code-exemplars-blueprint-generator

name: code-exemplars-blueprint-generator description: 'Technology-agnostic prompt generator that creates customizable AI prompts for scanning codebases and identifying high-quality code exemplars. Supports multiple programming languages (.NET, Java, JavaScript, TypeScript, React, Angular, Python) with configurable analysis depth, categorization methods, and documentation formats to establish coding standards and maintain consistency across development teams.'

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.