PowerShell Exec MCP Server

# 🤖 GitHub Issue Templates for Copilot Optimization - Complete Setup ## 📁 Created Files and Structure Your repository now includes a comprehensive set of GitHub issue templates optimized for AI coding agents like GitHub Copilot: ``` .github/ ├── ISSUE_TEMPLATE/ │ ├── README.md # Comprehensive guide for using templates │ ├── config.yml # Issue template configuration │ ├── bug_report.yml # 🐛 Copilot-optimized bug reports │ ├── feature_request.yml # ✨ Technical feature specifications │ ├── copilot_task.yml # 🤖 Specific coding task requests │ └── documentation.yml # 📋 Documentation improvement requests ├── workflows/ │ └── copilot-validation.yml # 🔄 Automated validation workflow └── SAMPLE_COPILOT_ISSUE.md # 📖 Example of best practices ``` ## 🎯 Key Features ### 1. **Copilot-Optimized Bug Reports** (`bug_report.yml`) - **Environment Details**: Complete system information for context - **Reproduction Steps**: Clear, step-by-step instructions - **Code Context**: Points to relevant files and functions - **Error Analysis**: Structured logging and error information - **Testing Hints**: Guidance for reproducing and testing fixes **Example sections:** ```yaml **Code Areas to Focus:** - [ ] PowerShell execution (`execute_powershell` function) - [ ] MCP tool definitions - [ ] Resource handlers - [ ] Template processing - [ ] Error handling ``` ### 2. **Technical Feature Requests** (`feature_request.yml`) - **API Specifications**: Complete function signatures and interfaces - **Implementation Guidance**: Code patterns and examples to follow - **Integration Points**: How features connect to existing systems - **Acceptance Criteria**: Clear definition of done - **Quality Checklists**: Comprehensive validation requirements **Example specifications:** ```python @mcp.tool() async def new_feature_tool( param1: str, param2: Optional[int] = None, ctx: Optional[Context] = None ) -> str: """Complete docstring with Args and Returns""" ``` ### 3. **Focused Copilot Tasks** (`copilot_task.yml`) - **Task Categorization**: Bug fix, feature, refactoring, etc. - **Step-by-Step Implementation**: Phased development approach - **Code Context**: Existing patterns and functions to reference - **Quality Requirements**: Security, performance, testing standards - **Integration Instructions**: How to connect new code to existing systems **Example implementation guide:** ```markdown **Phase 1: Planning** - [ ] Review existing code patterns - [ ] Identify dependencies and imports needed - [ ] Plan function structure and flow **Phase 2: Core Implementation** - [ ] Implement main functionality - [ ] Add input validation - [ ] Add error handling - [ ] Add logging and progress reporting ``` ### 4. **Documentation Requests** (`documentation.yml`) - **Content Specifications**: What documentation needs updating - **Style Guidelines**: Formatting and structure requirements - **Example Templates**: Consistent documentation patterns - **Target Audiences**: End users, contributors, administrators ### 5. **Automated Validation Workflow** (`copilot-validation.yml`) - **Issue Quality Checks**: Validates Copilot-ready issues have required sections - **Code Quality Testing**: Linting, formatting, type checking - **Security Scanning**: Automated security vulnerability detection - **Documentation Validation**: Ensures docs are current and complete - **Copilot Readiness Assessment**: Checks code structure for AI optimization ## 🚀 Benefits for AI-Assisted Development ### For GitHub Copilot: 1. **Rich Context**: Detailed information about codebase structure and patterns 2. **Clear Specifications**: Exact function signatures and requirements 3. **Implementation Patterns**: Examples of existing code to follow 4. **Quality Standards**: Explicit requirements for code quality and testing ### For Development Teams: 1. **Consistent Issue Quality**: Standardized information gathering 2. **Faster Implementation**: Clear specifications reduce back-and-forth 3. **Better Code Quality**: Built-in quality checklists and requirements 4. **Knowledge Transfer**: Documentation of patterns and best practices ### For Project Maintenance: 1. **Automated Validation**: GitHub Actions workflow validates issues and PRs 2. **Security Monitoring**: Automatic security scanning and reporting 3. **Quality Metrics**: Tracking of code quality and documentation completeness 4. **Development Analytics**: Insights into development patterns and efficiency ## 📋 Usage Instructions ### Creating Issues 1. **For Bugs**: Use the 🐛 Bug Report template - Include complete environment details - Provide step-by-step reproduction - Point to relevant code areas - Include logs and error messages 2. **For New Features**: Use the ✨ Feature Request template - Provide technical specifications - Include API design and examples - Define acceptance criteria - Plan implementation phases 3. **For Specific Tasks**: Use the 🤖 Copilot Task Request template - Define clear, focused objectives - Provide implementation guidance - Include quality checklists - Reference existing code patterns 4. **For Documentation**: Use the 📋 Documentation Update template - Specify exact files and sections - Provide style guidelines - Include target audience information ### Working with the Templates 1. **Read the Sample Issue**: Review `SAMPLE_COPILOT_ISSUE.md` for best practices 2. **Follow the Patterns**: Use the established structure and sections 3. **Provide Context**: Always include relevant code locations and patterns 4. **Be Specific**: Give exact specifications rather than vague requirements 5. **Include Examples**: Show expected inputs, outputs, and behavior ## 🔧 Configuration Options ### Issue Template Configuration (`config.yml`) - Disables blank issues to encourage structured reporting - Provides helpful links to documentation and resources - Can be customized to add more contact links or resources ### GitHub Actions Workflow (`copilot-validation.yml`) - Runs on every PR and push to main/develop branches - Validates issue quality for Copilot optimization - Performs comprehensive code quality checks - Can be customized to add more validation steps ### Labels and Automation The templates automatically assign labels: - `copilot-ready` - Issues optimized for AI assistance - `copilot-task` - Specific development tasks - `needs-triage` - Requires review and prioritization - `needs-implementation` - Ready for development ## 📊 Quality Metrics The templates help track: - **Issue Completeness**: Required sections and information - **Code Quality**: Type hints, documentation, error handling - **Security Standards**: Input validation, safe operations - **Testing Coverage**: Test cases and validation requirements - **Documentation Currency**: Up-to-date docs and examples ## 🔄 Continuous Improvement ### Regular Reviews 1. **Template Effectiveness**: Monitor if issues provide enough context 2. **AI Success Rates**: Track how well AI agents handle templated issues 3. **Developer Feedback**: Gather input on template usability 4. **Quality Outcomes**: Measure code quality improvements ### Template Updates 1. **Add New Sections**: As new patterns emerge in the codebase 2. **Refine Examples**: Update code examples to match current patterns 3. **Improve Automation**: Enhance GitHub Actions validation 4. **Update References**: Keep documentation links current ## 🎉 Success Indicators Your issue templates are working well when you see: - ✅ Issues contain comprehensive technical specifications - ✅ AI agents can implement features with minimal clarification - ✅ Code quality remains consistent across contributions - ✅ Security and testing requirements are consistently met - ✅ Documentation stays current and complete - ✅ Development velocity increases with better requirements ## 📚 Additional Resources - **GitHub Issue Templates**: https://docs.github.com/en/communities/using-templates-to-encourage-useful-issues-and-pull-requests - **GitHub Copilot Best Practices**: https://docs.github.com/en/copilot/using-github-copilot - **GitHub Actions**: https://docs.github.com/en/actions - **MCP Documentation**: https://modelcontextprotocol.io/ Your repository is now fully equipped with industry-leading issue templates optimized for AI-assisted development! 🚀