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.