Files
wifi-densepose/.claude/commands/automation/session-memory.md
Claude 6ed69a3d48 feat: Complete Rust port of WiFi-DensePose with modular crates
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
2026-01-13 03:11:16 +00:00

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Markdown

# Cross-Session Memory
## Purpose
Maintain context and learnings across Claude Code sessions for continuous improvement.
## Memory Features
### 1. Automatic State Persistence
At session end, automatically saves:
- Active agents and specializations
- Task history and patterns
- Performance metrics
- Neural network weights
- Knowledge base updates
### 2. Session Restoration
```javascript
// Using MCP tools for memory operations
mcp__claude-flow__memory_usage({
"action": "retrieve",
"key": "session-state",
"namespace": "sessions"
})
// Restore swarm state
mcp__claude-flow__context_restore({
"snapshotId": "sess-123"
})
```
**Fallback with npx:**
```bash
npx claude-flow hook session-restore --session-id "sess-123"
```
### 3. Memory Types
**Project Memory:**
- File relationships
- Common edit patterns
- Testing approaches
- Build configurations
**Agent Memory:**
- Specialization levels
- Task success rates
- Optimization strategies
- Error patterns
**Performance Memory:**
- Bottleneck history
- Optimization results
- Token usage patterns
- Efficiency trends
### 4. Privacy & Control
```javascript
// List memory contents
mcp__claude-flow__memory_usage({
"action": "list",
"namespace": "sessions"
})
// Delete specific memory
mcp__claude-flow__memory_usage({
"action": "delete",
"key": "session-123",
"namespace": "sessions"
})
// Backup memory
mcp__claude-flow__memory_backup({
"path": "./backups/memory-backup.json"
})
```
**Manual control:**
```bash
# View stored memory
ls .claude-flow/memory/
# Disable memory
export CLAUDE_FLOW_MEMORY_PERSIST=false
```
## Benefits
- 🧠 Contextual awareness
- 📈 Cumulative learning
- ⚡ Faster task completion
- 🎯 Personalized optimization