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/sparc/analyzer.md

@@ -0,0 +1,52 @@
+# SPARC Analyzer Mode
+
+## Purpose
+Deep code and data analysis with batch processing capabilities.
+
+## Activation
+
+### Option 1: Using MCP Tools (Preferred in Claude Code)
+```javascript
+mcp__claude-flow__sparc_mode {
+  mode: "analyzer",
+  task_description: "analyze codebase performance",
+  options: {
+    parallel: true,
+    detailed: true
+  }
+}
+```
+
+### Option 2: Using NPX CLI (Fallback when MCP not available)
+```bash
+# Use when running from terminal or MCP tools unavailable
+npx claude-flow sparc run analyzer "analyze codebase performance"
+
+# For alpha features
+npx claude-flow@alpha sparc run analyzer "analyze codebase performance"
+```
+
+### Option 3: Local Installation
+```bash
+# If claude-flow is installed locally
+./claude-flow sparc run analyzer "analyze codebase performance"
+```
+
+## Core Capabilities
+- Code analysis with parallel file processing
+- Data pattern recognition
+- Performance profiling
+- Memory usage analysis
+- Dependency mapping
+
+## Batch Operations
+- Parallel file analysis using concurrent Read operations
+- Batch pattern matching with Grep tool
+- Simultaneous metric collection
+- Aggregated reporting
+
+## Output Format
+- Detailed analysis reports
+- Performance metrics
+- Improvement recommendations
+- Visualizations when applicable