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/performance-bottlenecks.md

@@ -0,0 +1,59 @@
+# Performance Bottleneck Analysis
+
+## Purpose
+Identify and resolve performance bottlenecks in your development workflow.
+
+## Automated Analysis
+
+### 1. Real-time Detection
+The post-task hook automatically analyzes:
+- Execution time vs. complexity
+- Agent utilization rates
+- Resource constraints
+- Operation patterns
+
+### 2. Common Bottlenecks
+
+**Time Bottlenecks:**
+- Tasks taking > 5 minutes
+- Sequential operations that could parallelize
+- Redundant file operations
+
+**Coordination Bottlenecks:**
+- Single agent for complex tasks
+- Unbalanced agent workloads
+- Poor topology selection
+
+**Resource Bottlenecks:**
+- High operation count (> 100)
+- Memory constraints
+- I/O limitations
+
+### 3. Improvement Suggestions
+
+```
+Tool: mcp__claude-flow__task_results
+Parameters: {"taskId": "task-123", "format": "detailed"}
+
+Result includes:
+{
+  "bottlenecks": [
+    {
+      "type": "coordination",
+      "severity": "high",
+      "description": "Single agent used for complex task",
+      "recommendation": "Spawn specialized agents for parallel work"
+    }
+  ],
+  "improvements": [
+    {
+      "area": "execution_time",
+      "suggestion": "Use parallel task execution",
+      "expectedImprovement": "30-50% time reduction"
+    }
+  ]
+}
+```
+
+## Continuous Optimization
+The system learns from each task to prevent future bottlenecks!