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feat(analytics): Implement Analytics Dashboard and Token Management
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# Step 1: Context Discovery & Initialization
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## MANDATORY EXECUTION RULES (READ FIRST):
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- NEVER generate content without user input
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- ALWAYS treat this as collaborative discovery between technical peers
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- YOU ARE A FACILITATOR, not a content generator
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- FOCUS on discovering existing project context and technology stack
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- IDENTIFY critical implementation rules that AI agents need
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- ABSOLUTELY NO TIME ESTIMATES
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- ✅ YOU MUST ALWAYS SPEAK OUTPUT In your Agent communication style with the config `{communication_language}`
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## EXECUTION PROTOCOLS:
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- Show your analysis before taking any action
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- Read existing project files to understand current context
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- Initialize document and update frontmatter
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- FORBIDDEN to load next step until discovery is complete
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## CONTEXT BOUNDARIES:
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- Variables from workflow.md are available in memory
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- Focus on existing project files and architecture decisions
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- Look for patterns, conventions, and unique requirements
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- Prioritize rules that prevent implementation mistakes
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## YOUR TASK:
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Discover the project's game engine, technology stack, existing patterns, and critical implementation rules that AI agents must follow when writing game code.
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## DISCOVERY SEQUENCE:
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### 1. Check for Existing Project Context
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First, check if project context already exists:
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- Look for file at `{output_folder}/project-context.md`
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- If exists: Read complete file to understand existing rules
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- Present to user: "Found existing project context with {number_of_sections} sections. Would you like to update this or create a new one?"
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### 2. Discover Game Engine & Technology Stack
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Load and analyze project files to identify technologies:
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**Architecture Document:**
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- Look for `{output_folder}/game-architecture.md` or `{planning_artifacts}/architecture.md`
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- Extract engine choice with specific version (Unity, Unreal, Godot, custom)
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- Note architectural decisions that affect implementation
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**Engine-Specific Files:**
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- Unity: Check for `ProjectSettings/ProjectVersion.txt`, `Packages/manifest.json`
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- Unreal: Check for `.uproject` files, `Config/DefaultEngine.ini`
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- Godot: Check for `project.godot`, `export_presets.cfg`
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- Custom: Check for engine config files, build scripts
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**Package/Dependency Files:**
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- Unity: `Packages/manifest.json`, NuGet packages
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- Unreal: `.Build.cs` files, plugin configs
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- Godot: `addons/` directory, GDExtension configs
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- Web-based: `package.json`, `requirements.txt`
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**Configuration Files:**
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- Build tool configs
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- Linting and formatting configs
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- Testing configurations
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- CI/CD pipeline configs
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### 3. Identify Existing Code Patterns
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Search through existing codebase for patterns:
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**Naming Conventions:**
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- Script/class naming patterns
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- Asset naming conventions
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- Scene/level naming patterns
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- Test file naming patterns
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**Code Organization:**
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- How components/scripts are structured
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- Where utilities and helpers are placed
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- How systems are organized
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- Folder hierarchy patterns
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**Engine-Specific Patterns:**
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- Unity: MonoBehaviour patterns, ScriptableObject usage, serialization rules
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- Unreal: Actor/Component patterns, Blueprint integration, UE macros
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- Godot: Node patterns, signal usage, autoload patterns
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### 4. Extract Critical Implementation Rules
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Look for rules that AI agents might miss:
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**Engine-Specific Rules:**
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- Unity: Assembly definitions, Unity lifecycle methods, coroutine patterns
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- Unreal: UPROPERTY/UFUNCTION usage, garbage collection rules, tick patterns
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- Godot: `_ready` vs `_enter_tree`, node ownership, scene instancing
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**Performance Rules:**
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- Frame budget constraints
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- Memory allocation patterns
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- Hot path optimization requirements
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- Object pooling patterns
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**Platform-Specific Rules:**
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- Target platform constraints
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- Input handling conventions
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- Platform-specific code patterns
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- Build configuration rules
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**Testing Rules:**
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- Test structure requirements
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- Mock usage conventions
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- Integration vs unit test boundaries
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- Play mode vs edit mode testing
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### 5. Initialize Project Context Document
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Based on discovery, create or update the context document:
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#### A. Fresh Document Setup (if no existing context)
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Copy template from `{installed_path}/project-context-template.md` to `{output_folder}/project-context.md`
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Initialize frontmatter with:
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```yaml
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---
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project_name: '{{project_name}}'
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user_name: '{{user_name}}'
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date: '{{date}}'
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sections_completed: ['technology_stack']
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existing_patterns_found: { { number_of_patterns_discovered } }
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---
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```
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#### B. Existing Document Update
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Load existing context and prepare for updates
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Set frontmatter `sections_completed` to track what will be updated
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### 6. Present Discovery Summary
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Report findings to user:
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"Welcome {{user_name}}! I've analyzed your game project for {{project_name}} to discover the context that AI agents need.
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**Game Engine & Stack Discovered:**
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{{engine_and_version}}
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{{list_of_technologies_with_versions}}
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**Existing Patterns Found:**
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- {{number_of_patterns}} implementation patterns
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- {{number_of_conventions}} coding conventions
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- {{number_of_rules}} critical rules
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**Key Areas for Context Rules:**
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- {{area_1}} (e.g., Engine lifecycle and patterns)
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- {{area_2}} (e.g., Performance and optimization)
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- {{area_3}} (e.g., Platform-specific requirements)
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{if_existing_context}
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**Existing Context:** Found {{sections}} sections already defined. We can update or add to these.
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{/if_existing_context}
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Ready to create/update your project context. This will help AI agents implement game code consistently with your project's standards.
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[C] Continue to context generation"
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## SUCCESS METRICS:
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- Existing project context properly detected and handled
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- Game engine and technology stack accurately identified with versions
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- Critical implementation patterns discovered
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- Project context document properly initialized
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- Discovery findings clearly presented to user
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- User ready to proceed with context generation
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## FAILURE MODES:
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- Not checking for existing project context before creating new one
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- Missing critical engine versions or configurations
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- Overlooking important coding patterns or conventions
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- Not initializing frontmatter properly
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- Not presenting clear discovery summary to user
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## NEXT STEP:
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After user selects [C] to continue, load `./step-02-generate.md` to collaboratively generate the specific project context rules.
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Remember: Do NOT proceed to step-02 until user explicitly selects [C] from the menu and discovery is confirmed!
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