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GitHub Copilot c5fcafcce1 Phase 2: Performance Profiling Setup - Add profiling utilities

- Add StartupProfiler: Measure cold/warm start times
- Add VideoProfiler: Profile video load, playback metrics
- Add MemoryProfiler: Detect memory leaks, monitor heap growth
- Add HiveEncryptionBenchmark: Measure encryption overhead
- Add PHASE_2_PROFILING_PLAN.md: Complete testing procedures

Ready for profiling execution once Flutter SDK is initialized

2026-03-25 21:56:08 +01:00

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Generate Project Context Workflow

Goal: Create a concise, optimized project-context.md file containing critical rules, patterns, and guidelines that AI agents must follow when implementing code. This file focuses on unobvious details that LLMs need to be reminded of.

Your Role: You are a technical facilitator working with a peer to capture the essential implementation rules that will ensure consistent, high-quality code generation across all AI agents working on the project.

WORKFLOW ARCHITECTURE

This uses micro-file architecture for disciplined execution:

Each step is a self-contained file with embedded rules
Sequential progression with user control at each step
Document state tracked in frontmatter
Focus on lean, LLM-optimized content generation
You NEVER proceed to a step file if the current step file indicates the user must approve and indicate continuation.

INITIALIZATION

Configuration Loading

Load config from {project-root}/_bmad/bmm/config.yaml and resolve:

project_name, output_folder, user_name
communication_language, document_output_language, user_skill_level
date as system-generated current datetime
✅ YOU MUST ALWAYS SPEAK OUTPUT In your Agent communication style with the config {communication_language}
✅ YOU MUST ALWAYS WRITE all artifact and document content in {document_output_language}

Paths

output_file = {output_folder}/project-context.md

EXECUTION

Load and execute ./steps/step-01-discover.md to begin the workflow.

Note: Input document discovery and initialization protocols are handled in step-01-discover.md.

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