Cursor Watchful Headers: Keep Your Project Structure Clean & Self-Documenting

Hey Cursor community! I just released a lightweight tool that I think could be really useful for anyone working with Cursor Agent. It automatically manages file headers and maintains clean project structure visualization - perfect for keeping context clear during long coding sessions.

What it does:

  • Automatically adds and updates headers in your files with the relative path (helps the LLM with situational awareness)
  • Maintains a live project tree structure in .cursorrules
  • Reminds the LLM in every generation to maintain the .watchlist and .donotwatchlist
  • Allows you to filters out noise in the tree (node_modules, build directories, etc.)

Why it matters for Cursor users:

  • Keeps file context clear during long conversations with AI
  • Prevents duplicate file creation by maintaining visible structure
  • Makes it easier for AI to understand your project layout
  • Keeps the project structure in every prompt to the LLM

Quick Start:

git clone https://github.com/johnbenac/cursor-watchful-headers.git
python cursor-watchful-headers/install.py

Check out the full details and documentation at: GitHub - johnbenac/cursor-watchful-headers: Automated file header management and project structure visualization in cursorrules. Maintains consistent headers across multiple file types while providing real-time project tree updates. Perfect for maintaining documentation context in large codebases.

Would love to hear your thoughts and feedback!

  1. cursorrules is being deprecated
  2. header #commenting is valid only for some file extensions
  3. ignored files should go into gitignore(remember to clear cache) and cursorignore
  4. tree format will start breaking when it gets long enough, xml is adviced
  5. git indexing and cursor already tracks latest file modifications/relevant files

I appreciate your effort and sharing but convey with me there are better solutions to this problem, included but not limited to a ‘project overview’ with trees and descriptions(prompt under “Improving: Add context from the project”):

And/or a diagram overview that helps you and AI to understand the codebase and its relationships.