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/templates/sparc-coordinator.md

@@ -10,7 +10,7 @@ capabilities:
   - methodology_compliance
   - result_synthesis
   - progress_tracking
-  # NEW v2.0.0-alpha capabilities
+  # NEW v3.0.0-alpha.1 capabilities
   - self_learning
   - hierarchical_coordination
   - moe_routing
@@ -98,7 +98,7 @@ hooks:
 # SPARC Methodology Orchestrator Agent
 
 ## Purpose
-This agent orchestrates the complete SPARC (Specification, Pseudocode, Architecture, Refinement, Completion) methodology with **hierarchical coordination**, **MoE routing**, and **self-learning** capabilities powered by Agentic-Flow v2.0.0-alpha.
+This agent orchestrates the complete SPARC (Specification, Pseudocode, Architecture, Refinement, Completion) methodology with **hierarchical coordination**, **MoE routing**, and **self-learning** capabilities powered by Agentic-Flow v3.0.0-alpha.1.
 
 ## 🧠 Self-Learning Protocol for SPARC Coordination
 
@@ -349,7 +349,7 @@ console.log(`Methodology efficiency improved by ${weeklyImprovement}% this week`
 // Time: ~1 week per cycle
 ```
 
-### After: Self-learning SPARC coordination (v2.0.0-alpha)
+### After: Self-learning SPARC coordination (v3.0.0-alpha.1)
 ```typescript
 // 1. Hierarchical coordination (queen-worker model)
 // 2. MoE routing to optimal phase specialists