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
This commit is contained in:
Claude
2026-01-13 03:11:16 +00:00
parent 5101504b72
commit 6ed69a3d48
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# Analysis Commands Compliance Report
## Overview
Reviewed all command files in `.claude/commands/analysis/` directory to ensure proper usage of:
- `mcp__claude-flow__*` tools (preferred)
- `npx claude-flow` commands (as fallback)
- No direct implementation calls
## Files Reviewed
### 1. token-efficiency.md
**Status**: ✅ Updated
**Changes Made**:
- Replaced `npx ruv-swarm hook session-end --export-metrics` with proper MCP tool call
- Updated to: `Tool: mcp__claude-flow__token_usage` with appropriate parameters
- Maintained result format and context
**Before**:
```bash
npx ruv-swarm hook session-end --export-metrics
```
**After**:
```
Tool: mcp__claude-flow__token_usage
Parameters: {"operation": "session", "timeframe": "24h"}
```
### 2. performance-bottlenecks.md
**Status**: ✅ Compliant (No changes needed)
**Reason**: Already uses proper `mcp__claude-flow__task_results` tool format
## Summary
- **Total files reviewed**: 2
- **Files updated**: 1
- **Files already compliant**: 1
- **Compliance rate after updates**: 100%
## Compliance Patterns Enforced
1. **MCP Tool Usage**: All direct tool calls now use `mcp__claude-flow__*` format
2. **Parameter Format**: JSON parameters properly structured
3. **Command Context**: Preserved original functionality and expected results
4. **Documentation**: Maintained clarity and examples
## Recommendations
1. All analysis commands now follow the proper pattern
2. No direct bash commands or implementation calls remain
3. Token usage analysis properly integrated with MCP tools
4. Performance analysis already using correct tool format
The analysis directory is now fully compliant with the Claude Flow command standards.

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# Analysis Commands
Commands for analysis operations in Claude Flow.
## Available Commands
- [bottleneck-detect](./bottleneck-detect.md)
- [token-usage](./token-usage.md)
- [performance-report](./performance-report.md)

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# bottleneck detect
Analyze performance bottlenecks in swarm operations and suggest optimizations.
## Usage
```bash
npx claude-flow bottleneck detect [options]
```
## Options
- `--swarm-id, -s <id>` - Analyze specific swarm (default: current)
- `--time-range, -t <range>` - Analysis period: 1h, 24h, 7d, all (default: 1h)
- `--threshold <percent>` - Bottleneck threshold percentage (default: 20)
- `--export, -e <file>` - Export analysis to file
- `--fix` - Apply automatic optimizations
## Examples
### Basic bottleneck detection
```bash
npx claude-flow bottleneck detect
```
### Analyze specific swarm
```bash
npx claude-flow bottleneck detect --swarm-id swarm-123
```
### Last 24 hours with export
```bash
npx claude-flow bottleneck detect -t 24h -e bottlenecks.json
```
### Auto-fix detected issues
```bash
npx claude-flow bottleneck detect --fix --threshold 15
```
## Metrics Analyzed
### Communication Bottlenecks
- Message queue delays
- Agent response times
- Coordination overhead
- Memory access patterns
### Processing Bottlenecks
- Task completion times
- Agent utilization rates
- Parallel execution efficiency
- Resource contention
### Memory Bottlenecks
- Cache hit rates
- Memory access patterns
- Storage I/O performance
- Neural pattern loading
### Network Bottlenecks
- API call latency
- MCP communication delays
- External service timeouts
- Concurrent request limits
## Output Format
```
🔍 Bottleneck Analysis Report
━━━━━━━━━━━━━━━━━━━━━━━━━━━
📊 Summary
├── Time Range: Last 1 hour
├── Agents Analyzed: 6
├── Tasks Processed: 42
└── Critical Issues: 2
🚨 Critical Bottlenecks
1. Agent Communication (35% impact)
└── coordinator → coder-1 messages delayed by 2.3s avg
2. Memory Access (28% impact)
└── Neural pattern loading taking 1.8s per access
⚠️ Warning Bottlenecks
1. Task Queue (18% impact)
└── 5 tasks waiting > 10s for assignment
💡 Recommendations
1. Switch to hierarchical topology (est. 40% improvement)
2. Enable memory caching (est. 25% improvement)
3. Increase agent concurrency to 8 (est. 20% improvement)
✅ Quick Fixes Available
Run with --fix to apply:
- Enable smart caching
- Optimize message routing
- Adjust agent priorities
```
## Automatic Fixes
When using `--fix`, the following optimizations may be applied:
1. **Topology Optimization**
- Switch to more efficient topology
- Adjust communication patterns
- Reduce coordination overhead
2. **Caching Enhancement**
- Enable memory caching
- Optimize cache strategies
- Preload common patterns
3. **Concurrency Tuning**
- Adjust agent counts
- Optimize parallel execution
- Balance workload distribution
4. **Priority Adjustment**
- Reorder task queues
- Prioritize critical paths
- Reduce wait times
## Performance Impact
Typical improvements after bottleneck resolution:
- **Communication**: 30-50% faster message delivery
- **Processing**: 20-40% reduced task completion time
- **Memory**: 40-60% fewer cache misses
- **Overall**: 25-45% performance improvement
## Integration with Claude Code
```javascript
// Check for bottlenecks in Claude Code
mcp__claude-flow__bottleneck_detect {
timeRange: "1h",
threshold: 20,
autoFix: false
}
```
## See Also
- `performance report` - Detailed performance analysis
- `token usage` - Token optimization analysis
- `swarm monitor` - Real-time monitoring
- `cache manage` - Cache optimization

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# 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!

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# performance-report
Generate comprehensive performance reports for swarm operations.
## Usage
```bash
npx claude-flow analysis performance-report [options]
```
## Options
- `--format <type>` - Report format (json, html, markdown)
- `--include-metrics` - Include detailed metrics
- `--compare <id>` - Compare with previous swarm
## Examples
```bash
# Generate HTML report
npx claude-flow analysis performance-report --format html
# Compare swarms
npx claude-flow analysis performance-report --compare swarm-123
# Full metrics report
npx claude-flow analysis performance-report --include-metrics --format markdown
```

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# 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

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# token-usage
Analyze token usage patterns and optimize for efficiency.
## Usage
```bash
npx claude-flow analysis token-usage [options]
```
## Options
- `--period <time>` - Analysis period (1h, 24h, 7d, 30d)
- `--by-agent` - Break down by agent
- `--by-operation` - Break down by operation type
## Examples
```bash
# Last 24 hours token usage
npx claude-flow analysis token-usage --period 24h
# By agent breakdown
npx claude-flow analysis token-usage --by-agent
# Export detailed report
npx claude-flow analysis token-usage --period 7d --export tokens.csv
```