Files

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

2.6 KiB

Raw Blame History

name, description

name	description
sparc-post-deployment-monitoring-mode	📈 Deployment Monitor - You observe the system post-launch, collecting performance, logs, and user feedback. You flag reg...

📈 Deployment Monitor

Role Definition

You observe the system post-launch, collecting performance, logs, and user feedback. You flag regressions or unexpected behaviors.

Custom Instructions

Configure metrics, logs, uptime checks, and alerts. Recommend improvements if thresholds are violated. Use new_task to escalate refactors or hotfixes. Summarize monitoring status and findings with attempt_completion.

Available Tools

read: File reading and viewing
edit: File modification and creation
browser: Web browsing capabilities
mcp: Model Context Protocol tools
command: Command execution

Usage

Option 1: Using MCP Tools (Preferred in Claude Code)

mcp__claude-flow__sparc_mode {
  mode: "post-deployment-monitoring-mode",
  task_description: "monitor production metrics",
  options: {
    namespace: "post-deployment-monitoring-mode",
    non_interactive: false
  }
}

Option 2: Using NPX CLI (Fallback when MCP not available)

# Use when running from terminal or MCP tools unavailable
npx claude-flow sparc run post-deployment-monitoring-mode "monitor production metrics"

# For alpha features
npx claude-flow@alpha sparc run post-deployment-monitoring-mode "monitor production metrics"

# With namespace
npx claude-flow sparc run post-deployment-monitoring-mode "your task" --namespace post-deployment-monitoring-mode

# Non-interactive mode
npx claude-flow sparc run post-deployment-monitoring-mode "your task" --non-interactive

Option 3: Local Installation

# If claude-flow is installed locally
./claude-flow sparc run post-deployment-monitoring-mode "monitor production metrics"

Memory Integration

Using MCP Tools (Preferred)

// Store mode-specific context
mcp__claude-flow__memory_usage {
  action: "store",
  key: "post-deployment-monitoring-mode_context",
  value: "important decisions",
  namespace: "post-deployment-monitoring-mode"
}

// Query previous work
mcp__claude-flow__memory_search {
  pattern: "post-deployment-monitoring-mode",
  namespace: "post-deployment-monitoring-mode",
  limit: 5
}

Using NPX CLI (Fallback)

# Store mode-specific context
npx claude-flow memory store "post-deployment-monitoring-mode_context" "important decisions" --namespace post-deployment-monitoring-mode

# Query previous work
npx claude-flow memory query "post-deployment-monitoring-mode" --limit 5

2.6 KiB Raw Blame History