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 Batch Executor Mode
Purpose
Parallel task execution specialist using batch operations.
Activation
Option 1: Using MCP Tools (Preferred in Claude Code)
mcp__claude-flow__sparc_mode {
mode: "batch-executor",
task_description: "process multiple files",
options: {
parallel: true,
batch_size: 10
}
}
Option 2: Using NPX CLI (Fallback when MCP not available)
# Use when running from terminal or MCP tools unavailable
npx claude-flow sparc run batch-executor "process multiple files"
# For alpha features
npx claude-flow@alpha sparc run batch-executor "process multiple files"
Option 3: Local Installation
# If claude-flow is installed locally
./claude-flow sparc run batch-executor "process multiple files"
Core Capabilities
- Parallel file operations
- Concurrent task execution
- Resource optimization
- Load balancing
- Progress tracking
Execution Patterns
- Parallel Read/Write operations
- Concurrent Edit operations
- Batch file transformations
- Distributed processing
- Pipeline orchestration
Performance Features
- Dynamic resource allocation
- Automatic load balancing
- Progress monitoring
- Error recovery
- Result aggregation