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awesome-copilot/collections/plan/prompts/plan-implementation-guide.md
Craig 38d67f3c2f feat: Add collections support with plan collection 
✨ New Features:
• Collections system for organizing domain-specific customizations
• Plan collection with 6 specialized prompts for feature development
• Automated README generation for collections
• Badge generation with proper collection path support

📝 Plan Collection Content:
• Epic planning (PRD and architecture)
• Feature planning (PRD and implementation)
• GitHub issue automation
• Build implementation guide
• Comprehensive development workflow

🔧 Script Enhancements:
• Collections discovery and processing
• Individual collection README generation
• Main README collections section
• Badge URL generation with collection paths
• Proper navigation links to README.md files

📁 Structure:
collections/
├── plan/ (6 prompts)
└── test/ (demo content)

The collections feature enables organized, domain-specific GitHub Copilot customizations with automated documentation and proper VS Code integration.
2025-07-30 20:40:54 +02:00

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---
mode: 'guide'
description: 'Implementation guide for using Epoch role-based development prompts to build features from planning artifacts.'
---
# Epoch Role-Based Implementation Guide
## Overview
This guide explains how to use the Epoch role-specific development prompts to transform planning artifacts (PRDs, implementation plans, GitHub issues) into working software. Each role prompt is designed to take planning outputs and create high-quality implementations with appropriate MCP tool integration.
## Implementation Workflow
### 1. Planning Phase (Already Complete)
Using the planning prompts in `/docs/ways-of-work/plan/`:
- ✅ Epic created: [#28 - Pantry Epic](https://github.com/craigbekker/epoch/issues/28)
- ✅ Feature created: [#29 - Recipe Library Management](https://github.com/craigbekker/epoch/issues/29)
- ✅ Technical enablers created: Database Schema (#30), tRPC API (#31), UI Components (#32), n8n Workflow (#33)
- ✅ User story created: [#34 - Recipe Grid View](https://github.com/craigbekker/epoch/issues/34)
### 2. Implementation Phase (Using Role Prompts)
#### Phase 1: Foundation Infrastructure
**Database Engineer** → Use `/prompts/roles/database-engineer.prompt.md`
- **Input**: Issue #30 - Database Schema & Migrations
- **Output**: PostgreSQL schema, Drizzle migrations, indexes
- **MCP Tools**: Database MCP for query testing and validation
- **Deliverables**:
- `apps/web/drizzle/schema/recipes.ts`
- Migration files in `apps/web/drizzle/`
- Performance indexes and constraints
**Backend Developer** → Use `/prompts/roles/backend-developer.prompt.md`
- **Input**: Issue #31 - tRPC Recipe API Router
- **Dependencies**: Database schema (#30)
- **Output**: Type-safe tRPC endpoints
- **MCP Tools**: Database MCP for testing, GitHub MCP for PR management
- **Deliverables**:
- `apps/web/src/server/api/routers/recipe.ts`
- Input validation schemas
- Error handling and authentication
#### Phase 2: User Interface Foundation
**Frontend Developer** → Use `/prompts/roles/frontend-developer.prompt.md`
- **Input**: Issue #32 - UI Foundation Components
- **Output**: Reusable React components
- **MCP Tools**: Playwright MCP for accessibility and interaction testing
- **Deliverables**:
- `packages/ui/components/recipes/RecipeCard.tsx`
- `packages/ui/components/recipes/RecipeGrid.tsx`
- Storybook stories and component tests
#### Phase 3: Feature Implementation
**Frontend Developer** → Use `/prompts/roles/frontend-developer.prompt.md`
- **Input**: Issue #34 - Recipe Grid View User Story
- **Dependencies**: Database (#30), API (#31), Components (#32)
- **Output**: Complete recipe library page
- **MCP Tools**: Playwright MCP for comprehensive E2E testing
- **Deliverables**:
- `apps/web/src/app/recipes/page.tsx`
- Complete responsive recipe grid
- Loading states, error handling, empty states
#### Phase 4: Intelligent Automation
**Automation Engineer** → Use `/prompts/roles/automation-engineer.prompt.md`
- **Input**: Issue #33 - n8n Recipe Import Workflow
- **Output**: Intelligent recipe import system
- **MCP Tools**: GitHub MCP for webhook setup, Memory MCP for pattern storage
- **Deliverables**:
- n8n workflow for URL scraping
- AI-powered recipe extraction
- Vector embeddings for search
**AI Context Engineer** → Use `/prompts/roles/ai-context-engineer.prompt.md`
- **Input**: AI requirements from automation workflows
- **Output**: Sophisticated prompt systems and context management
- **MCP Tools**: Memory MCP for context storage, Sequential Thinking MCP for complex reasoning
- **Deliverables**:
- Recipe extraction prompts
- Context management systems
- Personalization engines
## Role Integration Patterns
### Cross-Role Dependencies
```mermaid
graph TD
A[Database Engineer] --> B[Backend Developer]
A --> C[Frontend Developer]
B --> C
B --> D[Automation Engineer]
C --> E[AI Context Engineer]
D --> E
F[Planning Artifacts] --> A
F --> B
F --> C
F --> D
F --> E
```
### Handoff Protocols
#### Database → Backend
- **Database Engineer** provides:
- Complete schema definitions
- Migration scripts
- Performance benchmarks
- Query optimization recommendations
- **Backend Developer** receives:
- Type-safe database access patterns
- Optimized query examples
- Performance constraints
- Data access patterns
#### Backend → Frontend
- **Backend Developer** provides:
- Complete tRPC type definitions
- API documentation with examples
- Error handling patterns
- Authentication requirements
- **Frontend Developer** receives:
- End-to-end type safety
- Clear API contracts
- Error handling guidance
- Performance expectations
#### Frontend → Automation
- **Frontend Developer** provides:
- User interaction patterns
- Data requirements
- Performance constraints
- Integration points
- **Automation Engineer** receives:
- User workflow understanding
- Integration requirements
- Performance targets
- Data transformation needs
## MCP Tool Integration Strategy
### By Role
#### Database Engineer
- **Primary**: Database MCP for query execution and schema validation
- **Secondary**: GitHub MCP for migration deployment coordination
- **Usage Pattern**: Always validate queries and performance with Database MCP
#### Backend Developer
- **Primary**: Database MCP for testing data operations
- **Secondary**: GitHub MCP for PR management and issue linking
- **Usage Pattern**: Test all database operations and API endpoints
#### Frontend Developer
- **Primary**: Playwright MCP for comprehensive UI testing
- **Secondary**: GitHub MCP for PR creation with screenshots
- **Usage Pattern**: Always validate accessibility and user interactions
#### Automation Engineer
- **Primary**: GitHub MCP for webhook integration
- **Secondary**: Memory MCP for storing workflow patterns
- **Usage Pattern**: Validate all webhook endpoints and data flows
#### AI Context Engineer
- **Primary**: Memory MCP for context storage and retrieval
- **Secondary**: Sequential Thinking MCP for complex reasoning
- **Usage Pattern**: Store and retrieve context patterns for optimization
## Quality Standards
### Each Role Must Deliver
#### Database Engineer
- [ ] Schema passes all constraint tests
- [ ] Migrations are reversible and tested
- [ ] Query performance meets <100ms targets
- [ ] Database MCP validation completed
- [ ] Household data isolation verified
#### Backend Developer
- [ ] All endpoints are type-safe and validated
- [ ] Authentication and authorization implemented
- [ ] Error handling covers all edge cases
- [ ] Performance targets met (<500ms API responses)
- [ ] Database MCP query testing completed
#### Frontend Developer
- [ ] Components pass accessibility testing
- [ ] Responsive design works on all devices
- [ ] Playwright MCP validation completed
- [ ] Performance targets met (90+ Lighthouse score)
- [ ] User interactions are smooth and intuitive
#### Automation Engineer
- [ ] Workflows handle errors gracefully
- [ ] Integration points are thoroughly tested
- [ ] Performance optimization implemented
- [ ] Monitoring and observability included
- [ ] GitHub MCP webhook validation completed
#### AI Context Engineer
- [ ] Prompts are robust and reliable
- [ ] Context management is efficient
- [ ] Privacy requirements are met
- [ ] Error handling and fallbacks implemented
- [ ] Memory MCP context validation completed
## Implementation Commands
### Starting Implementation
For each role, use this pattern:
```bash
# Example: Backend Developer implementing tRPC API
copilot prompt --file=".github/prompts/roles/backend-developer.prompt.md" \
--context="Issue #31: tRPC Recipe API Router" \
--context="Database schema from #30" \
--context="Feature requirements from #29"
```
### MCP Validation Commands
Each role should validate their work:
```bash
# Database Engineer
dbcode-execute-query --query="SELECT * FROM recipes LIMIT 1"
# Frontend Developer
mcp_playwright_browser_snapshot
mcp_playwright_browser_take_screenshot
# Automation Engineer
mcp_github_create_pull_request --title="Recipe Import Workflow"
```
## Success Metrics
### Overall Feature Success
- [ ] All user acceptance criteria met
- [ ] Performance benchmarks achieved
- [ ] Security requirements validated
- [ ] Accessibility standards met
- [ ] Cross-browser compatibility confirmed
### Role-Specific Success
- [ ] Database: Query performance and data integrity
- [ ] Backend: API performance and type safety
- [ ] Frontend: User experience and accessibility
- [ ] Automation: Workflow reliability and intelligence
- [ ] AI Context: Prompt effectiveness and context retention
This implementation guide ensures that each role delivers high-quality work that integrates seamlessly with other roles, leveraging the appropriate MCP tools for validation and testing throughout the development process.
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