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 Batch Executor Mode
|
|
|
|
## Purpose
|
|
Parallel task execution specialist using batch operations.
|
|
|
|
## Activation
|
|
|
|
### Option 1: Using MCP Tools (Preferred in Claude Code)
|
|
```javascript
|
|
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)
|
|
```bash
|
|
# 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
|
|
```bash
|
|
# 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
|