agentleFS
wrm3wrm3/ai_project_template/.cursor/rules/always.mdc

ai_project_template / rules

Cursor rule1 starsChanged 12 months agoMITView on GitHub

The file

We split this always.mdc into the rules it gives an agent

3 rules. About 0 are common, 3 are distinctive, and 0 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈3

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 0

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    if any particular file in the code base exceeds 800 lines of code...

    line 10
  2. Distinctive:

    check your MCP tool lists, you seem to forget you have a lot of tools

    line 15
  3. Distinctive:

    When working with Python Project, please use the UV for virtual environment management

    line 18

≈ “Common” is an estimate: at least 1 in 5 of the 6,668 Cursor rule files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
always.mdc
---
description: 
globs: 
alwaysApply: true
---
1. include in the final line(s) on every response the the user 
  * the current timestamp 
  * list the tools used during the call 

2. if any particular file in the code base exceeds 800 lines of code...
  * begin asking the user if they would like to refactor the code to keep the file sizes smaller
  * become more insistant with every 100 lines added thereafter
  * become very insistant on refactoring once a file has hit 1000 lines

3. check your MCP tool lists, you seem to forget you have a lot of tools


4. When working with Python Project, please use the UV for virtual environment management

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.