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
73 lines
1.5 KiB
Markdown
73 lines
1.5 KiB
Markdown
# Smart Agent Auto-Spawning
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## Purpose
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Automatically spawn the right agents at the right time without manual intervention.
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## Auto-Spawning Triggers
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### 1. File Type Detection
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When editing files, agents auto-spawn:
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- **JavaScript/TypeScript**: Coder agent
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- **Markdown**: Researcher agent
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- **JSON/YAML**: Analyst agent
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- **Multiple files**: Coordinator agent
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### 2. Task Complexity
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```
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Simple task: "Fix typo"
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→ Single coordinator agent
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Complex task: "Implement OAuth with Google"
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→ Architect + Coder + Tester + Researcher
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```
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### 3. Dynamic Scaling
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The system monitors workload and spawns additional agents when:
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- Task queue grows
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- Complexity increases
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- Parallel opportunities exist
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**Status Monitoring:**
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```javascript
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// Check swarm health
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mcp__claude-flow__swarm_status({
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"swarmId": "current"
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})
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// Monitor agent performance
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mcp__claude-flow__agent_metrics({
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"agentId": "agent-123"
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})
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```
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## Configuration
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### MCP Tool Integration
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Uses Claude Flow MCP tools for agent coordination:
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```javascript
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// Initialize swarm with appropriate topology
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mcp__claude-flow__swarm_init({
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"topology": "mesh",
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"maxAgents": 8,
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"strategy": "auto"
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})
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// Spawn agents based on file type
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mcp__claude-flow__agent_spawn({
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"type": "coder",
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"name": "JavaScript Handler",
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"capabilities": ["javascript", "typescript"]
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})
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```
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### Fallback Configuration
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If MCP tools are unavailable:
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```bash
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npx claude-flow hook pre-task --auto-spawn-agents
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```
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## Benefits
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- 🤖 Zero manual agent management
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- 🎯 Perfect agent selection
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- 📈 Dynamic scaling
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- 💾 Resource efficiency |