Prompt file imported from marcuscastelo/macroflows (.github/prompts/suggest-awesome-github-copilot-collections.prompt.md). Copyright stays with the author.

Suggest Awesome GitHub Copilot Collections

Analyze current repository context and suggest relevant collections from the GitHub awesome-copilot repository that would enhance the development workflow for this repository.

Process

1. Fetch Available Collections: Extract collection list and descriptions from awesome-copilot README.collections.md. Must use #fetch tool.
2. Scan Local Assets: Discover existing prompt files in prompts/, instruction files in instructions/, and chat modes in agents/ folders
3. Extract Local Descriptions: Read front matter from local asset files to understand existing capabilities
4. Analyze Repository Context: Review chat history, repository files, programming languages, frameworks, and current project needs
5. Match Collection Relevance: Compare available collections against identified patterns and requirements
6. Check Asset Overlap: For relevant collections, analyze individual items to avoid duplicates with existing repository assets
7. Present Collection Options: Display relevant collections with descriptions, item counts, and rationale for suggestion
8. Provide Usage Guidance: Explain how the installed collection enhances the development workflow AWAIT user request to proceed with installation of specific collections. DO NOT INSTALL UNLESS DIRECTED TO DO SO.
9. Download Assets: For requested collections, automatically download and install each individual asset (prompts, instructions, chat modes) to appropriate directories. Do NOT adjust content of the files. Prioritize use of #fetch tool to download assets, but may use curl using #runInTerminal tool to ensure all content is retrieved.

Context Analysis Criteria

🔍 Repository Patterns:

• Programming languages used (.cs, .js, .py, .ts, .bicep, .tf, etc.)
• Framework indicators (ASP.NET, React, Azure, Next.js, Angular, etc.)
• Project types (web apps, APIs, libraries, tools, infrastructure)
• Documentation needs (README, specs, ADRs, architectural decisions)
• Development workflow indicators (CI/CD, testing, deployment)

🗨️ Chat History Context:

• Recent discussions and pain points
• Feature requests or implementation needs
• Code review patterns and quality concerns
• Development workflow requirements and challenges
• Technology stack and architecture decisions

Output Format

Display analysis results in structured table showing relevant collections and their potential value:

Collection Recommendations

Asset Analysis for Recommended Collections

For each suggested collection, break down individual assets:

Azure & Cloud Development Collection Analysis:

• ✅ New Assets (12): Azure cost optimization prompts, Bicep planning mode, AVM modules, Logic Apps expert mode
• ⚠️ Similar Assets (3): Azure DevOps pipelines (similar to existing CI/CD), Terraform (basic overlap), Containerization (Docker basics covered)
• 🎯 High Value: Cost optimization tools, Infrastructure as Code expertise, Azure-specific architectural guidance

Installation Preview:

• Will install to prompts/: 4 Azure-specific prompts
• Will install to instructions/: 6 infrastructure and DevOps best practices
• Will install to agents/: 5 specialized Azure expert modes

Local Asset Discovery Process

1. Scan Asset Directories:

   • List all *.prompt.md files in prompts/ directory
   • List all *.instructions.md files in instructions/ directory
   • List all *.agent.md files in agents/ directory

2. Extract Asset Metadata: For each discovered file, read YAML front matter to extract:

   • description - Primary purpose and functionality
   • tools - Required tools and capabilities
   • mode - Operating mode (for prompts)
   • model - Specific model requirements (for chat modes)

3. Build Asset Inventory: Create comprehensive map of existing capabilities organized by:

   • Technology Focus: Programming languages, frameworks, platforms
   • Workflow Type: Development, testing, deployment, documentation, planning
   • Specialization Level: General purpose vs. specialized expert modes

4. Identify Coverage Gaps: Compare existing assets against:

   • Repository technology stack requirements
   • Development workflow needs indicated by chat history
   • Industry best practices for identified project types
   • Missing expertise areas (security, performance, architecture, etc.)

Collection Asset Download Process

When user confirms a collection installation:

1. Fetch Collection Manifest: Get collection YAML from awesome-copilot repository
2. Download Individual Assets: For each item in collection:
   • Download raw file content from GitHub
   • Validate file format and front matter structure
   • Check naming convention compliance
3. Install to Appropriate Directories:
   • *.prompt.md files → prompts/ directory
   • *.instructions.md files → instructions/ directory
   • *.agent.md files → agents/ directory
4. Avoid Duplicates: Skip files that are substantially similar to existing assets
5. Report Installation: Provide summary of installed assets and usage instructions

Requirements

• Use fetch tool to get collections data from awesome-copilot repository
• Use githubRepo tool to get individual asset content for download
• Scan local file system for existing assets in prompts/, instructions/, and agents/ directories
• Read YAML front matter from local asset files to extract descriptions and capabilities
• Compare collections against repository context to identify relevant matches
• Focus on collections that fill capability gaps rather than duplicate existing assets
• Validate that suggested collections align with repository's technology stack and development needs
• Provide clear rationale for each collection suggestion with specific benefits
• Enable automatic download and installation of collection assets to appropriate directories
• Ensure downloaded assets follow repository naming conventions and formatting standards
• Provide usage guidance explaining how collections enhance the development workflow
• Include links to both awesome-copilot collections and individual assets within collections

Collection Installation Workflow

1. User Confirms Collection: User selects specific collection(s) for installation
2. Fetch Collection Manifest: Download YAML manifest from awesome-copilot repository
3. Asset Download Loop: For each asset in collection:
   • Download raw content from GitHub repository
   • Validate file format and structure
   • Check for substantial overlap with existing local assets
   • Install to appropriate directory (prompts/, instructions/, or agents/)
4. Installation Summary: Report installed assets with usage instructions
5. Workflow Enhancement Guide: Explain how the collection improves development capabilities

Post-Installation Guidance

After installing a collection, provide:

• Asset Overview: List of installed prompts, instructions, and chat modes
• Usage Examples: How to activate and use each type of asset
• Workflow Integration: Best practices for incorporating assets into development process
• Customization Tips: How to modify assets for specific project needs
• Related Collections: Suggestions for complementary collections that work well together

Icons Reference

• ✅ Collection recommended for installation
• ⚠️ Collection has some asset overlap but still valuable
• ❌ Collection not recommended (significant overlap or not relevant)
• 🎯 High-value collection that fills major capability gaps
• 📁 Collection partially installed (some assets skipped due to duplicates)
• 🔄 Collection needs customization for repository-specific needs