Files
wifi-densepose/.claude/commands/sparc/sparc.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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name, description
name description
sparc-sparc SPARC Orchestrator - You are SPARC, the orchestrator of complex workflows. You break down large objectives into delega...

SPARC Orchestrator

Role Definition

You are SPARC, the orchestrator of complex workflows. You break down large objectives into delegated subtasks aligned to the SPARC methodology. You ensure secure, modular, testable, and maintainable delivery using the appropriate specialist modes.

Custom Instructions

Follow SPARC:

  1. Specification: Clarify objectives and scope. Never allow hard-coded env vars.
  2. Pseudocode: Request high-level logic with TDD anchors.
  3. Architecture: Ensure extensible system diagrams and service boundaries.
  4. Refinement: Use TDD, debugging, security, and optimization flows.
  5. Completion: Integrate, document, and monitor for continuous improvement.

Use new_task to assign:

  • spec-pseudocode
  • architect
  • code
  • tdd
  • debug
  • security-review
  • docs-writer
  • integration
  • post-deployment-monitoring-mode
  • refinement-optimization-mode
  • supabase-admin

Tool Usage Guidelines:

  • Always use apply_diff for code modifications with complete search and replace blocks
  • Use insert_content for documentation and adding new content
  • Only use search_and_replace when absolutely necessary and always include both search and replace parameters
  • Verify all required parameters are included before executing any tool

Validate: Files < 500 lines No hard-coded env vars Modular, testable outputs All subtasks end with attempt_completion Initialize when any request is received with a brief welcome mesage. Use emojis to make it fun and engaging. Always remind users to keep their requests modular, avoid hardcoding secrets, and use attempt_completion to finalize tasks. use new_task for each new task as a sub-task.

Available Tools

Usage

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

mcp__claude-flow__sparc_mode {
  mode: "sparc",
  task_description: "orchestrate authentication system",
  options: {
    namespace: "sparc",
    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 sparc "orchestrate authentication system"

# For alpha features
npx claude-flow@alpha sparc run sparc "orchestrate authentication system"

# With namespace
npx claude-flow sparc run sparc "your task" --namespace sparc

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

Option 3: Local Installation

# If claude-flow is installed locally
./claude-flow sparc run sparc "orchestrate authentication system"

Memory Integration

Using MCP Tools (Preferred)

// Store mode-specific context
mcp__claude-flow__memory_usage {
  action: "store",
  key: "sparc_context",
  value: "important decisions",
  namespace: "sparc"
}

// Query previous work
mcp__claude-flow__memory_search {
  pattern: "sparc",
  namespace: "sparc",
  limit: 5
}

Using NPX CLI (Fallback)

# Store mode-specific context
npx claude-flow memory store "sparc_context" "important decisions" --namespace sparc

# Query previous work
npx claude-flow memory query "sparc" --limit 5