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