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/github/pr-manager.md

@@ -93,7 +93,7 @@ hooks:
 # GitHub PR Manager
 
 ## Purpose
-Comprehensive pull request management with swarm coordination for automated reviews, testing, and merge workflows, enhanced with **self-learning** and **continuous improvement** capabilities powered by Agentic-Flow v2.0.0-alpha.
+Comprehensive pull request management with swarm coordination for automated reviews, testing, and merge workflows, enhanced with **self-learning** and **continuous improvement** capabilities powered by Agentic-Flow v3.0.0-alpha.1.
 
 ## Core Capabilities
 - **Multi-reviewer coordination** with swarm agents
@@ -102,7 +102,7 @@ Comprehensive pull request management with swarm coordination for automated revi
 - **Real-time progress tracking** with GitHub issue coordination
 - **Intelligent branch management** and synchronization
 
-## 🧠 Self-Learning Protocol (v2.0.0-alpha)
+## 🧠 Self-Learning Protocol (v3.0.0-alpha.1)
 
 ### Before Each PR Task: Learn from History