feat: Complete Rust port of WiFi-DensePose with modular crates

Major changes:
- Organized Python v1 implementation into v1/ subdirectory
- Created Rust workspace with 9 modular crates:
  - wifi-densepose-core: Core types, traits, errors
  - wifi-densepose-signal: CSI processing, phase sanitization, FFT
  - wifi-densepose-nn: Neural network inference (ONNX/Candle/tch)
  - wifi-densepose-api: Axum-based REST/WebSocket API
  - wifi-densepose-db: SQLx database layer
  - wifi-densepose-config: Configuration management
  - wifi-densepose-hardware: Hardware abstraction
  - wifi-densepose-wasm: WebAssembly bindings
  - wifi-densepose-cli: Command-line interface

Documentation:
- ADR-001: Workspace structure
- ADR-002: Signal processing library selection
- ADR-003: Neural network inference strategy
- DDD domain model with bounded contexts

Testing:
- 69 tests passing across all crates
- Signal processing: 45 tests
- Neural networks: 21 tests
- Core: 3 doc tests

Performance targets:
- 10x faster CSI processing (~0.5ms vs ~5ms)
- 5x lower memory usage (~100MB vs ~500MB)
- WASM support for browser deployment

.claude/commands/analysis/token-efficiency.md

@@ -0,0 +1,45 @@
+# Token Usage Optimization
+
+## Purpose
+Reduce token consumption while maintaining quality through intelligent coordination.
+
+## Optimization Strategies
+
+### 1. Smart Caching
+- Search results cached for 5 minutes
+- File content cached during session
+- Pattern recognition reduces redundant searches
+
+### 2. Efficient Coordination
+- Agents share context automatically
+- Avoid duplicate file reads
+- Batch related operations
+
+### 3. Measurement & Tracking
+
+```bash
+# Check token savings after session
+Tool: mcp__claude-flow__token_usage
+Parameters: {"operation": "session", "timeframe": "24h"}
+
+# Result shows:
+{
+  "metrics": {
+    "tokensSaved": 15420,
+    "operations": 45,
+    "efficiency": "343 tokens/operation"
+  }
+}
+```
+
+## Best Practices
+1. **Use Task tool** for complex searches
+2. **Enable caching** in pre-search hooks
+3. **Batch operations** when possible
+4. **Review session summaries** for insights
+
+## Token Reduction Results
+- 📉 32.3% average token reduction
+- 🎯 More focused operations
+- 🔄 Intelligent result reuse
+- 📊 Cumulative improvements