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
566 B
566 B
token-usage
Analyze token usage patterns and optimize for efficiency.
Usage
npx claude-flow analysis token-usage [options]
Options
--period <time>- Analysis period (1h, 24h, 7d, 30d)--by-agent- Break down by agent--by-operation- Break down by operation type
Examples
# Last 24 hours token usage
npx claude-flow analysis token-usage --period 24h
# By agent breakdown
npx claude-flow analysis token-usage --by-agent
# Export detailed report
npx claude-flow analysis token-usage --period 7d --export tokens.csv