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
62 lines
1.5 KiB
Markdown
62 lines
1.5 KiB
Markdown
# Automatic Topology Selection
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## Purpose
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Automatically select the optimal swarm topology based on task complexity analysis.
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## How It Works
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### 1. Task Analysis
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The system analyzes your task description to determine:
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- Complexity level (simple/medium/complex)
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- Required agent types
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- Estimated duration
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- Resource requirements
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### 2. Topology Selection
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Based on analysis, it selects:
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- **Star**: For simple, centralized tasks
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- **Mesh**: For medium complexity with flexibility needs
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- **Hierarchical**: For complex tasks requiring structure
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- **Ring**: For sequential processing workflows
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### 3. Example Usage
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**Simple Task:**
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```
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Tool: mcp__claude-flow__task_orchestrate
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Parameters: {"task": "Fix typo in README.md"}
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Result: Automatically uses star topology with single agent
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```
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**Complex Task:**
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```
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Tool: mcp__claude-flow__task_orchestrate
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Parameters: {"task": "Refactor authentication system with JWT, add tests, update documentation"}
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Result: Automatically uses hierarchical topology with architect, coder, and tester agents
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```
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## Benefits
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- 🎯 Optimal performance for each task type
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- 🤖 Automatic agent assignment
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- ⚡ Reduced setup time
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- 📊 Better resource utilization
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## Hook Configuration
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The pre-task hook automatically handles topology selection:
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```json
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{
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"command": "npx claude-flow hook pre-task --optimize-topology"
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}
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```
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## Direct Optimization
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```
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Tool: mcp__claude-flow__topology_optimize
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Parameters: {"swarmId": "current"}
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```
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## CLI Usage
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```bash
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# Auto-optimize topology via CLI
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npx claude-flow optimize topology
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``` |