leoyeai/code-exemplars-blueprint-generator

Code Exemplars Blueprint Generator

Configuration Variables

${PROJECT_TYPE="Auto-detect|.NET|Java|JavaScript|TypeScript|React|Angular|Python|Other"} <!-- Primary technology --> ${SCAN_DEPTH="Basic|Standard|Comprehensive"} <!-- How deeply to analyze the codebase --> ${INCLUDE_CODE_SNIPPETS=true|false} <!-- Include actual code snippets in addition to file references --> ${CATEGORIZATION="Pattern Type|Architecture Layer|File Type"} <!-- How to organize exemplars --> ${MAX_EXAMPLES_PER_CATEGORY=3} <!-- Maximum number of examples per category --> ${INCLUDE_COMMENTS=true|false} <!-- Include explanatory comments for each exemplar -->

Generated Prompt

"Scan this codebase and generate an exemplars.md file that identifies high-quality, representative code examples. The exemplars should demonstrate our coding standards and patterns to help maintain consistency. Use the following approach:

1. Codebase Analysis Phase

• ${PROJECT_TYPE == "Auto-detect" ? "Automatically detect primary programming languages and frameworks by scanning file extensions and configuration files" : Focus on ${PROJECT_TYPE} code files}
• Identify files with high-quality implementation, good documentation, and clear structure
• Look for commonly used patterns, architecture components, and well-structured implementations
• Prioritize files that demonstrate best practices for our technology stack
• Only reference actual files that exist in the codebase - no hypothetical examples

2. Exemplar Identification Criteria

• Well-structured, readable code with clear naming conventions
• Comprehensive comments and documentation
• Proper error handling and validation
• Adherence to design patterns and architectural principles
• Separation of concerns and single responsibility principle
• Efficient implementation without code smells
• Representative of our standard approaches

3. Core Pattern Categories

${PROJECT_TYPE == ".NET" || PROJECT_TYPE == "Auto-detect" ? `#### .NET Exemplars (if detected)

• Domain Models: Find entities that properly implement encapsulation and domain logic
• Repository Implementations: Examples of our data access approach
• Service Layer Components: Well-structured business logic implementations
• Controller Patterns: Clean API controllers with proper validation and responses
• Dependency Injection Usage: Good examples of DI configuration and usage
• Middleware Components: Custom middleware implementations
• Unit Test Patterns: Well-structured tests with proper arrangement and assertions` : ""}

${(PROJECT_TYPE == "JavaScript" || PROJECT_TYPE == "TypeScript" || PROJECT_TYPE == "React" || PROJECT_TYPE == "Angular" || PROJECT_TYPE == "Auto-detect") ? `#### Frontend Exemplars (if detected)

• Component Structure: Clean, well-structured components
• State Management: Good examples of state handling
• API Integration: Well-implemented service calls and data handling
• Form Handling: Validation and submission patterns
• Routing Implementation: Navigation and route configuration
• UI Components: Reusable, well-structured UI elements
• Unit Test Examples: Component and service tests` : ""}

${PROJECT_TYPE == "Java" || PROJECT_TYPE == "Auto-detect" ? `#### Java Exemplars (if detected)

• Entity Classes: Well-designed JPA entities or domain models
• Service Implementations: Clean service layer components
• Repository Patterns: Data access implementations
• Controller/Resource Classes: API endpoint implementations
• Configuration Classes: Application configuration
• Unit Tests: Well-structured JUnit tests` : ""}

${PROJECT_TYPE == "Python" || PROJECT_TYPE == "Auto-detect" ? `#### Python Exemplars (if detected)

• Class Definitions: Well-structured classes with proper documentation
• API Routes/Views: Clean API implementations
• Data Models: ORM model definitions
• Service Functions: Business logic implementations
• Utility Modules: Helper and utility functions
• Test Cases: Well-structured unit tests` : ""}

4. Architecture Layer Exemplars

• Presentation Layer:

  • User interface components
  • Controllers/API endpoints
  • View models/DTOs

• Business Logic Layer:

  • Service implementations
  • Business logic components
  • Workflow orchestration

• Data Access Layer:

  • Repository implementations
  • Data models
  • Query patterns

• Cross-Cutting Concerns:

  • Logging implementations
  • Error handling
  • Authentication/authorization
  • Validation

5. Exemplar Documentation Format

For each identified exemplar, document:

• File path (relative to repository root)
• Brief description of what makes it exemplary
• Pattern or component type it represents ${INCLUDE_COMMENTS ? "- Key implementation details and coding principles demonstrated" : ""} ${INCLUDE_CODE_SNIPPETS ? "- Small, representative code snippet (if applicable)" : ""}

${SCAN_DEPTH == "Comprehensive" ? `### 6. Additional Documentation

• Consistency Patterns: Note consistent patterns observed across the codebase
• Architecture Observations: Document architectural patterns evident in the code
• Implementation Conventions: Identify naming and structural conventions
• Anti-patterns to Avoid: Note any areas where the codebase deviates from best practices` : ""}

${SCAN_DEPTH == "Comprehensive" ? "7" : "6"}. Output Format

Create exemplars.md with:

1. Introduction explaining the purpose of the document
2. Table of contents with links to categories
3. Organized sections based on ${CATEGORIZATION}
4. Up to ${MAX_EXAMPLES_PER_CATEGORY} exemplars per category
5. Conclusion with recommendations for maintaining code quality

The document should be actionable for developers needing guidance on implementing new features consistent with existing patterns.

Important: Only include actual files from the codebase. Verify all file paths exist. Do not include placeholder or hypothetical examples. "

Expected Output

Upon running this prompt, GitHub Copilot will scan your codebase and generate an exemplars.md file containing real references to high-quality code examples in your repository, organized according to your selected parameters.