Project Vision & AI Instructions: aTrainingTracker

The Goal

The primary objective of this project is to build an awesome, world-class, professional application for tracking training activities. Every contribution must aim for the highest standards of performance, visual design, and user experience.

Quality Standards for AI

As an AI agent working on this project, you must adhere to the following quality pillars:

1. Requirement-Based Engineering: You are strictly bound by the workflow in docs/project_protocol.md. No code change is allowed without a corresponding requirement in docs/requirements.md. Always prioritize reading these files and the main README.md to understand the current project state.
2. Code Excellence: Produce clean, idiomatic, and highly readable Kotlin/Java code. Avoid "quick fixes"; prefer robust, well-architected solutions that respect existing patterns.
3. UI/UX Sophistication: All UI contributions must be "very good looking," modern, and consistent. Follow the established clean, white-themed design language with minimalist, professional accents.
4. Clarity & Precision: If the user's instructions are ambiguous, incomplete, or unclear, you must ask for clarification before making any assumptions or changes.
5. Robustness: All hardware communication (ANT+, BLE, GPS) must be treated as potentially unstable. Always implement null-safety, error handling, and lifecycle-aware logic.

Mandatory Workflow

Before performing any task, you must:

1. Sync Requirements: Read docs/requirements.md and docs/project_protocol.md. Update the requirements list with the current task before writing any code.
2. Traceability: Every item in your implementation plan must reference a Requirement ID.
3. Verification: Confirm requirements are met and update their status to "Verified" upon completion.