Suggest relevant GitHub Copilot collections from the awesome-copilot repository...

# Suggest Awesome GitHub Copilot Collections

Analyze current repository context and suggest relevant collections from the [GitHub awesome-copilot repository](https://github.com/github/awesome-copilot/blob/main/docs/README.collections.md) 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](https://github.com/github/awesome-copilot/blob/main/docs/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)