/metrics/learning.json in
  repo for team sharing (semantic search available to all contributors)
- Update CLAUDE.md: add wifi-densepose project context, key crates,
  ruvector integration map, correct build/test commands for this repo,
  ADR cross-reference (ADR-014 through ADR-017)

https://claude.ai/code/session_01BSBAQJ34SLkiJy4A8SoiL4

.claude/agents/core/planner.md

@@ -9,7 +9,7 @@ capabilities:
   - resource_allocation
   - timeline_estimation
   - risk_assessment
-  # NEW v2.0.0-alpha capabilities
+  # NEW v3.0.0-alpha.1 capabilities
   - self_learning         # Learn from planning outcomes
   - context_enhancement   # GNN-enhanced dependency mapping
   - fast_processing       # Flash Attention planning
@@ -366,7 +366,7 @@ console.log(`Common planning gaps: ${stats.commonCritiques}`);
    - Efficient resource utilization (MoE expert selection)
    - Continuous progress visibility
 
-4. **New v2.0.0-alpha Practices**:
+4. **New v3.0.0-alpha.1 Practices**:
    - Learn from past plans (ReasoningBank)
    - Use GNN for dependency mapping (+12.4% accuracy)
    - Route tasks with MoE attention (optimal agent selection)