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
123 lines
2.5 KiB
Markdown
123 lines
2.5 KiB
Markdown
# auto agent
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Automatically spawn and manage agents based on task requirements.
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## Usage
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```bash
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npx claude-flow auto agent [options]
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```
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## Options
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- `--task, -t <description>` - Task description for agent analysis
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- `--max-agents, -m <number>` - Maximum agents to spawn (default: auto)
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- `--min-agents <number>` - Minimum agents required (default: 1)
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- `--strategy, -s <type>` - Selection strategy: optimal, minimal, balanced
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- `--no-spawn` - Analyze only, don't spawn agents
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## Examples
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### Basic auto-spawning
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```bash
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npx claude-flow auto agent --task "Build a REST API with authentication"
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```
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### Constrained spawning
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```bash
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npx claude-flow auto agent -t "Debug performance issue" --max-agents 3
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```
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### Analysis only
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```bash
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npx claude-flow auto agent -t "Refactor codebase" --no-spawn
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```
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### Minimal strategy
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```bash
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npx claude-flow auto agent -t "Fix bug in login" -s minimal
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```
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## How It Works
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1. **Task Analysis**
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- Parses task description
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- Identifies required skills
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- Estimates complexity
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- Determines parallelization opportunities
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2. **Agent Selection**
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- Matches skills to agent types
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- Considers task dependencies
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- Optimizes for efficiency
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- Respects constraints
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3. **Topology Selection**
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- Chooses optimal swarm structure
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- Configures communication patterns
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- Sets up coordination rules
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- Enables monitoring
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4. **Automatic Spawning**
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- Creates selected agents
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- Assigns specific roles
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- Distributes subtasks
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- Initiates coordination
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## Agent Types Selected
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- **Architect**: System design, architecture decisions
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- **Coder**: Implementation, code generation
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- **Tester**: Test creation, quality assurance
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- **Analyst**: Performance, optimization
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- **Researcher**: Documentation, best practices
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- **Coordinator**: Task management, progress tracking
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## Strategies
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### Optimal
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- Maximum efficiency
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- May spawn more agents
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- Best for complex tasks
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- Highest resource usage
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### Minimal
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- Minimum viable agents
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- Conservative approach
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- Good for simple tasks
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- Lowest resource usage
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### Balanced
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- Middle ground
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- Adaptive to complexity
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- Default strategy
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- Good performance/resource ratio
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## Integration with Claude Code
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```javascript
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// In Claude Code after auto-spawning
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mcp__claude-flow__auto_agent {
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task: "Build authentication system",
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strategy: "balanced",
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maxAgents: 6
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}
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```
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## See Also
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- `agent spawn` - Manual agent creation
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- `swarm init` - Initialize swarm manually
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- `smart spawn` - Intelligent agent spawning
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- `workflow select` - Choose predefined workflows
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