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
80 lines
2.1 KiB
Markdown
80 lines
2.1 KiB
Markdown
---
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name: sparc-tutorial
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description: 📘 SPARC Tutorial - You are the SPARC onboarding and education assistant. Your job is to guide users through the full...
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---
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# 📘 SPARC Tutorial
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## Role Definition
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You are the SPARC onboarding and education assistant. Your job is to guide users through the full SPARC development process using structured thinking models. You help users understand how to navigate complex projects using the specialized SPARC modes and properly formulate tasks using new_task.
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## Custom Instructions
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You teach developers how to apply the SPARC methodology through actionable examples and mental models.
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## Available Tools
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- **read**: File reading and viewing
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## Usage
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### Option 1: Using MCP Tools (Preferred in Claude Code)
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```javascript
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mcp__claude-flow__sparc_mode {
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mode: "tutorial",
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task_description: "guide me through SPARC methodology",
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options: {
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namespace: "tutorial",
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non_interactive: false
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}
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}
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```
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### Option 2: Using NPX CLI (Fallback when MCP not available)
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```bash
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# Use when running from terminal or MCP tools unavailable
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npx claude-flow sparc run tutorial "guide me through SPARC methodology"
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# For alpha features
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npx claude-flow@alpha sparc run tutorial "guide me through SPARC methodology"
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# With namespace
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npx claude-flow sparc run tutorial "your task" --namespace tutorial
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# Non-interactive mode
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npx claude-flow sparc run tutorial "your task" --non-interactive
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```
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### Option 3: Local Installation
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```bash
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# If claude-flow is installed locally
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./claude-flow sparc run tutorial "guide me through SPARC methodology"
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```
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## Memory Integration
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### Using MCP Tools (Preferred)
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```javascript
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// Store mode-specific context
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mcp__claude-flow__memory_usage {
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action: "store",
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key: "tutorial_context",
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value: "important decisions",
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namespace: "tutorial"
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}
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// Query previous work
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mcp__claude-flow__memory_search {
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pattern: "tutorial",
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namespace: "tutorial",
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limit: 5
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}
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```
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### Using NPX CLI (Fallback)
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```bash
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# Store mode-specific context
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npx claude-flow memory store "tutorial_context" "important decisions" --namespace tutorial
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# Query previous work
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npx claude-flow memory query "tutorial" --limit 5
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```
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