6ed69a3d48
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
1.2 KiB
1.2 KiB
SPARC Analyzer Mode
Purpose
Deep code and data analysis with batch processing capabilities.
Activation
Option 1: Using MCP Tools (Preferred in Claude Code)
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)
# 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
# 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