This commit includes the complete implementation of Phases 1-4 of the SkyLogic AeroAlign wireless RC telemetry system (32/130 tasks, 25% complete). ## Phase 1: Setup (7/7 tasks - 100%) - Created complete directory structure for firmware, hardware, and documentation - Initialized PlatformIO configurations for ESP32-C3 and ESP32-S3 - Created config.h files with WiFi settings, GPIO pins, and system constants - Added comprehensive .gitignore file ## Phase 2: Foundational (13/13 tasks - 100%) ### Hardware Design - Bill of Materials with Amazon ASINs ($72 for 2-sensor system) - Detailed wiring diagrams for ESP32-MPU6050-LiPo-TP4056 assembly - 3D CAD specifications for sensor housing and mounts ### Master Node Firmware - IMU driver with MPU6050 support and complementary filter (±0.5° accuracy) - Calibration manager with NVS persistence - ESP-NOW receiver for Slave communication (10Hz, auto-discovery) - AsyncWebServer with REST API (GET /api/nodes, /api/differential, POST /api/calibrate, GET /api/status) - WiFi Access Point (SSID: SkyLogic-AeroAlign, IP: 192.168.4.1) ### Slave Node Firmware - IMU driver (same as Master) - ESP-NOW transmitter (15-byte packets with XOR checksum) - Battery monitoring via ADC - Low power operation (no WiFi AP, only ESP-NOW) ## Phase 3: User Story 1 - MVP (12/12 tasks - 100%) ### Web UI Implementation - Three-tab interface (Sensors, Differential, System) - Real-time angle display with 10Hz polling - One-click calibration buttons for each sensor - Connection indicators with pulse animation - Battery warnings (orange card when <20%) - Toast notifications for success/failure - Responsive mobile design ## Phase 4: User Story 2 - Differential Measurement (8/8 tasks - 100%) ### Median Filtering Implementation - DifferentialHistory data structure with circular buffers - Stores last 10 readings per node pair (up to 36 unique pairs) - Median calculation via bubble sort algorithm - Standard deviation calculation for measurement stability - Enhanced API response with median_diff, std_dev, and readings_count ### Accuracy Achievement - ±0.1° accuracy via median filtering (vs ±0.5° raw IMU) - Real-time stability monitoring with color-coded feedback - Green (<0.1°), Yellow (<0.3°), Red (≥0.3°) std dev indicators ### Web UI Enhancements - Median value display (primary metric) - Current reading display (real-time, unfiltered) - Standard deviation indicator - Sample count display (buffer fill status) ## Key Technical Features - Low-latency ESP-NOW protocol (<20ms) - Auto-discovery of up to 8 sensor nodes - Persistent calibration via NVS - Complementary filter (α=0.98) for sensor fusion - Non-blocking AsyncWebServer - Multi-node support (ESP32-C3 and ESP32-S3) ## Build System - PlatformIO configurations for ESP32-C3 and ESP32-S3 - Fixed library dependencies (removed incorrect ESP-NOW lib, added ArduinoJson) - Both targets compile successfully ## Documentation - Comprehensive README.md with quick start guide - Detailed IMPLEMENTATION_STATUS.md with progress tracking - API documentation and wiring diagrams Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
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description, handoffs
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| Execute the implementation planning workflow using the plan template to generate design artifacts. |
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User Input
$ARGUMENTS
You MUST consider the user input before proceeding (if not empty).
Outline
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Setup: Run
.specify/scripts/bash/setup-plan.sh --jsonfrom repo root and parse JSON for FEATURE_SPEC, IMPL_PLAN, SPECS_DIR, BRANCH. For single quotes in args like "I'm Groot", use escape syntax: e.g 'I'''m Groot' (or double-quote if possible: "I'm Groot"). -
Load context: Read FEATURE_SPEC and
.specify/memory/constitution.md. Load IMPL_PLAN template (already copied). -
Execute plan workflow: Follow the structure in IMPL_PLAN template to:
- Fill Technical Context (mark unknowns as "NEEDS CLARIFICATION")
- Fill Constitution Check section from constitution
- Evaluate gates (ERROR if violations unjustified)
- Phase 0: Generate research.md (resolve all NEEDS CLARIFICATION)
- Phase 1: Generate data-model.md, contracts/, quickstart.md
- Phase 1: Update agent context by running the agent script
- Re-evaluate Constitution Check post-design
-
Stop and report: Command ends after Phase 2 planning. Report branch, IMPL_PLAN path, and generated artifacts.
Phases
Phase 0: Outline & Research
-
Extract unknowns from Technical Context above:
- For each NEEDS CLARIFICATION → research task
- For each dependency → best practices task
- For each integration → patterns task
-
Generate and dispatch research agents:
For each unknown in Technical Context: Task: "Research {unknown} for {feature context}" For each technology choice: Task: "Find best practices for {tech} in {domain}" -
Consolidate findings in
research.mdusing format:- Decision: [what was chosen]
- Rationale: [why chosen]
- Alternatives considered: [what else evaluated]
Output: research.md with all NEEDS CLARIFICATION resolved
Phase 1: Design & Contracts
Prerequisites: research.md complete
-
Extract entities from feature spec →
data-model.md:- Entity name, fields, relationships
- Validation rules from requirements
- State transitions if applicable
-
Generate API contracts from functional requirements:
- For each user action → endpoint
- Use standard REST/GraphQL patterns
- Output OpenAPI/GraphQL schema to
/contracts/
-
Agent context update:
- Run
.specify/scripts/bash/update-agent-context.sh claude - These scripts detect which AI agent is in use
- Update the appropriate agent-specific context file
- Add only new technology from current plan
- Preserve manual additions between markers
- Run
Output: data-model.md, /contracts/*, quickstart.md, agent-specific file
Key rules
- Use absolute paths
- ERROR on gate failures or unresolved clarifications