agentleFS
synaptiaisynaptiai/prompt-decorators/.cursor/rules/070-workflow-preferences.mdc

prompt-decorators / rules

Cursor rule44 starsChanged 20 months agoApache-2.0View on GitHub

MAINTAIN development discipline WHEN implementing changes TO preserve codebase integrity

The file

We split this 070-workflow-preferences.mdc into the rules it gives an agent

8 rules. About 0 are common, 8 are distinctive, and 0 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈8

    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:

    When implementing new features

    line 11
  2. Distinctive:

    When fixing bugs or addressing issues

    line 12
  3. Distinctive:

    When refactoring or improving existing code

    line 13
  4. Distinctive:

    When reviewing code changes

    line 14
  5. Distinctive:

    Do not modify code unrelated to the assigned task

    line 18
  6. Distinctive:

    Write thorough tests for all major functionality

    line 19
  7. Distinctive:

    Consider side effects before implementing changes

    line 22
  8. Distinctive:

    Document significant architectural decisions

    line 24

≈ “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
070-workflow-preferences.mdc
---
description: MAINTAIN development discipline WHEN implementing changes TO preserve codebase integrity
globs: **/*.py
---

# Coding Workflow Preferences

<version>1.0.0</version>

## Context
- When implementing new features
- When fixing bugs or addressing issues
- When refactoring or improving existing code
- When reviewing code changes

## Requirements
- Focus exclusively on code areas relevant to the current task
- Do not modify code unrelated to the assigned task
- Write thorough tests for all major functionality
- Preserve existing architectural patterns unless explicitly instructed otherwise
- Analyze potential impacts of changes on other methods and code areas
- Consider side effects before implementing changes
- Maintain backward compatibility when possible
- Document significant architectural decisions

## Examples

<example>
# Good: Focused change with proper testing
# Task: Fix validation in user registration

# 1. Only modify the relevant validation function
def validate_user_input(user_data):
    """Validate user registration data with fixed email validation."""
    if not is_valid_email(user_data.get('email')):
        raise ValidationError("Invalid email format")
    # Other existing validation logic...

# 2. Add thorough tests for the fix
def test_validate_user_input_with_invalid_email():
    """Test that invalid emails are properly rejected."""
    with pytest.raises(ValidationError):
        validate_user_input({'email': 'invalid-email'})

def test_validate_user_input_with_valid_email():
    """Test that valid emails pass validation."""
    result = validate_user_input({'email': 'user@example.com'})
    assert result is True
</example>

<example type="invalid">
# Bad: Unfocused changes beyond task scope
# Task: Fix validation in user registration

# 1. Modifying unrelated authentication system
def authenticate_user(username, password):
    """Authenticate user with new token-based system."""
    # Completely rewriting authentication when task was only about validation
    token = generate_jwt_token(username, password)
    return {'authenticated': True, 'token': token}

# 2. Changing database schema without tests
def update_user_schema():
    """Update user table with new fields."""
    # Adding schema changes unrelated to the validation fix
    db.execute("ALTER TABLE users ADD COLUMN last_login TIMESTAMP")
</example>

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.