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
2.1 KiB
2.1 KiB
Self-Healing Workflows
Purpose
Automatically detect and recover from errors without interrupting your flow.
Self-Healing Features
1. Error Detection
Monitors for:
- Failed commands
- Syntax errors
- Missing dependencies
- Broken tests
2. Automatic Recovery
Missing Dependencies:
Error: Cannot find module 'express'
→ Automatically runs: npm install express
→ Retries original command
Syntax Errors:
Error: Unexpected token
→ Analyzes error location
→ Suggests fix through analyzer agent
→ Applies fix with confirmation
Test Failures:
Test failed: "user authentication"
→ Spawns debugger agent
→ Analyzes failure cause
→ Implements fix
→ Re-runs tests
3. Learning from Failures
Each recovery improves future prevention:
- Patterns saved to knowledge base
- Similar errors prevented proactively
- Recovery strategies optimized
Pattern Storage:
// Store error patterns
mcp__claude-flow__memory_usage({
"action": "store",
"key": "error-pattern-" + Date.now(),
"value": JSON.stringify(errorData),
"namespace": "error-patterns",
"ttl": 2592000 // 30 days
})
// Analyze patterns
mcp__claude-flow__neural_patterns({
"action": "analyze",
"operation": "error-recovery",
"outcome": "success"
})
Self-Healing Integration
MCP Tool Coordination
// Initialize self-healing swarm
mcp__claude-flow__swarm_init({
"topology": "star",
"maxAgents": 4,
"strategy": "adaptive"
})
// Spawn recovery agents
mcp__claude-flow__agent_spawn({
"type": "monitor",
"name": "Error Monitor",
"capabilities": ["error-detection", "recovery"]
})
// Orchestrate recovery
mcp__claude-flow__task_orchestrate({
"task": "recover from error",
"strategy": "sequential",
"priority": "critical"
})
Fallback Hook Configuration
{
"PostToolUse": [{
"matcher": "^Bash$",
"command": "npx claude-flow hook post-bash --exit-code '${tool.result.exitCode}' --auto-recover"
}]
}
Benefits
- 🛡️ Resilient workflows
- 🔄 Automatic recovery
- 📚 Learns from errors
- ⏱️ Saves debugging time