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.
${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 -->
"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:
Focus on ${PROJECT_TYPE} code files}${PROJECT_TYPE == ".NET" || PROJECT_TYPE == "Auto-detect" ? `#### .NET Exemplars (if detected)
${(PROJECT_TYPE == "JavaScript" || PROJECT_TYPE == "TypeScript" || PROJECT_TYPE == "React" || PROJECT_TYPE == "Angular" || PROJECT_TYPE == "Auto-detect") ? `#### Frontend Exemplars (if detected)
${PROJECT_TYPE == "Java" || PROJECT_TYPE == "Auto-detect" ? `#### Java Exemplars (if detected)
${PROJECT_TYPE == "Python" || PROJECT_TYPE == "Auto-detect" ? `#### Python Exemplars (if detected)
Presentation Layer:
Business Logic Layer:
Data Access Layer:
Cross-Cutting Concerns:
For each identified exemplar, document:
${SCAN_DEPTH == "Comprehensive" ? `### 6. Additional Documentation
Create exemplars.md with:
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. "
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.