The challenge
Developers using multiple AI coding assistants manually maintain duplicate instruction files. Updating conventions means editing 3+ separate files, leading to inconsistencies and stale guidelines that slow down development workflows.
Where I focused
Architected context-aware CLI with auto-discovery, fuzzy matching, and Model Context Protocol integration. Shipped zero-config experience with optional MCP server for AI self-update capabilities.
How we approached it
Auto-discovery engine
Built intelligent file scanner that locates instruction files across GitHub Copilot, Claude Projects, Cursor IDE, Cline extension, and custom agent configs—no manual configuration required.
Context-aware updates
Designed project analysis system that detects languages and frameworks, then applies instructions with appropriate context to each AI tool's format requirements.
Validation & consistency
Implemented fuzzy matching algorithm to prevent duplicate instructions and validation commands that ensure all instruction files stay synchronized.
Model Context Protocol integration
Created optional MCP server that allows AI assistants to update their own instruction files through structured tool calls, enabling self-improving workflows.
What shipped
- Reduced instruction maintenance from N×M files to single brief update command.
- Shipped with minimal dependencies (Click, PyYAML, Rich) for fast installs and broad compatibility.
- Delivered beautiful terminal UI with color-coded diffs, tables, and preview mode for safe updates.
- Published to PyPI with separate core and MCP extras for flexible installation options.
What I learned
Developer tools succeed when they reduce friction without adding complexity. Brief's zero-config approach and context awareness make multi-tool AI workflows maintainable, while the optional MCP integration shows how AI can participate in improving its own instructions.
