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
3.1 KiB
3.1 KiB
SPARC Orchestrator Mode
Purpose
Multi-agent task orchestration with TodoWrite/TodoRead/Task/Memory using MCP tools.
Activation
Option 1: Using MCP Tools (Preferred in Claude Code)
mcp__claude-flow__sparc_mode {
mode: "orchestrator",
task_description: "coordinate feature development"
}
Option 2: Using NPX CLI (Fallback when MCP not available)
# Use when running from terminal or MCP tools unavailable
npx claude-flow sparc run orchestrator "coordinate feature development"
# For alpha features
npx claude-flow@alpha sparc run orchestrator "coordinate feature development"
Option 3: Local Installation
# If claude-flow is installed locally
./claude-flow sparc run orchestrator "coordinate feature development"
Core Capabilities
- Task decomposition
- Agent coordination
- Resource allocation
- Progress tracking
- Result synthesis
Integration Examples
Using MCP Tools (Preferred)
// Initialize orchestration swarm
mcp__claude-flow__swarm_init {
topology: "hierarchical",
strategy: "auto",
maxAgents: 8
}
// Spawn coordinator agent
mcp__claude-flow__agent_spawn {
type: "coordinator",
capabilities: ["task-planning", "resource-management"]
}
// Orchestrate tasks
mcp__claude-flow__task_orchestrate {
task: "feature development",
strategy: "parallel",
dependencies: ["auth", "ui", "api"]
}
Using NPX CLI (Fallback)
# Initialize orchestration swarm
npx claude-flow swarm init --topology hierarchical --strategy auto --max-agents 8
# Spawn coordinator agent
npx claude-flow agent spawn --type coordinator --capabilities "task-planning,resource-management"
# Orchestrate tasks
npx claude-flow task orchestrate --task "feature development" --strategy parallel --deps "auth,ui,api"
Orchestration Patterns
- Hierarchical coordination
- Parallel execution
- Sequential pipelines
- Event-driven flows
- Adaptive strategies
Coordination Tools
- TodoWrite for planning
- Task for agent launch
- Memory for sharing
- Progress monitoring
- Result aggregation
Workflow Example
Using MCP Tools (Preferred)
// 1. Initialize orchestration swarm
mcp__claude-flow__swarm_init {
topology: "hierarchical",
maxAgents: 10
}
// 2. Create workflow
mcp__claude-flow__workflow_create {
name: "feature-development",
steps: ["design", "implement", "test", "deploy"]
}
// 3. Execute orchestration
mcp__claude-flow__sparc_mode {
mode: "orchestrator",
options: {parallel: true, monitor: true},
task_description: "develop user management system"
}
// 4. Monitor progress
mcp__claude-flow__swarm_monitor {
swarmId: "current",
interval: 5000
}
Using NPX CLI (Fallback)
# 1. Initialize orchestration swarm
npx claude-flow swarm init --topology hierarchical --max-agents 10
# 2. Create workflow
npx claude-flow workflow create --name "feature-development" --steps "design,implement,test,deploy"
# 3. Execute orchestration
npx claude-flow sparc run orchestrator "develop user management system" --parallel --monitor
# 4. Monitor progress
npx claude-flow swarm monitor --interval 5000