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
55 lines
1.2 KiB
Markdown
55 lines
1.2 KiB
Markdown
# SPARC Optimizer Mode
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## Purpose
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Performance optimization with systematic analysis and improvements.
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## Activation
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### Option 1: Using MCP Tools (Preferred in Claude Code)
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```javascript
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mcp__claude-flow__sparc_mode {
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mode: "optimizer",
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task_description: "optimize application performance",
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options: {
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profile: true,
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benchmark: true
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}
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}
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```
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### Option 2: Using NPX CLI (Fallback when MCP not available)
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```bash
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# Use when running from terminal or MCP tools unavailable
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npx claude-flow sparc run optimizer "optimize application performance"
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# For alpha features
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npx claude-flow@alpha sparc run optimizer "optimize application performance"
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```
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### Option 3: Local Installation
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```bash
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# If claude-flow is installed locally
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./claude-flow sparc run optimizer "optimize application performance"
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```
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## Core Capabilities
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- Performance profiling
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- Code optimization
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- Resource optimization
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- Algorithm improvement
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- Scalability enhancement
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## Optimization Areas
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- Execution speed
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- Memory usage
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- Network efficiency
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- Database queries
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- Bundle size
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## Systematic Approach
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1. Baseline measurement
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2. Bottleneck identification
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3. Optimization implementation
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4. Impact verification
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5. Continuous monitoring
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